Sample records for abstract rule learning

  1. Information from multiple modalities helps 5-month-olds learn abstract rules.

    PubMed

    Frank, Michael C; Slemmer, Jonathan A; Marcus, Gary F; Johnson, Scott P

    2009-07-01

    By 7 months of age, infants are able to learn rules based on the abstract relationships between stimuli (Marcus et al., 1999), but they are better able to do so when exposed to speech than to some other classes of stimuli. In the current experiments we ask whether multimodal stimulus information will aid younger infants in identifying abstract rules. We habituated 5-month-olds to simple abstract patterns (ABA or ABB) instantiated in coordinated looming visual shapes and speech sounds (Experiment 1), shapes alone (Experiment 2), and speech sounds accompanied by uninformative but coordinated shapes (Experiment 3). Infants showed evidence of rule learning only in the presence of the informative multimodal cues. We hypothesize that the additional evidence present in these multimodal displays was responsible for the success of younger infants in learning rules, congruent with both a Bayesian account and with the Intersensory Redundancy Hypothesis.

  2. Individual differences in learning and transfer: stable tendencies for learning exemplars versus abstracting rules.

    PubMed

    McDaniel, Mark A; Cahill, Michael J; Robbins, Mathew; Wiener, Chelsea

    2014-04-01

    We hypothesize that during training some learners may focus on acquiring the particular exemplars and responses associated with the exemplars (termed exemplar learners), whereas other learners attempt to abstract underlying regularities reflected in the particular exemplars linked to an appropriate response (termed rule learners). Supporting this distinction, after training (on a function-learning task), participants displayed an extrapolation profile reflecting either acquisition of the trained cue-criterion associations (exemplar learners) or abstraction of the function rule (rule learners; Studies 1a and 1b). Further, working memory capacity (measured by operation span [Ospan]) was associated with the tendency to rely on rule versus exemplar processes. Studies 1c and 2 examined the persistence of these learning tendencies on several categorization tasks. Study 1c showed that rule learners were more likely than exemplar learners (indexed a priori by extrapolation profiles) to resist using idiosyncratic features (exemplar similarity) in generalization (transfer) of the trained category. Study 2 showed that the rule learners but not the exemplar learners performed well on a novel categorization task (transfer) after training on an abstract coherent category. These patterns suggest that in complex conceptual tasks, (a) individuals tend to either focus on exemplars during learning or on extracting some abstraction of the concept, (b) this tendency might be a relatively stable characteristic of the individual, and (c) transfer patterns are determined by that tendency.

  3. Individual Differences in Learning and Transfer: Stable Tendencies for Learning Exemplars versus Abstracting Rules

    PubMed Central

    McDaniel, Mark A.; Cahill, Michael J.; Robbins, Mathew; Wiener, Chelsea

    2013-01-01

    We hypothesize that during training some learners may focus on acquiring the particular exemplars and responses associated with the exemplars (termed exemplar learners), whereas other learners attempt to abstract underlying regularities reflected in the particular exemplars linked to an appropriate response (termed rule learners). Supporting this distinction, after training (on a function-learning task), participants either displayed an extrapolation profile reflecting acquisition of the trained cue-criterion associations (exemplar learners) or abstraction of the function rule (rule learners; Studies 1a and 1b). Further, working memory capacity (measured by Ospan) was associated with the tendency to rely on rule versus exemplar processes. Studies 1c and 2 examined the persistence of these learning tendencies on several categorization tasks. Study 1c showed that rule learners were more likely than exemplar learners (indexed a priori by extrapolation profiles) to resist using idiosyncratic features (exemplar similarity) in generalization (transfer) of the trained category. Study 2 showed that the rule learners but not the exemplar learners performed well on a novel categorization task (transfer) after training on an abstract coherent category. These patterns suggest that in complex conceptual tasks, (a) individuals tend to either focus on exemplars during learning or on extracting some abstraction of the concept, (b) this tendency might be a relatively stable characteristic of the individual, and (c) transfer patterns are determined by that tendency. PMID:23750912

  4. Communicative signals support abstract rule learning by 7-month-old infants

    PubMed Central

    Ferguson, Brock; Lew-Williams, Casey

    2016-01-01

    The mechanisms underlying the discovery of abstract rules like those found in natural language may be evolutionarily tuned to speech, according to previous research. When infants hear speech sounds, they can learn rules that govern their combination, but when they hear non-speech sounds such as sine-wave tones, they fail to do so. Here we show that infants’ rule learning is not tied to speech per se, but is instead enhanced more broadly by communicative signals. In two experiments, infants succeeded in learning and generalizing rules from tones that were introduced as if they could be used to communicate. In two control experiments, infants failed to learn the very same rules when familiarized to tones outside of a communicative exchange. These results reveal that infants’ attention to social agents and communication catalyzes a fundamental achievement of human learning. PMID:27150270

  5. Bimodal emotion congruency is critical to preverbal infants' abstract rule learning.

    PubMed

    Tsui, Angeline Sin Mei; Ma, Yuen Ki; Ho, Anna; Chow, Hiu Mei; Tseng, Chia-huei

    2016-05-01

    Extracting general rules from specific examples is important, as we must face the same challenge displayed in various formats. Previous studies have found that bimodal presentation of grammar-like rules (e.g. ABA) enhanced 5-month-olds' capacity to acquire a rule that infants failed to learn when the rule was presented with visual presentation of the shapes alone (circle-triangle-circle) or auditory presentation of the syllables (la-ba-la) alone. However, the mechanisms and constraints for this bimodal learning facilitation are still unknown. In this study, we used audio-visual relation congruency between bimodal stimulation to disentangle possible facilitation sources. We exposed 8- to 10-month-old infants to an AAB sequence consisting of visual faces with affective expressions and/or auditory voices conveying emotions. Our results showed that infants were able to distinguish the learned AAB rule from other novel rules under bimodal stimulation when the affects in audio and visual stimuli were congruently paired (Experiments 1A and 2A). Infants failed to acquire the same rule when audio-visual stimuli were incongruently matched (Experiment 2B) and when only the visual (Experiment 1B) or the audio (Experiment 1C) stimuli were presented. Our results highlight that bimodal facilitation in infant rule learning is not only dependent on better statistical probability and redundant sensory information, but also the relational congruency of audio-visual information. A video abstract of this article can be viewed at https://m.youtube.com/watch?v=KYTyjH1k9RQ. © 2015 John Wiley & Sons Ltd.

  6. Single neurons in prefrontal cortex encode abstract rules.

    PubMed

    Wallis, J D; Anderson, K C; Miller, E K

    2001-06-21

    The ability to abstract principles or rules from direct experience allows behaviour to extend beyond specific circumstances to general situations. For example, we learn the 'rules' for restaurant dining from specific experiences and can then apply them in new restaurants. The use of such rules is thought to depend on the prefrontal cortex (PFC) because its damage often results in difficulty in following rules. Here we explore its neural basis by recording from single neurons in the PFC of monkeys trained to use two abstract rules. They were required to indicate whether two successively presented pictures were the same or different depending on which rule was currently in effect. The monkeys performed this task with new pictures, thus showing that they had learned two general principles that could be applied to stimuli that they had not yet experienced. The most prevalent neuronal activity observed in the PFC reflected the coding of these abstract rules.

  7. Information from Multiple Modalities Helps 5-Month-Olds Learn Abstract Rules

    ERIC Educational Resources Information Center

    Frank, Michael C.; Slemmer, Jonathan A.; Marcus, Gary F.; Johnson, Scott P.

    2009-01-01

    By 7 months of age, infants are able to learn rules based on the abstract relationships between stimuli ( Marcus et al., 1999 ), but they are better able to do so when exposed to speech than to some other classes of stimuli. In the current experiments we ask whether multimodal stimulus information will aid younger infants in identifying abstract…

  8. Electrophysiological responses to feedback during the application of abstract rules.

    PubMed

    Walsh, Matthew M; Anderson, John R

    2013-11-01

    Much research focuses on how people acquire concrete stimulus-response associations from experience; however, few neuroscientific studies have examined how people learn about and select among abstract rules. To address this issue, we recorded ERPs as participants performed an abstract rule-learning task. In each trial, they viewed a sample number and two test numbers. Participants then chose a test number using one of three abstract mathematical rules they freely selected from: greater than the sample number, less than the sample number, or equal to the sample number. No one rule was always rewarded, but some rules were rewarded more frequently than others. To maximize their earnings, participants needed to learn which rules were rewarded most frequently. All participants learned to select the best rules for repeating and novel stimulus sets that obeyed the overall reward probabilities. Participants differed, however, in the extent to which they overgeneralized those rules to repeating stimulus sets that deviated from the overall reward probabilities. The feedback-related negativity (FRN), an ERP component thought to reflect reward prediction error, paralleled behavior. The FRN was sensitive to item-specific reward probabilities in participants who detected the deviant stimulus set, and the FRN was sensitive to overall reward probabilities in participants who did not. These results show that the FRN is sensitive to the utility of abstract rules and that the individual's representation of a task's states and actions shapes behavior as well as the FRN.

  9. Electrophysiological Responses to Feedback during the Application of Abstract Rules

    PubMed Central

    Walsh, Matthew M.; Anderson, John R.

    2017-01-01

    Much research focuses on how people acquire concrete stimulus–response associations from experience; however, few neuroscientific studies have examined how people learn about and select among abstract rules. To address this issue, we recorded ERPs as participants performed an abstract rule-learning task. In each trial, they viewed a sample number and two test numbers. Participants then chose a test number using one of three abstract mathematical rules they freely selected from: greater than the sample number, less than the sample number, or equal to the sample number. No one rule was always rewarded, but some rules were rewarded more frequently than others. To maximize their earnings, participants needed to learn which rules were rewarded most frequently. All participants learned to select the best rules for repeating and novel stimulus sets that obeyed the overall reward probabilities. Participants differed, however, in the extent to which they overgeneralized those rules to repeating stimulus sets that deviated from the overall reward probabilities. The feedback-related negativity (FRN), an ERP component thought to reflect reward prediction error, paralleled behavior. The FRN was sensitive to item-specific reward probabilities in participants who detected the deviant stimulus set, and the FRN was sensitive to overall reward probabilities in participants who did not. These results show that the FRN is sensitive to the utility of abstract rules and that the individualʼs representation of a taskʼs states and actions shapes behavior as well as the FRN. PMID:23915052

  10. Rapid Transfer of Abstract Rules to Novel Contexts in Human Lateral Prefrontal Cortex

    PubMed Central

    Cole, Michael W.; Etzel, Joset A.; Zacks, Jeffrey M.; Schneider, Walter; Braver, Todd S.

    2011-01-01

    Flexible, adaptive behavior is thought to rely on abstract rule representations within lateral prefrontal cortex (LPFC), yet it remains unclear how these representations provide such flexibility. We recently demonstrated that humans can learn complex novel tasks in seconds. Here we hypothesized that this impressive mental flexibility may be possible due to rapid transfer of practiced rule representations within LPFC to novel task contexts. We tested this hypothesis using functional MRI and multivariate pattern analysis, classifying LPFC activity patterns across 64 tasks. Classifiers trained to identify abstract rules based on practiced task activity patterns successfully generalized to novel tasks. This suggests humans can transfer practiced rule representations within LPFC to rapidly learn new tasks, facilitating cognitive performance in novel circumstances. PMID:22125519

  11. Contributions of Lateral and Orbital Frontal Regions to Abstract Rule Acquisition and Reversal in Monkeys

    PubMed Central

    La Camera, Giancarlo; Bouret, Sebastien; Richmond, Barry J.

    2018-01-01

    The ability to learn and follow abstract rules relies on intact prefrontal regions including the lateral prefrontal cortex (LPFC) and the orbitofrontal cortex (OFC). Here, we investigate the specific roles of these brain regions in learning rules that depend critically on the formation of abstract concepts as opposed to simpler input-output associations. To this aim, we tested monkeys with bilateral removals of either LPFC or OFC on a rapidly learned task requiring the formation of the abstract concept of same vs. different. While monkeys with OFC removals were significantly slower than controls at both acquiring and reversing the concept-based rule, monkeys with LPFC removals were not impaired in acquiring the task, but were significantly slower at rule reversal. Neither group was impaired in the acquisition or reversal of a delayed visual cue-outcome association task without a concept-based rule. These results suggest that OFC is essential for the implementation of a concept-based rule, whereas LPFC seems essential for its modification once established. PMID:29615854

  12. Bimodal Emotion Congruency Is Critical to Preverbal Infants' Abstract Rule Learning

    ERIC Educational Resources Information Center

    Tsui, Angeline Sin Mei; Ma, Yuen Ki; Ho, Anna; Chow, Hiu Mei; Tseng, Chia-huei

    2016-01-01

    Extracting general rules from specific examples is important, as we must face the same challenge displayed in various formats. Previous studies have found that bimodal presentation of grammar-like rules (e.g. ABA) enhanced 5-month-olds' capacity to acquire a rule that infants failed to learn when the rule was presented with visual presentation of…

  13. Rule learning in autism: the role of reward type and social context.

    PubMed

    Jones, E J H; Webb, S J; Estes, A; Dawson, G

    2013-01-01

    Learning abstract rules is central to social and cognitive development. Across two experiments, we used Delayed Non-Matching to Sample tasks to characterize the longitudinal development and nature of rule-learning impairments in children with Autism Spectrum Disorder (ASD). Results showed that children with ASD consistently experienced more difficulty learning an abstract rule from a discrete physical reward than children with DD. Rule learning was facilitated by the provision of more concrete reinforcement, suggesting an underlying difficulty in forming conceptual connections. Learning abstract rules about social stimuli remained challenging through late childhood, indicating the importance of testing executive functions in both social and non-social contexts.

  14. Rule Learning in Autism: The Role of Reward Type and Social Context

    PubMed Central

    Jones, E. J. H.; Webb, S. J.; Estes, A.; Dawson, G.

    2013-01-01

    Learning abstract rules is central to social and cognitive development. Across two experiments, we used Delayed Non-Matching to Sample tasks to characterize the longitudinal development and nature of rule-learning impairments in children with Autism Spectrum Disorder (ASD). Results showed that children with ASD consistently experienced more difficulty learning an abstract rule from a discrete physical reward than children with DD. Rule learning was facilitated by the provision of more concrete reinforcement, suggesting an underlying difficulty in forming conceptual connections. Learning abstract rules about social stimuli remained challenging through late childhood, indicating the importance of testing executive functions in both social and non-social contexts. PMID:23311315

  15. Category Learning Strategies in Younger and Older Adults: Rule Abstraction and Memorization

    PubMed Central

    Wahlheim, Christopher N.; McDaniel, Mark A.; Little, Jeri L.

    2016-01-01

    Despite the fundamental role of category learning in cognition, few studies have examined how this ability differs between younger and older adults. The present experiment examined possible age differences in category learning strategies and their effects on learning. Participants were trained on a category determined by a disjunctive rule applied to relational features. The utilization of rule- and exemplar-based strategies was indexed by self-reports and transfer performance. Based on self-reported strategies, both age groups had comparable frequencies of rule- and exemplar-based learners, but older adults had a higher frequency of intermediate learners (i.e., learners not identifying with a reliance on either rule- or exemplar-based strategies). Training performance was higher for younger than older adults regardless of the strategy utilized, showing that older adults were impaired in their ability to learn the correct rule or to remember exemplar-label associations. Transfer performance converged with strategy reports in showing higher fidelity category representations for younger adults. Younger adults with high working memory capacity were more likely to use an exemplar-based strategy, and older adults with high working memory capacity showed better training performance. Age groups did not differ in their self-reported memory beliefs, and these beliefs did not predict training strategies or performance. Overall, the present results contradict earlier findings that older adults prefer rule- to exemplar-based learning strategies, presumably to compensate for memory deficits. PMID:26950225

  16. Category learning strategies in younger and older adults: Rule abstraction and memorization.

    PubMed

    Wahlheim, Christopher N; McDaniel, Mark A; Little, Jeri L

    2016-06-01

    Despite the fundamental role of category learning in cognition, few studies have examined how this ability differs between younger and older adults. The present experiment examined possible age differences in category learning strategies and their effects on learning. Participants were trained on a category determined by a disjunctive rule applied to relational features. The utilization of rule- and exemplar-based strategies was indexed by self-reports and transfer performance. Based on self-reported strategies, the frequencies of rule- and exemplar-based learners were not significantly different between age groups, but there was a significantly higher frequency of intermediate learners (i.e., learners not identifying with a reliance on either rule- or exemplar-based strategies) in the older than younger adult group. Training performance was higher for younger than older adults regardless of the strategy utilized, showing that older adults were impaired in their ability to learn the correct rule or to remember exemplar-label associations. Transfer performance converged with strategy reports in showing higher fidelity category representations for younger adults. Younger adults with high working memory capacity were more likely to use an exemplar-based strategy, and older adults with high working memory capacity showed better training performance. Age groups did not differ in their self-reported memory beliefs, and these beliefs did not predict training strategies or performance. Overall, the present results contradict earlier findings that older adults prefer rule- to exemplar-based learning strategies, presumably to compensate for memory deficits. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  17. Infants learn a rule predicated on the relation same but fail to simultaneously learn a rule predicated on the relation different.

    PubMed

    Hochmann, Jean-Rémy; Carey, Susan; Mehler, Jacques

    2018-08-01

    In two experiments, we assessed whether infants are able to learn rules predicated on two abstract relations linked by negation: same and different (not same). In an anticipatory looking paradigm, the relation between successive colored geometrical shapes predicted the location where a puppet would appear next. In Experiment 1, 7-month-olds learned and generalized a rule predicated on the relation same, but not a rule predicated on the relation different. Similarly, in Experiment 2, 12-month-olds learned a rule predicated on the relation same-shape, but not a rule predicated on the relation different-shape. Comparing our data with that from previous experiments in the speech domain, we found no effect of age, modality or rule complexity. We conclude that, in the first year of life, infants already possess a representation of the abstract relation same, which serves as input to a rule. In contrast, we find no evidence that they represent the relation different. Copyright © 2018 Elsevier B.V. All rights reserved.

  18. Abstract rule learning in 11- and 14-month-old infants.

    PubMed

    Koulaguina, Elena; Shi, Rushen

    2013-02-01

    This study tests the hypothesis that distributional information can guide infants in the generalization of word order movement rules at the initial stage of language acquisition. Participants were 11- and 14-month-old infants. Stimuli were sentences in Russian, a language that was unknown to our infants. During training the word order of each sentence was transformed following a consistent pattern (e.g., ABC-BAC). During the test phase infants heard novel sentences that respected the trained rule and ones that violated the trained rule (i.e., a different transformation such as ABC-ACB). Stimuli words had highly variable phonological and morphological shapes. The cue available was the positional information of words and their non-adjacent relations across sentences. We found that 14-month-olds, but not 11-month-olds, showed evidence of abstract rule generalization to novel instances. The implications of this finding to early syntactic acquisition are discussed.

  19. A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

    PubMed

    Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

    2016-03-01

    Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015

  20. A neural mechanism of cognitive control for resolving conflict between abstract task rules

    PubMed Central

    Sheu, Yi-Shin; Courtney, Susan M.

    2016-01-01

    Conflict between multiple sensory stimuli or potential motor responses is thought to be resolved via bias signals from prefrontal cortex. However, population codes in the prefrontal cortex also represent abstract information, such as task rules. How is conflict between active abstract representations resolved? We used functional neuroimaging to investigate the mechanism responsible for resolving conflict between abstract representations of task rules. Participants performed two different tasks based on a cue. We manipulated the degree of conflict at the task-rule level by training participants to associate the color and shape dimensions of the cue with either the same task rule (congruent cues) or different ones (incongruent cues). Phonological and semantic tasks were used in which performance depended on learned, abstract representations of information, rather than sensory features of the target stimulus or on any habituated stimulus-response associations. In addition, these tasks activate distinct regions that allowed us to measure magnitude of conflict between tasks. We found that incongruent cues were associated with increased activity in several cognitive control areas, including the inferior frontal gyrus, inferior parietal lobule, insula, and subcortical regions. Conflict between abstract representations appears to be resolved by rule-specific activity in the inferior frontal gyrus that is correlated with enhanced activity related to the relevant information. Furthermore, multivoxel pattern analysis of the activity in the inferior frontal gyrus was shown to carry information about both the currently relevant rule (semantic/phonological) and the currently relevant cue context (color/shape). Similar to models of attentional selection of conflicting sensory or motor representations, the current findings indicate part of the frontal cortex provides a bias signal, representing task rules, that enhances task-relevant information. However, the frontal cortex can

  1. A neural mechanism of cognitive control for resolving conflict between abstract task rules.

    PubMed

    Sheu, Yi-Shin; Courtney, Susan M

    2016-12-01

    Conflict between multiple sensory stimuli or potential motor responses is thought to be resolved via bias signals from prefrontal cortex (PFC). However, population codes in the PFC also represent abstract information, such as task rules. How is conflict between active abstract representations resolved? We used functional neuroimaging to investigate the mechanism responsible for resolving conflict between abstract representations of task rules. Participants performed two different tasks based on a cue. We manipulated the degree of conflict at the task-rule level by training participants to associate the color and shape dimensions of the cue with either the same task rule (congruent cues) or different ones (incongruent cues). Phonological and semantic tasks were used in which performance depended on learned, abstract representations of information, rather than sensory features of the target stimulus or on any habituated stimulus-response associations. In addition, these tasks activate distinct regions that allowed us to measure magnitude of conflict between tasks. We found that incongruent cues were associated with increased activity in several cognitive control areas, including the inferior frontal gyrus, inferior parietal lobule, insula, and subcortical regions. Conflict between abstract representations appears to be resolved by rule-specific activity in the inferior frontal gyrus that is correlated with enhanced activity related to the relevant information. Furthermore, multi-voxel pattern analysis of the activity in the inferior frontal gyrus was shown to carry information about both the currently relevant rule (semantic/phonological) and the currently relevant cue context (color/shape). Similar to models of attentional selection of conflicting sensory or motor representations, the current findings indicate part of the frontal cortex provides a bias signal, representing task rules, that enhances task-relevant information. However, the frontal cortex can also be

  2. Sleep facilitates learning a new linguistic rule.

    PubMed

    Batterink, Laura J; Oudiette, Delphine; Reber, Paul J; Paller, Ken A

    2014-12-01

    Natural languages contain countless regularities. Extraction of these patterns is an essential component of language acquisition. Here we examined the hypothesis that memory processing during sleep contributes to this learning. We exposed participants to a hidden linguistic rule by presenting a large number of two-word phrases, each including a noun preceded by one of four novel words that functioned as an article (e.g., gi rhino). These novel words (ul, gi, ro and ne) were presented as obeying an explicit rule: two words signified that the noun referent was relatively near, and two that it was relatively far. Undisclosed to participants was the fact that the novel articles also predicted noun animacy, with two of the articles preceding animate referents and the other two preceding inanimate referents. Rule acquisition was tested implicitly using a task in which participants responded to each phrase according to whether the noun was animate or inanimate. Learning of the hidden rule was evident in slower responses to phrases that violated the rule. Responses were delayed regardless of whether rule-knowledge was consciously accessible. Brain potentials provided additional confirmation of implicit and explicit rule-knowledge. An afternoon nap was interposed between two 20-min learning sessions. Participants who obtained greater amounts of both slow-wave and rapid-eye-movement sleep showed increased sensitivity to the hidden linguistic rule in the second session. We conclude that during sleep, reactivation of linguistic information linked with the rule was instrumental for stabilizing learning. The combination of slow-wave and rapid-eye-movement sleep may synergistically facilitate the abstraction of complex patterns in linguistic input. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. Sleep facilitates learning a new linguistic rule

    PubMed Central

    Batterink, Laura J.; Oudiette, Delphine; Reber, Paul J.; Paller, Ken A.

    2014-01-01

    Natural languages contain countless regularities. Extraction of these patterns is an essential component of language acquisition. Here we examined the hypothesis that memory processing during sleep contributes to this learning. We exposed participants to a hidden linguistic rule by presenting a large number of two-word phrases, each including a noun preceded by one of four novel words that functioned as an article (e.g., gi rhino). These novel words (ul, gi, ro and ne) were presented as obeying an explicit rule: two words signified that the noun referent was relatively near, and two that it was relatively far. Undisclosed to participants was the fact that the novel articles also predicted noun animacy, with two of the articles preceding animate referents and the other two preceding inanimate referents. Rule acquisition was tested implicitly using a task in which participants responded to each phrase according to whether the noun was animate or inanimate. Learning of the hidden rule was evident in slower responses to phrases that violated the rule. Responses were delayed regardless of whether rule-knowledge was consciously accessible. Brain potentials provided additional confirmation of implicit and explicit rule-knowledge. An afternoon nap was interposed between two 20-min learning sessions. Participants who obtained greater amounts of both slow-wave and rapid-eye-movement sleep showed increased sensitivity to the hidden linguistic rule in the second session. We conclude that during sleep, reactivation of linguistic information linked with the rule was instrumental for stabilizing learning. The combination of slow-wave and rapid-eye-movement sleep may synergistically facilitate the abstraction of complex patterns in linguistic input. PMID:25447376

  4. A Flexible Mechanism of Rule Selection Enables Rapid Feature-Based Reinforcement Learning

    PubMed Central

    Balcarras, Matthew; Womelsdorf, Thilo

    2016-01-01

    Learning in a new environment is influenced by prior learning and experience. Correctly applying a rule that maps a context to stimuli, actions, and outcomes enables faster learning and better outcomes compared to relying on strategies for learning that are ignorant of task structure. However, it is often difficult to know when and how to apply learned rules in new contexts. In our study we explored how subjects employ different strategies for learning the relationship between stimulus features and positive outcomes in a probabilistic task context. We test the hypothesis that task naive subjects will show enhanced learning of feature specific reward associations by switching to the use of an abstract rule that associates stimuli by feature type and restricts selections to that dimension. To test this hypothesis we designed a decision making task where subjects receive probabilistic feedback following choices between pairs of stimuli. In the task, trials are grouped in two contexts by blocks, where in one type of block there is no unique relationship between a specific feature dimension (stimulus shape or color) and positive outcomes, and following an un-cued transition, alternating blocks have outcomes that are linked to either stimulus shape or color. Two-thirds of subjects (n = 22/32) exhibited behavior that was best fit by a hierarchical feature-rule model. Supporting the prediction of the model mechanism these subjects showed significantly enhanced performance in feature-reward blocks, and rapidly switched their choice strategy to using abstract feature rules when reward contingencies changed. Choice behavior of other subjects (n = 10/32) was fit by a range of alternative reinforcement learning models representing strategies that do not benefit from applying previously learned rules. In summary, these results show that untrained subjects are capable of flexibly shifting between behavioral rules by leveraging simple model-free reinforcement learning and context

  5. Sensory Intelligence for Extraction of an Abstract Auditory Rule: A Cross-Linguistic Study.

    PubMed

    Guo, Xiao-Tao; Wang, Xiao-Dong; Liang, Xiu-Yuan; Wang, Ming; Chen, Lin

    2018-02-21

    In a complex linguistic environment, while speech sounds can greatly vary, some shared features are often invariant. These invariant features constitute so-called abstract auditory rules. Our previous study has shown that with auditory sensory intelligence, the human brain can automatically extract the abstract auditory rules in the speech sound stream, presumably serving as the neural basis for speech comprehension. However, whether the sensory intelligence for extraction of abstract auditory rules in speech is inherent or experience-dependent remains unclear. To address this issue, we constructed a complex speech sound stream using auditory materials in Mandarin Chinese, in which syllables had a flat lexical tone but differed in other acoustic features to form an abstract auditory rule. This rule was occasionally and randomly violated by the syllables with the rising, dipping or falling tone. We found that both Chinese and foreign speakers detected the violations of the abstract auditory rule in the speech sound stream at a pre-attentive stage, as revealed by the whole-head recordings of mismatch negativity (MMN) in a passive paradigm. However, MMNs peaked earlier in Chinese speakers than in foreign speakers. Furthermore, Chinese speakers showed different MMN peak latencies for the three deviant types, which paralleled recognition points. These findings indicate that the sensory intelligence for extraction of abstract auditory rules in speech sounds is innate but shaped by language experience. Copyright © 2018 IBRO. Published by Elsevier Ltd. All rights reserved.

  6. Learning to use working memory: a reinforcement learning gating model of rule acquisition in rats

    PubMed Central

    Lloyd, Kevin; Becker, Nadine; Jones, Matthew W.; Bogacz, Rafal

    2012-01-01

    Learning to form appropriate, task-relevant working memory representations is a complex process central to cognition. Gating models frame working memory as a collection of past observations and use reinforcement learning (RL) to solve the problem of when to update these observations. Investigation of how gating models relate to brain and behavior remains, however, at an early stage. The current study sought to explore the ability of simple RL gating models to replicate rule learning behavior in rats. Rats were trained in a maze-based spatial learning task that required animals to make trial-by-trial choices contingent upon their previous experience. Using an abstract version of this task, we tested the ability of two gating algorithms, one based on the Actor-Critic and the other on the State-Action-Reward-State-Action (SARSA) algorithm, to generate behavior consistent with the rats'. Both models produced rule-acquisition behavior consistent with the experimental data, though only the SARSA gating model mirrored faster learning following rule reversal. We also found that both gating models learned multiple strategies in solving the initial task, a property which highlights the multi-agent nature of such models and which is of importance in considering the neural basis of individual differences in behavior. PMID:23115551

  7. Learning and Tuning of Fuzzy Rules

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1997-01-01

    In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.

  8. A Local Learning Rule for Independent Component Analysis

    PubMed Central

    Isomura, Takuya; Toyoizumi, Taro

    2016-01-01

    Humans can separately recognize independent sources when they sense their superposition. This decomposition is mathematically formulated as independent component analysis (ICA). While a few biologically plausible learning rules, so-called local learning rules, have been proposed to achieve ICA, their performance varies depending on the parameters characterizing the mixed signals. Here, we propose a new learning rule that is both easy to implement and reliable. Both mathematical and numerical analyses confirm that the proposed rule outperforms other local learning rules over a wide range of parameters. Notably, unlike other rules, the proposed rule can separate independent sources without any preprocessing, even if the number of sources is unknown. The successful performance of the proposed rule is then demonstrated using natural images and movies. We discuss the implications of this finding for our understanding of neuronal information processing and its promising applications to neuromorphic engineering. PMID:27323661

  9. Developmental changes in automatic rule-learning mechanisms across early childhood.

    PubMed

    Mueller, Jutta L; Friederici, Angela D; Männel, Claudia

    2018-06-27

    Infants' ability to learn complex linguistic regularities from early on has been revealed by electrophysiological studies indicating that 3-month-olds, but not adults, can automatically detect non-adjacent dependencies between syllables. While different ERP responses in adults and infants suggest that both linguistic rule learning and its link to basic auditory processing undergo developmental changes, systematic investigations of the developmental trajectories are scarce. In the present study, we assessed 2- and 4-year-olds' ERP indicators of pitch discrimination and linguistic rule learning in a syllable-based oddball design. To test for the relation between auditory discrimination and rule learning, ERP responses to pitch changes were used as predictor for potential linguistic rule-learning effects. Results revealed that 2-year-olds, but not 4-year-olds, showed ERP markers of rule learning. Although, 2-year-olds' rule learning was not dependent on differences in pitch perception, 4-year-old children demonstrated a dependency, such that those children who showed more pronounced responses to pitch changes still showed an effect of rule learning. These results narrow down the developmental decline of the ability for automatic linguistic rule learning to the age between 2 and 4 years, and, moreover, point towards a strong modification of this change by auditory processes. At an age when the ability of automatic linguistic rule learning phases out, rule learning can still be observed in children with enhanced auditory responses. The observed interrelations are plausible causes for age-of-acquisition effects and inter-individual differences in language learning. © 2018 John Wiley & Sons Ltd.

  10. Ego depletion interferes with rule-defined category learning but not non-rule-defined category learning.

    PubMed

    Minda, John P; Rabi, Rahel

    2015-01-01

    Considerable research on category learning has suggested that many cognitive and environmental factors can have a differential effect on the learning of rule-defined (RD) categories as opposed to the learning of non-rule-defined (NRD) categories. Prior research has also suggested that ego depletion can temporarily reduce the capacity for executive functioning and cognitive flexibility. The present study examined whether temporarily reducing participants' executive functioning via a resource depletion manipulation would differentially impact RD and NRD category learning. Participants were either asked to write a story with no restrictions (the control condition), or without using two common letters (the ego depletion condition). Participants were then asked to learn either a set of RD categories or a set of NRD categories. Resource depleted participants performed more poorly than controls on the RD task, but did not differ from controls on the NRD task, suggesting that self regulatory resources are required for successful RD category learning. These results lend support to multiple systems theories and clarify the role of self-regulatory resources within this theory.

  11. Ego depletion interferes with rule-defined category learning but not non-rule-defined category learning

    PubMed Central

    Minda, John P.; Rabi, Rahel

    2015-01-01

    Considerable research on category learning has suggested that many cognitive and environmental factors can have a differential effect on the learning of rule-defined (RD) categories as opposed to the learning of non-rule-defined (NRD) categories. Prior research has also suggested that ego depletion can temporarily reduce the capacity for executive functioning and cognitive flexibility. The present study examined whether temporarily reducing participants’ executive functioning via a resource depletion manipulation would differentially impact RD and NRD category learning. Participants were either asked to write a story with no restrictions (the control condition), or without using two common letters (the ego depletion condition). Participants were then asked to learn either a set of RD categories or a set of NRD categories. Resource depleted participants performed more poorly than controls on the RD task, but did not differ from controls on the NRD task, suggesting that self regulatory resources are required for successful RD category learning. These results lend support to multiple systems theories and clarify the role of self-regulatory resources within this theory. PMID:25688220

  12. Abstraction in Mathematics and Mathematics Learning

    ERIC Educational Resources Information Center

    Mitchelmore, Michael; White, Paul

    2004-01-01

    It is claimed that, since mathematics is essentially a self-contained system, mathematical objects may best be described as "abstract-apart." On the other hand, fundamental mathematical ideas are closely related to the real world and their learning involves empirical concepts. These concepts may be called "abstract-general" because they embody…

  13. Automated Assume-Guarantee Reasoning by Abstraction Refinement

    NASA Technical Reports Server (NTRS)

    Pasareanu, Corina S.; Giannakopoulous, Dimitra; Glannakopoulou, Dimitra

    2008-01-01

    Current automated approaches for compositional model checking in the assume-guarantee style are based on learning of assumptions as deterministic automata. We propose an alternative approach based on abstraction refinement. Our new method computes the assumptions for the assume-guarantee rules as conservative and not necessarily deterministic abstractions of some of the components, and refines those abstractions using counter-examples obtained from model checking them together with the other components. Our approach also exploits the alphabets of the interfaces between components and performs iterative refinement of those alphabets as well as of the abstractions. We show experimentally that our preliminary implementation of the proposed alternative achieves similar or better performance than a previous learning-based implementation.

  14. Refining Linear Fuzzy Rules by Reinforcement Learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap S.; Malkani, Anil

    1996-01-01

    Linear fuzzy rules are increasingly being used in the development of fuzzy logic systems. Radial basis functions have also been used in the antecedents of the rules for clustering in product space which can automatically generate a set of linear fuzzy rules from an input/output data set. Manual methods are usually used in refining these rules. This paper presents a method for refining the parameters of these rules using reinforcement learning which can be applied in domains where supervised input-output data is not available and reinforcements are received only after a long sequence of actions. This is shown for a generalization of radial basis functions. The formation of fuzzy rules from data and their automatic refinement is an important step in closing the gap between the application of reinforcement learning methods in the domains where only some limited input-output data is available.

  15. Learning Problem-Solving Rules as Search Through a Hypothesis Space.

    PubMed

    Lee, Hee Seung; Betts, Shawn; Anderson, John R

    2016-07-01

    Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem property such as computational difficulty of the rules biased the search process and so affected learning. Experiment 2 examined the impact of examples as instructional tools and found that their effectiveness was determined by whether they uniquely pointed to the correct rule. Experiment 3 compared verbal directions with examples and found that both could guide search. The final experiment tried to improve learning by using more explicit verbal directions or by adding scaffolding to the example. While both manipulations improved learning, learning still took the form of a search through a hypothesis space of possible rules. We describe a model that embodies two assumptions: (1) the instruction can bias the rules participants hypothesize rather than directly be encoded into a rule; (2) participants do not have memory for past wrong hypotheses and are likely to retry them. These assumptions are realized in a Markov model that fits all the data by estimating two sets of probabilities. First, the learning condition induced one set of Start probabilities of trying various rules. Second, should this first hypothesis prove wrong, the learning condition induced a second set of Choice probabilities of considering various rules. These findings broaden our understanding of effective instruction and provide implications for instructional design. Copyright © 2015 Cognitive Science Society, Inc.

  16. Abstraction and model evaluation in category learning.

    PubMed

    Vanpaemel, Wolf; Storms, Gert

    2010-05-01

    Thirty previously published data sets, from seminal category learning tasks, are reanalyzed using the varying abstraction model (VAM). Unlike a prototype-versus-exemplar analysis, which focuses on extreme levels of abstraction only, a VAM analysis also considers the possibility of partial abstraction. Whereas most data sets support no abstraction when only the extreme possibilities are considered, we show that evidence for abstraction can be provided using the broader view on abstraction provided by the VAM. The present results generalize earlier demonstrations of partial abstraction (Vanpaemel & Storms, 2008), in which only a small number of data sets was analyzed. Following the dominant modus operandi in category learning research, Vanpaemel and Storms evaluated the models on their best fit, a practice known to ignore the complexity of the models under consideration. In the present study, in contrast, model evaluation not only relies on the maximal likelihood, but also on the marginal likelihood, which is sensitive to model complexity. Finally, using a large recovery study, it is demonstrated that, across the 30 data sets, complexity differences between the models in the VAM family are small. This indicates that a (computationally challenging) complexity-sensitive model evaluation method is uncalled for, and that the use of a (computationally straightforward) complexity-insensitive model evaluation method is justified.

  17. Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

    NASA Astrophysics Data System (ADS)

    Mihai, Gabroveanu

    2015-09-01

    Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

  18. Myths and legends in learning classification rules

    NASA Technical Reports Server (NTRS)

    Buntine, Wray

    1990-01-01

    A discussion is presented of machine learning theory on empirically learning classification rules. Six myths are proposed in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, universal learning algorithms, and interactive learning. Some of the problems raised are also addressed from a Bayesian perspective. Questions are suggested that machine learning researchers should be addressing both theoretically and experimentally.

  19. Concurrence of rule- and similarity-based mechanisms in artificial grammar learning.

    PubMed

    Opitz, Bertram; Hofmann, Juliane

    2015-03-01

    A current theoretical debate regards whether rule-based or similarity-based learning prevails during artificial grammar learning (AGL). Although the majority of findings are consistent with a similarity-based account of AGL it has been argued that these results were obtained only after limited exposure to study exemplars, and performance on subsequent grammaticality judgment tests has often been barely above chance level. In three experiments the conditions were investigated under which rule- and similarity-based learning could be applied. Participants were exposed to exemplars of an artificial grammar under different (implicit and explicit) learning instructions. The analysis of receiver operating characteristics (ROC) during a final grammaticality judgment test revealed that explicit but not implicit learning led to rule knowledge. It also demonstrated that this knowledge base is built up gradually while similarity knowledge governed the initial state of learning. Together these results indicate that rule- and similarity-based mechanisms concur during AGL. Moreover, it could be speculated that two different rule processes might operate in parallel; bottom-up learning via gradual rule extraction and top-down learning via rule testing. Crucially, the latter is facilitated by performance feedback that encourages explicit hypothesis testing. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Learning general phonological rules from distributional information: a computational model.

    PubMed

    Calamaro, Shira; Jarosz, Gaja

    2015-04-01

    Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony (Peperkamp, Le Calvez, Nadal, & Dupoux, 2006). This paper extends the model to account for learning of a broader set of phonological alternations and the formalization of these alternations as general rules. In Experiment 1, we apply the original model to new data in Dutch and demonstrate its limitations in learning nonallophonic rules. In Experiment 2, we extend the model to allow it to learn general rules for alternations that apply to a class of segments. In Experiment 3, the model is further extended to allow for generalization by context; we argue that this generalization must be constrained by linguistic principles. Copyright © 2014 Cognitive Science Society, Inc.

  1. Applications of Machine Learning and Rule Induction,

    DTIC Science & Technology

    1995-02-15

    An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. In this paper...we review the major paradigms for machine learning , including neural networks, instance-based methods, genetic learning, rule induction, and analytic

  2. Myths and legends in learning classification rules

    NASA Technical Reports Server (NTRS)

    Buntine, Wray

    1990-01-01

    This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, 'universal' learning algorithms, and interactive learnings. Some of the problems raised are also addressed from a Bayesian perspective. The paper concludes by suggesting questions that machine learning researchers should be addressing both theoretically and experimentally.

  3. Stochastic abstract policies: generalizing knowledge to improve reinforcement learning.

    PubMed

    Koga, Marcelo L; Freire, Valdinei; Costa, Anna H R

    2015-01-01

    Reinforcement learning (RL) enables an agent to learn behavior by acquiring experience through trial-and-error interactions with a dynamic environment. However, knowledge is usually built from scratch and learning to behave may take a long time. Here, we improve the learning performance by leveraging prior knowledge; that is, the learner shows proper behavior from the beginning of a target task, using the knowledge from a set of known, previously solved, source tasks. In this paper, we argue that building stochastic abstract policies that generalize over past experiences is an effective way to provide such improvement and this generalization outperforms the current practice of using a library of policies. We achieve that contributing with a new algorithm, AbsProb-PI-multiple and a framework for transferring knowledge represented as a stochastic abstract policy in new RL tasks. Stochastic abstract policies offer an effective way to encode knowledge because the abstraction they provide not only generalizes solutions but also facilitates extracting the similarities among tasks. We perform experiments in a robotic navigation environment and analyze the agent's behavior throughout the learning process and also assess the transfer ratio for different amounts of source tasks. We compare our method with the transfer of a library of policies, and experiments show that the use of a generalized policy produces better results by more effectively guiding the agent when learning a target task.

  4. Dissociable roles of medial and lateral PFC in rule learning.

    PubMed

    Cao, Bihua; Li, Wei; Li, Fuhong; Li, Hong

    2016-11-01

    Although the neural basis of rule learning is of great interest to cognitive neuroscientists, the pattern of transient brain activation during rule discovery remains to be investigated. In this study, we measured event-related functional magnetic resonance imaging (fMRI) during distinct phases of rule learning. Twenty-one healthy human volunteers were presented with a series of cards, each containing a clock-like display of 12 circles numbered sequentially. Participants were instructed that a fictitious animal would move from one circle to another either in a regular pattern (according to a rule hidden in consecutive trials) or randomly. Participants were then asked to judge whether a given step followed a rule. While the rule-search phase evoked more activation in the posterior lateral prefrontal cortex (LPFC), the rule-following phase caused stronger activation in the anterior medial prefrontal cortex (MPFC). Importantly, the intermediate phase, the rule-discovery phase evoked more activations in MPFC and dorsal anterior cingulate cortex (dACC) than rule search, and more activations in LPFC than rule following. Therefore, we can conclude that the medial and lateral PFC have dissociable contributions in rule learning.

  5. Rule-Based and Information-Integration Category Learning in Normal Aging

    ERIC Educational Resources Information Center

    Maddox, W. Todd; Pacheco, Jennifer; Reeves, Maia; Zhu, Bo; Schnyer, David M.

    2010-01-01

    The basal ganglia and prefrontal cortex play critical roles in category learning. Both regions evidence age-related structural and functional declines. The current study examined rule-based and information-integration category learning in a group of older and younger adults. Rule-based learning is thought to involve explicit, frontally mediated…

  6. Effects of Abstract and Concrete Simulation Elements on Science Learning

    ERIC Educational Resources Information Center

    Jaakkola, T.; Veermans, K.

    2015-01-01

    Contemporary evidence on the effectiveness of concrete and abstract representations in science education is based solely on studies conducted in college context. There it has been found that learning with abstract representations produces predominantly better outcomes than learning with concrete representations and combining the representations…

  7. A self-learning rule base for command following in dynamical systems

    NASA Technical Reports Server (NTRS)

    Tsai, Wei K.; Lee, Hon-Mun; Parlos, Alexander

    1992-01-01

    In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.

  8. Does hearing two dialects at different times help infants learn dialect-specific rules?

    PubMed Central

    Gonzales, Kalim; Gerken, LouAnn; Gómez, Rebecca L.

    2015-01-01

    Infants might be better at teasing apart dialects with different language rules when hearing the dialects at different times, since language learners do not always combine input heard at different times. However, no previous research has independently varied the temporal distribution of conflicting language input. Twelve-month-olds heard two artificial language streams representing different dialects—a “pure stream” whose sentences adhered to abstract grammar rules like aX bY, and a “mixed stream” wherein any a- or b-word could precede any X- or Y-word. Infants were then tested for generalization of the pure stream’s rules to novel sentences. Supporting our hypothesis, infants showed generalization when the two streams’ sentences alternated in minutes-long intervals without any perceptually salient change across streams (Experiment 2), but not when all sentences from these same streams were randomly interleaved (Experiment 3). Results are interpreted in light of temporal context effects in word learning. PMID:25880342

  9. Evolving fuzzy rules in a learning classifier system

    NASA Technical Reports Server (NTRS)

    Valenzuela-Rendon, Manuel

    1993-01-01

    The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.

  10. Criterion learning in rule-based categorization: Simulation of neural mechanism and new data

    PubMed Central

    Helie, Sebastien; Ell, Shawn W.; Filoteo, J. Vincent; Maddox, W. Todd

    2015-01-01

    In perceptual categorization, rule selection consists of selecting one or several stimulus-dimensions to be used to categorize the stimuli (e.g, categorize lines according to their length). Once a rule has been selected, criterion learning consists of defining how stimuli will be grouped using the selected dimension(s) (e.g., if the selected rule is line length, define ‘long’ and ‘short’). Very little is known about the neuroscience of criterion learning, and most existing computational models do not provide a biological mechanism for this process. In this article, we introduce a new model of rule learning called Heterosynaptic Inhibitory Criterion Learning (HICL). HICL includes a biologically-based explanation of criterion learning, and we use new category-learning data to test key aspects of the model. In HICL, rule selective cells in prefrontal cortex modulate stimulus-response associations using pre-synaptic inhibition. Criterion learning is implemented by a new type of heterosynaptic error-driven Hebbian learning at inhibitory synapses that uses feedback to drive cell activation above/below thresholds representing ionic gating mechanisms. The model is used to account for new human categorization data from two experiments showing that: (1) changing rule criterion on a given dimension is easier if irrelevant dimensions are also changing (Experiment 1), and (2) showing that changing the relevant rule dimension and learning a new criterion is more difficult, but also facilitated by a change in the irrelevant dimension (Experiment 2). We conclude with a discussion of some of HICL’s implications for future research on rule learning. PMID:25682349

  11. Criterion learning in rule-based categorization: simulation of neural mechanism and new data.

    PubMed

    Helie, Sebastien; Ell, Shawn W; Filoteo, J Vincent; Maddox, W Todd

    2015-04-01

    In perceptual categorization, rule selection consists of selecting one or several stimulus-dimensions to be used to categorize the stimuli (e.g., categorize lines according to their length). Once a rule has been selected, criterion learning consists of defining how stimuli will be grouped using the selected dimension(s) (e.g., if the selected rule is line length, define 'long' and 'short'). Very little is known about the neuroscience of criterion learning, and most existing computational models do not provide a biological mechanism for this process. In this article, we introduce a new model of rule learning called Heterosynaptic Inhibitory Criterion Learning (HICL). HICL includes a biologically-based explanation of criterion learning, and we use new category-learning data to test key aspects of the model. In HICL, rule selective cells in prefrontal cortex modulate stimulus-response associations using pre-synaptic inhibition. Criterion learning is implemented by a new type of heterosynaptic error-driven Hebbian learning at inhibitory synapses that uses feedback to drive cell activation above/below thresholds representing ionic gating mechanisms. The model is used to account for new human categorization data from two experiments showing that: (1) changing rule criterion on a given dimension is easier if irrelevant dimensions are also changing (Experiment 1), and (2) showing that changing the relevant rule dimension and learning a new criterion is more difficult, but also facilitated by a change in the irrelevant dimension (Experiment 2). We conclude with a discussion of some of HICL's implications for future research on rule learning. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. Learning General Phonological Rules from Distributional Information: A Computational Model

    ERIC Educational Resources Information Center

    Calamaro, Shira; Jarosz, Gaja

    2015-01-01

    Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony…

  13. Rule induction performance in amnestic mild cognitive impairment and Alzheimer's dementia: examining the role of simple and biconditional rule learning processes.

    PubMed

    Oosterman, Joukje M; Heringa, Sophie M; Kessels, Roy P C; Biessels, Geert Jan; Koek, Huiberdina L; Maes, Joseph H R; van den Berg, Esther

    2017-04-01

    Rule induction tests such as the Wisconsin Card Sorting Test require executive control processes, but also the learning and memorization of simple stimulus-response rules. In this study, we examined the contribution of diminished learning and memorization of simple rules to complex rule induction test performance in patients with amnestic mild cognitive impairment (aMCI) or Alzheimer's dementia (AD). Twenty-six aMCI patients, 39 AD patients, and 32 control participants were included. A task was used in which the memory load and the complexity of the rules were independently manipulated. This task consisted of three conditions: a simple two-rule learning condition (Condition 1), a simple four-rule learning condition (inducing an increase in memory load, Condition 2), and a complex biconditional four-rule learning condition-inducing an increase in complexity and, hence, executive control load (Condition 3). Performance of AD patients declined disproportionately when the number of simple rules that had to be memorized increased (from Condition 1 to 2). An additional increment in complexity (from Condition 2 to 3) did not, however, disproportionately affect performance of the patients. Performance of the aMCI patients did not differ from that of the control participants. In the patient group, correlation analysis showed that memory performance correlated with Condition 1 performance, whereas executive task performance correlated with Condition 2 performance. These results indicate that the reduced learning and memorization of underlying task rules explains a significant part of the diminished complex rule induction performance commonly reported in AD, although results from the correlation analysis suggest involvement of executive control functions as well. Taken together, these findings suggest that care is needed when interpreting rule induction task performance in terms of executive function deficits in these patients.

  14. Rule-based mechanisms of learning for intelligent adaptive flight control

    NASA Technical Reports Server (NTRS)

    Handelman, David A.; Stengel, Robert F.

    1990-01-01

    How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

  15. Learning a New Selection Rule in Visual and Frontal Cortex.

    PubMed

    van der Togt, Chris; Stănişor, Liviu; Pooresmaeili, Arezoo; Albantakis, Larissa; Deco, Gustavo; Roelfsema, Pieter R

    2016-08-01

    How do you make a decision if you do not know the rules of the game? Models of sensory decision-making suggest that choices are slow if evidence is weak, but they may only apply if the subject knows the task rules. Here, we asked how the learning of a new rule influences neuronal activity in the visual (area V1) and frontal cortex (area FEF) of monkeys. We devised a new icon-selection task. On each day, the monkeys saw 2 new icons (small pictures) and learned which one was relevant. We rewarded eye movements to a saccade target connected to the relevant icon with a curve. Neurons in visual and frontal cortex coded the monkey's choice, because the representation of the selected curve was enhanced. Learning delayed the neuronal selection signals and we uncovered the cause of this delay in V1, where learning to select the relevant icon caused an early suppression of surrounding image elements. These results demonstrate that the learning of a new rule causes a transition from fast and random decisions to a more considerate strategy that takes additional time and they reveal the contribution of visual and frontal cortex to the learning process. © The Author 2016. Published by Oxford University Press.

  16. Rule-Based Category Learning in Down Syndrome

    ERIC Educational Resources Information Center

    Phillips, B. Allyson; Conners, Frances A.; Merrill, Edward; Klinger, Mark R.

    2014-01-01

    Rule-based category learning was examined in youths with Down syndrome (DS), youths with intellectual disability (ID), and typically developing (TD) youths. Two tasks measured category learning: the Modified Card Sort task (MCST) and the Concept Formation test of the Woodcock-Johnson-III (Woodcock, McGrew, & Mather, 2001). In regression-based…

  17. Beyond learning fixed rules and social cues: abstraction in the social arena.

    PubMed Central

    Call, Joseph

    2003-01-01

    Abstraction is a central idea in many areas of physical comparative cognition such as categorization, numerical competence or problem solving. This idea, however, has rarely been applied to comparative social cognition. In this paper, I propose that the notion of abstraction can be applied to the social arena and become an important tool to investigate the social cognition and behaviour processes in animals. To make this point, I present recent evidence showing that chimpanzees know about what others can see and about what others intend. These data do not fit either low-level mechanisms based on stimulus-response associations or high-level explanations based on metarepresentational mechanisms such as false belief attribution. Instead, I argue that social abstraction, in particular the development of concepts such as seeing in others, is key to explaining the behaviour of our closest relative in a variety of situations. PMID:12903652

  18. Cognitive changes in conjunctive rule-based category learning: An ERP approach.

    PubMed

    Rabi, Rahel; Joanisse, Marc F; Zhu, Tianshu; Minda, John Paul

    2018-06-25

    When learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule ("easy" stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule ("difficult" stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared with easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning.

  19. Grounded understanding of abstract concepts: The case of STEM learning.

    PubMed

    Hayes, Justin C; Kraemer, David J M

    2017-01-01

    Characterizing the neural implementation of abstract conceptual representations has long been a contentious topic in cognitive science. At the heart of the debate is whether the "sensorimotor" machinery of the brain plays a central role in representing concepts, or whether the involvement of these perceptual and motor regions is merely peripheral or epiphenomenal. The domain of science, technology, engineering, and mathematics (STEM) learning provides an important proving ground for sensorimotor (or grounded) theories of cognition, as concepts in science and engineering courses are often taught through laboratory-based and other hands-on methodologies. In this review of the literature, we examine evidence suggesting that sensorimotor processes strengthen learning associated with the abstract concepts central to STEM pedagogy. After considering how contemporary theories have defined abstraction in the context of semantic knowledge, we propose our own explanation for how body-centered information, as computed in sensorimotor brain regions and visuomotor association cortex, can form a useful foundation upon which to build an understanding of abstract scientific concepts, such as mechanical force. Drawing from theories in cognitive neuroscience, we then explore models elucidating the neural mechanisms involved in grounding intangible concepts, including Hebbian learning, predictive coding, and neuronal recycling. Empirical data on STEM learning through hands-on instruction are considered in light of these neural models. We conclude the review by proposing three distinct ways in which the field of cognitive neuroscience can contribute to STEM learning by bolstering our understanding of how the brain instantiates abstract concepts in an embodied fashion.

  20. Bayesian learning and the psychology of rule induction

    PubMed Central

    Endress, Ansgar D.

    2014-01-01

    In recent years, Bayesian learning models have been applied to an increasing variety of domains. While such models have been criticized on theoretical grounds, the underlying assumptions and predictions are rarely made concrete and tested experimentally. Here, I use Frank and Tenenbaum's (2011) Bayesian model of rule-learning as a case study to spell out the underlying assumptions, and to confront them with the empirical results Frank and Tenenbaum (2011) propose to simulate, as well as with novel experiments. While rule-learning is arguably well suited to rational Bayesian approaches, I show that their models are neither psychologically plausible nor ideal observer models. Further, I show that their central assumption is unfounded: humans do not always preferentially learn more specific rules, but, at least in some situations, those rules that happen to be more salient. Even when granting the unsupported assumptions, I show that all of the experiments modeled by Frank and Tenenbaum (2011) either contradict their models, or have a large number of more plausible interpretations. I provide an alternative account of the experimental data based on simple psychological mechanisms, and show that this account both describes the data better, and is easier to falsify. I conclude that, despite the recent surge in Bayesian models of cognitive phenomena, psychological phenomena are best understood by developing and testing psychological theories rather than models that can be fit to virtually any data. PMID:23454791

  1. Learning Problem-Solving Rules as Search through a Hypothesis Space

    ERIC Educational Resources Information Center

    Lee, Hee Seung; Betts, Shawn; Anderson, John R.

    2016-01-01

    Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem…

  2. Rule Based Category Learning in Patients with Parkinson’s Disease

    PubMed Central

    Price, Amanda; Filoteo, J. Vincent; Maddox, W. Todd

    2009-01-01

    Measures of explicit rule-based category learning are commonly used in neuropsychological evaluation of individuals with Parkinson’s disease (PD) and the pattern of PD performance on these measures tends to be highly varied. We review the neuropsychological literature to clarify the manner in which PD affects the component processes of rule-based category learning and work to identify and resolve discrepancies within this literature. In particular, we address the manner in which PD and its common treatments affect the processes of rule generation, maintenance, shifting and selection. We then integrate the neuropsychological research with relevant neuroimaging and computational modeling evidence to clarify the neurobiological impact of PD on each process. Current evidence indicates that neurochemical changes associated with PD primarily disrupt rule shifting, and may disturb feedback-mediated learning processes that guide rule selection. Although surgical and pharmacological therapies remediate this deficit, it appears that the same treatments may contribute to impaired rule generation, maintenance and selection processes. These data emphasize the importance of distinguishing between the impact of PD and its common treatments when considering the neuropsychological profile of the disease. PMID:19428385

  3. Incremental Learning of Context Free Grammars by Parsing-Based Rule Generation and Rule Set Search

    NASA Astrophysics Data System (ADS)

    Nakamura, Katsuhiko; Hoshina, Akemi

    This paper discusses recent improvements and extensions in Synapse system for inductive inference of context free grammars (CFGs) from sample strings. Synapse uses incremental learning, rule generation based on bottom-up parsing, and the search for rule sets. The form of production rules in the previous system is extended from Revised Chomsky Normal Form A→βγ to Extended Chomsky Normal Form, which also includes A→B, where each of β and γ is either a terminal or nonterminal symbol. From the result of bottom-up parsing, a rule generation mechanism synthesizes minimum production rules required for parsing positive samples. Instead of inductive CYK algorithm in the previous version of Synapse, the improved version uses a novel rule generation method, called ``bridging,'' which bridges the lacked part of the derivation tree for the positive string. The improved version also employs a novel search strategy, called serial search in addition to minimum rule set search. The synthesis of grammars by the serial search is faster than the minimum set search in most cases. On the other hand, the size of the generated CFGs is generally larger than that by the minimum set search, and the system can find no appropriate grammar for some CFL by the serial search. The paper shows experimental results of incremental learning of several fundamental CFGs and compares the methods of rule generation and search strategies.

  4. Learning abstract visual concepts via probabilistic program induction in a Language of Thought.

    PubMed

    Overlan, Matthew C; Jacobs, Robert A; Piantadosi, Steven T

    2017-11-01

    The ability to learn abstract concepts is a powerful component of human cognition. It has been argued that variable binding is the key element enabling this ability, but the computational aspects of variable binding remain poorly understood. Here, we address this shortcoming by formalizing the Hierarchical Language of Thought (HLOT) model of rule learning. Given a set of data items, the model uses Bayesian inference to infer a probability distribution over stochastic programs that implement variable binding. Because the model makes use of symbolic variables as well as Bayesian inference and programs with stochastic primitives, it combines many of the advantages of both symbolic and statistical approaches to cognitive modeling. To evaluate the model, we conducted an experiment in which human subjects viewed training items and then judged which test items belong to the same concept as the training items. We found that the HLOT model provides a close match to human generalization patterns, significantly outperforming two variants of the Generalized Context Model, one variant based on string similarity and the other based on visual similarity using features from a deep convolutional neural network. Additional results suggest that variable binding happens automatically, implying that binding operations do not add complexity to peoples' hypothesized rules. Overall, this work demonstrates that a cognitive model combining symbolic variables with Bayesian inference and stochastic program primitives provides a new perspective for understanding people's patterns of generalization. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Students Learn by Doing: Teaching about Rules of Thumb.

    ERIC Educational Resources Information Center

    Cude, Brenda J.

    1990-01-01

    Identifies situation in which consumers are likely to substitute rules of thumb for research, reviews rules of thumb often used as substitutes, and identifies teaching activities to help students learn when substitution is appropriate. (JOW)

  6. The evolution of social learning rules: payoff-biased and frequency-dependent biased transmission.

    PubMed

    Kendal, Jeremy; Giraldeau, Luc-Alain; Laland, Kevin

    2009-09-21

    Humans and other animals do not use social learning indiscriminately, rather, natural selection has favoured the evolution of social learning rules that make selective use of social learning to acquire relevant information in a changing environment. We present a gene-culture coevolutionary analysis of a small selection of such rules (unbiased social learning, payoff-biased social learning and frequency-dependent biased social learning, including conformism and anti-conformism) in a population of asocial learners where the environment is subject to a constant probability of change to a novel state. We define conditions under which each rule evolves to a genetically polymorphic equilibrium. We find that payoff-biased social learning may evolve under high levels of environmental variation if the fitness benefit associated with the acquired behaviour is either high or low but not of intermediate value. In contrast, both conformist and anti-conformist biases can become fixed when environment variation is low, whereupon the mean fitness in the population is higher than for a population of asocial learners. Our examination of the population dynamics reveals stable limit cycles under conformist and anti-conformist biases and some highly complex dynamics including chaos. Anti-conformists can out-compete conformists when conditions favour a low equilibrium frequency of the learned behaviour. We conclude that evolution, punctuated by the repeated successful invasion of different social learning rules, should continuously favour a reduction in the equilibrium frequency of asocial learning, and propose that, among competing social learning rules, the dominant rule will be the one that can persist with the lowest frequency of asocial learning.

  7. Abstract numerical discrimination learning in rats.

    PubMed

    Taniuchi, Tohru; Sugihara, Junko; Wakashima, Mariko; Kamijo, Makiko

    2016-06-01

    In this study, we examined rats' discrimination learning of the numerical ordering positions of objects. In Experiments 1 and 2, five out of seven rats successfully learned to respond to the third of six identical objects in a row and showed reliable transfer of this discrimination to novel stimuli after being trained with three different training stimuli. In Experiment 3, the three rats from Experiment 2 continued to be trained to respond to the third object in an object array, which included an odd object that needed to be excluded when identifying the target third object. All three rats acquired this selective-counting task of specific stimuli, and two rats showed reliable transfer of this selective-counting performance to test sets of novel stimuli. In Experiment 4, the three rats from Experiment 3 quickly learned to respond to the third stimulus in object rows consisting of either six identical or six different objects. These results offer strong evidence for abstract numerical discrimination learning in rats.

  8. Thermodynamic efficiency of learning a rule in neural networks

    NASA Astrophysics Data System (ADS)

    Goldt, Sebastian; Seifert, Udo

    2017-11-01

    Biological systems have to build models from their sensory input data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a binary classification rule for these inputs from examples provided by a teacher. We analyse the ability of the network to apply the rule to new inputs, that is to generalise from past experience. Using stochastic thermodynamics, we show that the thermodynamic costs of the learning process provide an upper bound on the amount of information that the network is able to learn from its teacher for both batch and online learning. This allows us to introduce a thermodynamic efficiency of learning. We analytically compute the dynamics and the efficiency of a noisy neural network performing online learning in the thermodynamic limit. In particular, we analyse three popular learning algorithms, namely Hebbian, Perceptron and AdaTron learning. Our work extends the methods of stochastic thermodynamics to a new type of learning problem and might form a suitable basis for investigating the thermodynamics of decision-making.

  9. Effects of Variation and Prior Knowledge on Abstract Concept Learning

    ERIC Educational Resources Information Center

    Braithwaite, David W.; Goldstone, Robert L.

    2015-01-01

    Learning abstract concepts through concrete examples may promote learning at the cost of inhibiting transfer. The present study investigated one approach to solving this problem: systematically varying superficial features of the examples. Participants learned to solve problems involving a mathematical concept by studying either superficially…

  10. From Domain-Generality to Domain-Sensitivity: 4-Month-Olds Learn an Abstract Repetition Rule in Music That 7-Month-Olds Do Not

    PubMed Central

    Dawson, Colin; Gerken, LouAnn

    2009-01-01

    Learning must be constrained for it to lead to productive generalizations. Although biology is undoubtedly an important source of constraints, prior experience may be another, leading learners to represent input in ways that are more conducive to some generalizations than others, and/or to up- and downweight features when entertaining generalizations. In two experiments, 4-month-old and 7-month-old infants were familiarized with sequences of musical chords or tones adhering either to an AAB pattern or an ABA pattern. In both cases, the 4-month-olds learned the generalization, but the 7-month-olds did not. The success of the 4-month-olds appears to contradict an account that this type of pattern learning is the provenance of a language-specific rule-learning module. It is not yet clear what drives the age-related change, but plausible candidates include differential experience with language and music, as well as interactions between general cognitive development and stimulus complexity. PMID:19338982

  11. Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

    PubMed

    van Ginneken, Bram

    2017-03-01

    Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.

  12. Using an improved association rules mining optimization algorithm in web-based mobile-learning system

    NASA Astrophysics Data System (ADS)

    Huang, Yin; Chen, Jianhua; Xiong, Shaojun

    2009-07-01

    Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

  13. Learning temporal rules to forecast instability in continuously monitored patients

    PubMed Central

    Dubrawski, Artur; Wang, Donghan; Hravnak, Marilyn; Clermont, Gilles; Pinsky, Michael R

    2017-01-01

    Inductive machine learning, and in particular extraction of association rules from data, has been successfully used in multiple application domains, such as market basket analysis, disease prognosis, fraud detection, and protein sequencing. The appeal of rule extraction techniques stems from their ability to handle intricate problems yet produce models based on rules that can be comprehended by humans, and are therefore more transparent. Human comprehension is a factor that may improve adoption and use of data-driven decision support systems clinically via face validity. In this work, we explore whether we can reliably and informatively forecast cardiorespiratory instability (CRI) in step-down unit (SDU) patients utilizing data from continuous monitoring of physiologic vital sign (VS) measurements. We use a temporal association rule extraction technique in conjunction with a rule fusion protocol to learn how to forecast CRI in continuously monitored patients. We detail our approach and present and discuss encouraging empirical results obtained using continuous multivariate VS data from the bedside monitors of 297 SDU patients spanning 29 346 hours (3.35 patient-years) of observation. We present example rules that have been learned from data to illustrate potential benefits of comprehensibility of the extracted models, and we analyze the empirical utility of each VS as a potential leading indicator of an impending CRI event. PMID:27274020

  14. A Rational Analysis of Rule-Based Concept Learning

    ERIC Educational Resources Information Center

    Goodman, Noah D.; Tenenbaum, Joshua B.; Feldman, Jacob; Griffiths, Thomas L.

    2008-01-01

    This article proposes a new model of human concept learning that provides a rational analysis of learning feature-based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space--a concept language of logical rules. This article compares the model predictions to human generalization judgments in several…

  15. A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

    ERIC Educational Resources Information Center

    Lu, Hongjing; Rojas, Randall R.; Beckers, Tom; Yuille, Alan L.

    2016-01-01

    Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about…

  16. Distinct Roles for the Anterior Cingulate and Dorsolateral Prefrontal Cortices During Conflict Between Abstract Rules

    PubMed Central

    Boschin, Erica A.; Brkic, Merima M.; Simons, Jon S.; Buckley, Mark J.

    2017-01-01

    Abstract Distinct patterns of activity within the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (dlPFC) reported in neuroimaging studies during tasks involving conflict between competing responses have often been cited as evidence for their key contributions to conflict-monitoring and behavioral adaptation, respectively. However, supporting evidence from neuropsychological patients has been scarce and contradictory. We administered a well-studied analog of the Wisconsin Card Sorting Test, designed to elicit conflict between 2 abstract rules, to a cohort of 6 patients with damage to ACC or dlPFC. Patients who had sustained more significant damage to the ACC were not impaired either on a measure of “conflict cost” nor on measures of “conflict-induced behavioral adaptation.” In contrast, damage to dlPFC did not affect the conflict cost measure but abolished the patients’ ability to adapt their behavior following exposure to conflict, compared with controls. This pattern of results complements the findings from nonhuman primates with more circumscribed lesions to ACC or dlPFC on the same task and provides converging evidence that ACC is not necessary for performance when conflict is elicited between 2 abstract rules, whereas dlPFC plays a fundamental role in behavioral adaptation. PMID:28365775

  17. The Convallis Rule for Unsupervised Learning in Cortical Networks

    PubMed Central

    Yger, Pierre; Harris, Kenneth D.

    2013-01-01

    The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the “Convallis rule”, mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex. PMID:24204224

  18. Role of Prefrontal Cortex in Learning and Generalizing Hierarchical Rules in 8-Month-Old Infants

    PubMed Central

    Werchan, Denise M.; Collins, Anne G.E.; Frank, Michael J.

    2016-01-01

    Recent research indicates that adults and infants spontaneously create and generalize hierarchical rule sets during incidental learning. Computational models and empirical data suggest that, in adults, this process is supported by circuits linking prefrontal cortex (PFC) with striatum and their modulation by dopamine, but the neural circuits supporting this form of learning in infants are largely unknown. We used near-infrared spectroscopy to record PFC activity in 8-month-old human infants during a simple audiovisual hierarchical-rule-learning task. Behavioral results confirmed that infants adopted hierarchical rule sets to learn and generalize spoken object–label mappings across different speaker contexts. Infants had increased activity over right dorsal lateral PFC when rule sets switched from one trial to the next, a neural marker related to updating rule sets into working memory in the adult literature. Infants' eye blink rate, a possible physiological correlate of striatal dopamine activity, also increased when rule sets switched from one trial to the next. Moreover, the increase in right dorsolateral PFC activity in conjunction with eye blink rate also predicted infants' generalization ability, providing exploratory evidence for frontostriatal involvement during learning. These findings provide evidence that PFC is involved in rudimentary hierarchical rule learning in 8-month-old infants, an ability that was previously thought to emerge later in life in concert with PFC maturation. SIGNIFICANCE STATEMENT Hierarchical rule learning is a powerful learning mechanism that allows rules to be selected in a context-appropriate fashion and transferred or reused in novel contexts. Data from computational models and adults suggests that this learning mechanism is supported by dopamine-innervated interactions between prefrontal cortex (PFC) and striatum. Here, we provide evidence that PFC also supports hierarchical rule learning during infancy, challenging the

  19. Bayesian Learning and the Psychology of Rule Induction

    ERIC Educational Resources Information Center

    Endress, Ansgar D.

    2013-01-01

    In recent years, Bayesian learning models have been applied to an increasing variety of domains. While such models have been criticized on theoretical grounds, the underlying assumptions and predictions are rarely made concrete and tested experimentally. Here, I use Frank and Tenenbaum's (2011) Bayesian model of rule-learning as a case study to…

  20. Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

    NASA Technical Reports Server (NTRS)

    Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

    1988-01-01

    Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

  1. Learning temporal rules to forecast instability in continuously monitored patients.

    PubMed

    Guillame-Bert, Mathieu; Dubrawski, Artur; Wang, Donghan; Hravnak, Marilyn; Clermont, Gilles; Pinsky, Michael R

    2017-01-01

    Inductive machine learning, and in particular extraction of association rules from data, has been successfully used in multiple application domains, such as market basket analysis, disease prognosis, fraud detection, and protein sequencing. The appeal of rule extraction techniques stems from their ability to handle intricate problems yet produce models based on rules that can be comprehended by humans, and are therefore more transparent. Human comprehension is a factor that may improve adoption and use of data-driven decision support systems clinically via face validity. In this work, we explore whether we can reliably and informatively forecast cardiorespiratory instability (CRI) in step-down unit (SDU) patients utilizing data from continuous monitoring of physiologic vital sign (VS) measurements. We use a temporal association rule extraction technique in conjunction with a rule fusion protocol to learn how to forecast CRI in continuously monitored patients. We detail our approach and present and discuss encouraging empirical results obtained using continuous multivariate VS data from the bedside monitors of 297 SDU patients spanning 29 346 hours (3.35 patient-years) of observation. We present example rules that have been learned from data to illustrate potential benefits of comprehensibility of the extracted models, and we analyze the empirical utility of each VS as a potential leading indicator of an impending CRI event. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  2. Sleep Promotes the Extraction of Grammatical Rules

    PubMed Central

    Nieuwenhuis, Ingrid L. C.; Folia, Vasiliki; Forkstam, Christian; Jensen, Ole; Petersson, Karl Magnus

    2013-01-01

    Grammar acquisition is a high level cognitive function that requires the extraction of complex rules. While it has been proposed that offline time might benefit this type of rule extraction, this remains to be tested. Here, we addressed this question using an artificial grammar learning paradigm. During a short-term memory cover task, eighty-one human participants were exposed to letter sequences generated according to an unknown artificial grammar. Following a time delay of 15 min, 12 h (wake or sleep) or 24 h, participants classified novel test sequences as Grammatical or Non-Grammatical. Previous behavioral and functional neuroimaging work has shown that classification can be guided by two distinct underlying processes: (1) the holistic abstraction of the underlying grammar rules and (2) the detection of sequence chunks that appear at varying frequencies during exposure. Here, we show that classification performance improved after sleep. Moreover, this improvement was due to an enhancement of rule abstraction, while the effect of chunk frequency was unaltered by sleep. These findings suggest that sleep plays a critical role in extracting complex structure from separate but related items during integrative memory processing. Our findings stress the importance of alternating periods of learning with sleep in settings in which complex information must be acquired. PMID:23755173

  3. A network model of behavioural performance in a rule learning task.

    PubMed

    Hasselmo, Michael E; Stern, Chantal E

    2018-04-19

    Humans demonstrate differences in performance on cognitive rule learning tasks which could involve differences in properties of neural circuits. An example model is presented to show how gating of the spread of neural activity could underlie rule learning and the generalization of rules to previously unseen stimuli. This model uses the activity of gating units to regulate the pattern of connectivity between neurons responding to sensory input and subsequent gating units or output units. This model allows analysis of network parameters that could contribute to differences in cognitive rule learning. These network parameters include differences in the parameters of synaptic modification and presynaptic inhibition of synaptic transmission that could be regulated by neuromodulatory influences on neural circuits. Neuromodulatory receptors play an important role in cognitive function, as demonstrated by the fact that drugs that block cholinergic muscarinic receptors can cause cognitive impairments. In discussions of the links between neuromodulatory systems and biologically based traits, the issue of mechanisms through which these linkages are realized is often missing. This model demonstrates potential roles of neural circuit parameters regulated by acetylcholine in learning context-dependent rules, and demonstrates the potential contribution of variation in neural circuit properties and neuromodulatory function to individual differences in cognitive function.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'. © 2018 The Author(s).

  4. Role of Prefrontal Cortex in Learning and Generalizing Hierarchical Rules in 8-Month-Old Infants.

    PubMed

    Werchan, Denise M; Collins, Anne G E; Frank, Michael J; Amso, Dima

    2016-10-05

    Recent research indicates that adults and infants spontaneously create and generalize hierarchical rule sets during incidental learning. Computational models and empirical data suggest that, in adults, this process is supported by circuits linking prefrontal cortex (PFC) with striatum and their modulation by dopamine, but the neural circuits supporting this form of learning in infants are largely unknown. We used near-infrared spectroscopy to record PFC activity in 8-month-old human infants during a simple audiovisual hierarchical-rule-learning task. Behavioral results confirmed that infants adopted hierarchical rule sets to learn and generalize spoken object-label mappings across different speaker contexts. Infants had increased activity over right dorsal lateral PFC when rule sets switched from one trial to the next, a neural marker related to updating rule sets into working memory in the adult literature. Infants' eye blink rate, a possible physiological correlate of striatal dopamine activity, also increased when rule sets switched from one trial to the next. Moreover, the increase in right dorsolateral PFC activity in conjunction with eye blink rate also predicted infants' generalization ability, providing exploratory evidence for frontostriatal involvement during learning. These findings provide evidence that PFC is involved in rudimentary hierarchical rule learning in 8-month-old infants, an ability that was previously thought to emerge later in life in concert with PFC maturation. Hierarchical rule learning is a powerful learning mechanism that allows rules to be selected in a context-appropriate fashion and transferred or reused in novel contexts. Data from computational models and adults suggests that this learning mechanism is supported by dopamine-innervated interactions between prefrontal cortex (PFC) and striatum. Here, we provide evidence that PFC also supports hierarchical rule learning during infancy, challenging the current dogma that PFC is an

  5. Event-related potentials reveal the relations between feature representations at different levels of abstraction.

    PubMed

    Hannah, Samuel D; Shedden, Judith M; Brooks, Lee R; Grundy, John G

    2016-11-01

    In this paper, we use behavioural methods and event-related potentials (ERPs) to explore the relations between informational and instantiated features, as well as the relation between feature abstraction and rule type. Participants are trained to categorize two species of fictitious animals and then identify perceptually novel exemplars. Critically, two groups are given a perfectly predictive counting rule that, according to Hannah and Brooks (2009. Featuring familiarity: How a familiar feature instantiation influences categorization. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 63, 263-275. Retrieved from http://doi.org/10.1037/a0017919), should orient them to using abstract informational features when categorizing the novel transfer items. A third group is taught a feature list rule, which should orient them to using detailed instantiated features. One counting-rule group were taught their rule before any exposure to the actual stimuli, and the other immediately after training, having learned the instantiations first. The feature-list group were also taught their rule after training. The ERP results suggest that at test, the two counting-rule groups processed items differently, despite their identical rule. This not only supports the distinction that informational and instantiated features are qualitatively different feature representations, but also implies that rules can readily operate over concrete inputs, in contradiction to traditional approaches that assume that rules necessarily act on abstract inputs.

  6. Learning and innovative elements of strategy adoption rules expand cooperative network topologies.

    PubMed

    Wang, Shijun; Szalay, Máté S; Zhang, Changshui; Csermely, Peter

    2008-04-09

    Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.

  7. Learning and disrupting invariance in visual recognition with a temporal association rule

    PubMed Central

    Isik, Leyla; Leibo, Joel Z.; Poggio, Tomaso

    2012-01-01

    Learning by temporal association rules such as Foldiak's trace rule is an attractive hypothesis that explains the development of invariance in visual recognition. Consistent with these rules, several recent experiments have shown that invariance can be broken at both the psychophysical and single cell levels. We show (1) that temporal association learning provides appropriate invariance in models of object recognition inspired by the visual cortex, (2) that we can replicate the “invariance disruption” experiments using these models with a temporal association learning rule to develop and maintain invariance, and (3) that despite dramatic single cell effects, a population of cells is very robust to these disruptions. We argue that these models account for the stability of perceptual invariance despite the underlying plasticity of the system, the variability of the visual world and expected noise in the biological mechanisms. PMID:22754523

  8. Applying the Rule Space Model to Develop a Learning Progression for Thermochemistry

    NASA Astrophysics Data System (ADS)

    Chen, Fu; Zhang, Shanshan; Guo, Yanfang; Xin, Tao

    2017-12-01

    We used the Rule Space Model, a cognitive diagnostic model, to measure the learning progression for thermochemistry for senior high school students. We extracted five attributes and proposed their hierarchical relationships to model the construct of thermochemistry at four levels using a hypothesized learning progression. For this study, we developed 24 test items addressing the attributes of exothermic and endothermic reactions, chemical bonds and heat quantity change, reaction heat and enthalpy, thermochemical equations, and Hess's law. The test was administered to a sample base of 694 senior high school students taught in 3 schools across 2 cities. Results based on the Rule Space Model analysis indicated that (1) the test items developed by the Rule Space Model were of high psychometric quality for good analysis of difficulties, discriminations, reliabilities, and validities; (2) the Rule Space Model analysis classified the students into seven different attribute mastery patterns; and (3) the initial hypothesized learning progression was modified by the attribute mastery patterns and the learning paths to be more precise and detailed.

  9. Ensemble learning with trees and rules: supervised, semi-supervised, unsupervised

    USDA-ARS?s Scientific Manuscript database

    In this article, we propose several new approaches for post processing a large ensemble of conjunctive rules for supervised and semi-supervised learning problems. We show with various examples that for high dimensional regression problems the models constructed by the post processing the rules with ...

  10. A supervised learning rule for classification of spatiotemporal spike patterns.

    PubMed

    Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

    2016-08-01

    This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

  11. The Utility of Implicit Learning in the Teaching of Rules

    ERIC Educational Resources Information Center

    Saetrevik, Bjorn; Reber, Rolf; Sannum, Petter

    2006-01-01

    The potential impact of implicit learning on education has been repeatedly stressed, though little research has examined this connection directly. The current paper describes two experiments that, inspired by artificial grammar learning experiments, examine the utility of implicit learning as a method for teaching atomic bonding rules to 11-12…

  12. From rule to response: neuronal processes in the premotor and prefrontal cortex.

    PubMed

    Wallis, Jonathan D; Miller, Earl K

    2003-09-01

    The ability to use abstract rules or principles allows behavior to generalize from specific circumstances (e.g., rules learned in a specific restaurant can subsequently be applied to any dining experience). Neurons in the prefrontal cortex (PFC) encode such rules. However, to guide behavior, rules must be linked to motor responses. We investigated the neuronal mechanisms underlying this process by recording from the PFC and the premotor cortex (PMC) of monkeys trained to use two abstract rules: "same" or "different." The monkeys had to either hold or release a lever, depending on whether two successively presented pictures were the same or different, and depending on which rule was in effect. The abstract rules were represented in both regions, although they were more prevalent and were encoded earlier and more strongly in the PMC. There was a perceptual bias in the PFC, relative to the PMC, with more PFC neurons encoding the presented pictures. In contrast, neurons encoding the behavioral response were more prevalent in the PMC, and the selectivity was stronger and appeared earlier in the PMC than in the PFC.

  13. Birth of an abstraction: a dynamical systems account of the discovery of an elsewhere principle in a category learning task.

    PubMed

    Tabor, Whitney; Cho, Pyeong W; Dankowicz, Harry

    2013-01-01

    Human participants and recurrent ("connectionist") neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular ("strong") classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks' encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can be directly compared with the mechanisms of non-connectionist, rule-based accounts. The results reveal that the networks "contain" structures related to mechanisms posited by rule-based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models. © 2013 Cognitive Science Society, Inc.

  14. RuleML-Based Learning Object Interoperability on the Semantic Web

    ERIC Educational Resources Information Center

    Biletskiy, Yevgen; Boley, Harold; Ranganathan, Girish R.

    2008-01-01

    Purpose: The present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions. Design/methodology/approach: As proof of concept, a rule…

  15. A hybrid learning method for constructing compact rule-based fuzzy models.

    PubMed

    Zhao, Wanqing; Niu, Qun; Li, Kang; Irwin, George W

    2013-12-01

    The Takagi–Sugeno–Kang-type rule-based fuzzy model has found many applications in different fields; a major challenge is, however, to build a compact model with optimized model parameters which leads to satisfactory model performance. To produce a compact model, most existing approaches mainly focus on selecting an appropriate number of fuzzy rules. In contrast, this paper considers not only the selection of fuzzy rules but also the structure of each rule premise and consequent, leading to the development of a novel compact rule-based fuzzy model. Here, each fuzzy rule is associated with two sets of input attributes, in which the first is used for constructing the rule premise and the other is employed in the rule consequent. A new hybrid learning method combining the modified harmony search method with a fast recursive algorithm is hereby proposed to determine the structure and the parameters for the rule premises and consequents. This is a hard mixed-integer nonlinear optimization problem, and the proposed hybrid method solves the problem by employing an embedded framework, leading to a significantly reduced number of model parameters and a small number of fuzzy rules with each being as simple as possible. Results from three examples are presented to demonstrate the compactness (in terms of the number of model parameters and the number of rules) and the performance of the fuzzy models obtained by the proposed hybrid learning method, in comparison with other techniques from the literature.

  16. Alternate Learning Center. Abstracts of Inservice Training Programs.

    ERIC Educational Resources Information Center

    Rhode Island State Dept. of Education, Providence. Div. of Development and Operations.

    This booklet is a collection of abstracts describing the 18 programs offered at the Alternate Learning Center of the Rhode Island Teacher Center which has as its Primary function school based inservice training for local teachers and administrators. Each project is described in detail, including course goals, specific objectives, training…

  17. Learning and Processing Abstract Words and Concepts: Insights From Typical and Atypical Development.

    PubMed

    Vigliocco, Gabriella; Ponari, Marta; Norbury, Courtenay

    2018-05-21

    The paper describes two plausible hypotheses concerning the learning of abstract words and concepts. According to a first hypothesis, children would learn abstract words by extracting co-occurrences among words in linguistic input, using, for example, mechanisms as described by models of Distributional Semantics. According to a second hypothesis, children would exploit the fact that abstract words tend to have more emotional associations than concrete words to infer that they refer to internal/mental states. Each hypothesis makes specific predictions with regards to when and which abstract words are more likely to be learned; also they make different predictions concerning the impact of developmental disorders. We start by providing a review of work characterizing how abstract words and concepts are learned in development, especially between the ages of 6 and 12. Second, we review some work from our group that tests the two hypotheses above. This work investigates typically developing (TD) children and children with atypical development (developmental language disorders [DLD] and autism spectrum disorder [ASD] with and without language deficits). We conclude that the use of strategies based on emotional information, or on co-occurrences in language, may play a role at different developmental stages. © 2018 Cognitive Science Society Inc.

  18. Perceptual learning improves adult amblyopic vision through rule-based cognitive compensation.

    PubMed

    Zhang, Jun-Yun; Cong, Lin-Juan; Klein, Stanley A; Levi, Dennis M; Yu, Cong

    2014-04-01

    We investigated whether perceptual learning in adults with amblyopia could be enabled to transfer completely to an orthogonal orientation, which would suggest that amblyopic perceptual learning results mainly from high-level cognitive compensation, rather than plasticity in the amblyopic early visual brain. Nineteen adults (mean age = 22.5 years) with anisometropic and/or strabismic amblyopia were trained following a training-plus-exposure (TPE) protocol. The amblyopic eyes practiced contrast, orientation, or Vernier discrimination at one orientation for six to eight sessions. Then the amblyopic or nonamblyopic eyes were exposed to an orthogonal orientation via practicing an irrelevant task. Training was first performed at a lower spatial frequency (SF), then at a higher SF near the cutoff frequency of the amblyopic eye. Perceptual learning was initially orientation specific. However, after exposure to the orthogonal orientation, learning transferred to an orthogonal orientation completely. Reversing the exposure and training order failed to produce transfer. Initial lower SF training led to broad improvement of contrast sensitivity, and later higher SF training led to more specific improvement at high SFs. Training improved visual acuity by 1.5 to 1.6 lines (P < 0.001) in the amblyopic eyes with computerized tests and a clinical E acuity chart. It also improved stereoacuity by 53% (P < 0.001). The complete transfer of learning suggests that perceptual learning in amblyopia may reflect high-level learning of rules for performing a visual discrimination task. These rules are applicable to new orientations to enable learning transfer. Therefore, perceptual learning may improve amblyopic vision mainly through rule-based cognitive compensation.

  19. A Computational Framework for Understanding Decision Making through Integration of Basic Learning Rules

    PubMed Central

    Bazhenov, Maxim; Huerta, Ramon; Smith, Brian H.

    2013-01-01

    Nonassociative and associative learning rules simultaneously modify neural circuits. However, it remains unclear how these forms of plasticity interact to produce conditioned responses. Here we integrate nonassociative and associative conditioning within a uniform model of olfactory learning in the honeybee. Honeybees show a fairly abrupt increase in response after a number of conditioning trials. The occurrence of this abrupt change takes many more trials after exposure to nonassociative trials than just using associative conditioning. We found that the interaction of unsupervised and supervised learning rules is critical for explaining latent inhibition phenomenon. Associative conditioning combined with the mutual inhibition between the output neurons produces an abrupt increase in performance despite smooth changes of the synaptic weights. The results show that an integrated set of learning rules implemented using fan-out connectivities together with neural inhibition can explain the broad range of experimental data on learning behaviors. PMID:23536082

  20. Compensatory processing during rule-based category learning in older adults.

    PubMed

    Bharani, Krishna L; Paller, Ken A; Reber, Paul J; Weintraub, Sandra; Yanar, Jorge; Morrison, Robert G

    2016-01-01

    Healthy older adults typically perform worse than younger adults at rule-based category learning, but better than patients with Alzheimer's or Parkinson's disease. To further investigate aging's effect on rule-based category learning, we monitored event-related potentials (ERPs) while younger and neuropsychologically typical older adults performed a visual category-learning task with a rule-based category structure and trial-by-trial feedback. Using these procedures, we previously identified ERPs sensitive to categorization strategy and accuracy in young participants. In addition, previous studies have demonstrated the importance of neural processing in the prefrontal cortex and the medial temporal lobe for this task. In this study, older adults showed lower accuracy and longer response times than younger adults, but there were two distinct subgroups of older adults. One subgroup showed near-chance performance throughout the procedure, never categorizing accurately. The other subgroup reached asymptotic accuracy that was equivalent to that in younger adults, although they categorized more slowly. These two subgroups were further distinguished via ERPs. Consistent with the compensation theory of cognitive aging, older adults who successfully learned showed larger frontal ERPs when compared with younger adults. Recruitment of prefrontal resources may have improved performance while slowing response times. Additionally, correlations of feedback-locked P300 amplitudes with category-learning accuracy differentiated successful younger and older adults. Overall, the results suggest that the ability to adapt one's behavior in response to feedback during learning varies across older individuals, and that the failure of some to adapt their behavior may reflect inadequate engagement of prefrontal cortex.

  1. Compensatory Processing During Rule-Based Category Learning in Older Adults

    PubMed Central

    Bharani, Krishna L.; Paller, Ken A.; Reber, Paul J.; Weintraub, Sandra; Yanar, Jorge; Morrison, Robert G.

    2016-01-01

    Healthy older adults typically perform worse than younger adults at rule-based category learning, but better than patients with Alzheimer's or Parkinson's disease. To further investigate aging's effect on rule-based category learning, we monitored event-related potentials (ERPs) while younger and neuropsychologically typical older adults performed a visual category-learning task with a rule-based category structure and trial-by-trial feedback. Using these procedures, we previously identified ERPs sensitive to categorization strategy and accuracy in young participants. In addition, previous studies have demonstrated the importance of neural processing in the prefrontal cortex and the medial temporal lobe for this task. In this study, older adults showed lower accuracy and longer response times than younger adults, but there were two distinct subgroups of older adults. One subgroup showed near-chance performance throughout the procedure, never categorizing accurately. The other subgroup reached asymptotic accuracy that was equivalent to that in younger adults, although they categorized more slowly. These two subgroups were further distinguished via ERPs. Consistent with the compensation theory of cognitive aging, older adults who successfully learned showed larger frontal ERPs when compared with younger adults. Recruitment of prefrontal resources may have improved performance while slowing response times. Additionally, correlations of feedback-locked P300 amplitudes with category-learning accuracy differentiated successful younger and older adults. Overall, the results suggest that the ability to adapt one's behavior in response to feedback during learning varies across older individuals, and that the failure of some to adapt their behavior may reflect inadequate engagement of prefrontal cortex. PMID:26422522

  2. From action to abstraction: Using the hands to learn math

    PubMed Central

    Novack, Miriam A.; Congdon, Eliza L.; Hemani-Lopez, Naureen; Goldin-Meadow, Susan

    2014-01-01

    Previous research has shown that children benefit from gesturing during math instruction. Here we ask whether gesturing promotes learning because it is itself a physical action, or because it uses physical action to represent abstract ideas. To address this question, we taught third-grade children a strategy for solving mathematical equivalence problems that was instantiated in one of three ways: (1) in the physical action children performed on objects, (2) in a concrete gesture miming that action, or (3) in an abstract gesture. All three types of hand movements helped children learn how to solve the problems on which they were trained. However, only gesture led to success on problems that required generalizing the knowledge gained. The results provide the first evidence that gesture promotes transfer of knowledge better than action, and suggest that the beneficial effects gesture has on learning may reside in the features that differentiate it from action. PMID:24503873

  3. Rule-Based Category Learning in Children: The Role of Age and Executive Functioning

    PubMed Central

    Rabi, Rahel; Minda, John Paul

    2014-01-01

    Rule-based category learning was examined in 4–11 year-olds and adults. Participants were asked to learn a set of novel perceptual categories in a classification learning task. Categorization performance improved with age, with younger children showing the strongest rule-based deficit relative to older children and adults. Model-based analyses provided insight regarding the type of strategy being used to solve the categorization task, demonstrating that the use of the task appropriate strategy increased with age. When children and adults who identified the correct categorization rule were compared, the performance deficit was no longer evident. Executive functions were also measured. While both working memory and inhibitory control were related to rule-based categorization and improved with age, working memory specifically was found to marginally mediate the age-related improvements in categorization. When analyses focused only on the sample of children, results showed that working memory ability and inhibitory control were associated with categorization performance and strategy use. The current findings track changes in categorization performance across childhood, demonstrating at which points performance begins to mature and resemble that of adults. Additionally, findings highlight the potential role that working memory and inhibitory control may play in rule-based category learning. PMID:24489658

  4. Perceptual Learning Improves Adult Amblyopic Vision Through Rule-Based Cognitive Compensation

    PubMed Central

    Zhang, Jun-Yun; Cong, Lin-Juan; Klein, Stanley A.; Levi, Dennis M.; Yu, Cong

    2014-01-01

    Purpose. We investigated whether perceptual learning in adults with amblyopia could be enabled to transfer completely to an orthogonal orientation, which would suggest that amblyopic perceptual learning results mainly from high-level cognitive compensation, rather than plasticity in the amblyopic early visual brain. Methods. Nineteen adults (mean age = 22.5 years) with anisometropic and/or strabismic amblyopia were trained following a training-plus-exposure (TPE) protocol. The amblyopic eyes practiced contrast, orientation, or Vernier discrimination at one orientation for six to eight sessions. Then the amblyopic or nonamblyopic eyes were exposed to an orthogonal orientation via practicing an irrelevant task. Training was first performed at a lower spatial frequency (SF), then at a higher SF near the cutoff frequency of the amblyopic eye. Results. Perceptual learning was initially orientation specific. However, after exposure to the orthogonal orientation, learning transferred to an orthogonal orientation completely. Reversing the exposure and training order failed to produce transfer. Initial lower SF training led to broad improvement of contrast sensitivity, and later higher SF training led to more specific improvement at high SFs. Training improved visual acuity by 1.5 to 1.6 lines (P < 0.001) in the amblyopic eyes with computerized tests and a clinical E acuity chart. It also improved stereoacuity by 53% (P < 0.001). Conclusions. The complete transfer of learning suggests that perceptual learning in amblyopia may reflect high-level learning of rules for performing a visual discrimination task. These rules are applicable to new orientations to enable learning transfer. Therefore, perceptual learning may improve amblyopic vision mainly through rule-based cognitive compensation. PMID:24550359

  5. Learning of Alignment Rules between Concept Hierarchies

    NASA Astrophysics Data System (ADS)

    Ichise, Ryutaro; Takeda, Hideaki; Honiden, Shinichi

    With the rapid advances of information technology, we are acquiring much information than ever before. As a result, we need tools for organizing this data. Concept hierarchies such as ontologies and information categorizations are powerful and convenient methods for accomplishing this goal, which have gained wide spread acceptance. Although each concept hierarchy is useful, it is difficult to employ multiple concept hierarchies at the same time because it is hard to align their conceptual structures. This paper proposes a rule learning method that inputs information from a source concept hierarchy and finds suitable location for them in a target hierarchy. The key idea is to find the most similar categories in each hierarchy, where similarity is measured by the κ(kappa) statistic that counts instances belonging to both categories. In order to evaluate our method, we conducted experiments using two internet directories: Yahoo! and LYCOS. We map information instances from the source directory into the target directory, and show that our learned rules agree with a human-generated assignment 76% of the time.

  6. Developing a Learning Progression of Buoyancy to Model Conceptual Change: A Latent Class and Rule Space Model Analysis

    NASA Astrophysics Data System (ADS)

    Gao, Yizhu; Zhai, Xiaoming; Andersson, Björn; Zeng, Pingfei; Xin, Tao

    2018-06-01

    We applied latent class analysis and the rule space model to verify the cumulative characteristic of conceptual change by developing a learning progression for buoyancy. For this study, we first abstracted seven attributes of buoyancy and then developed a hypothesized learning progression for buoyancy. A 14-item buoyancy instrument was administered to 1089 8th grade students to verify and refine the learning progression. The results suggest four levels of progression during conceptual change when 8th grade students understand buoyancy. Students at level 0 can only master Density. When students progress to level 1, they can grasp Direction, Identification, Submerged volume, and Relative density on the basis of the prior level. Then, students gradually master Archimedes' theory as they reach level 2. The most advanced students can further grasp Relation with motion and arrive at level 3. In addition, this four-level learning progression can be accounted for by the Qualitative-Quantitative-Integrative explanatory model.

  7. Basic mathematical rules are encoded by primate prefrontal cortex neurons

    PubMed Central

    Bongard, Sylvia; Nieder, Andreas

    2010-01-01

    Mathematics is based on highly abstract principles, or rules, of how to structure, process, and evaluate numerical information. If and how mathematical rules can be represented by single neurons, however, has remained elusive. We therefore recorded the activity of individual prefrontal cortex (PFC) neurons in rhesus monkeys required to switch flexibly between “greater than” and “less than” rules. The monkeys performed this task with different numerical quantities and generalized to set sizes that had not been presented previously, indicating that they had learned an abstract mathematical principle. The most prevalent activity recorded from randomly selected PFC neurons reflected the mathematical rules; purely sensory- and memory-related activity was almost absent. These data show that single PFC neurons have the capacity to represent flexible operations on most abstract numerical quantities. Our findings support PFC network models implementing specific “rule-coding” units that control the flow of information between segregated input, memory, and output layers. We speculate that these neuronal circuits in the monkey lateral PFC could readily have been adopted in the course of primate evolution for syntactic processing of numbers in formalized mathematical systems. PMID:20133872

  8. Prefrontal Contributions to Rule-Based and Information-Integration Category Learning

    ERIC Educational Resources Information Center

    Schnyer, David M.; Maddox, W. Todd; Ell, Shawn; Davis, Sarah; Pacheco, Jenni; Verfaellie, Mieke

    2009-01-01

    Previous research revealed that the basal ganglia play a critical role in category learning [Ell, S. W., Marchant, N. L., & Ivry, R. B. (2006). "Focal putamen lesions impair learning in rule-based, but not information-integration categorization tasks." "Neuropsychologia", 44(10), 1737-1751; Maddox, W. T. & Filoteo, J.…

  9. Distinct Roles for the Anterior Cingulate and Dorsolateral Prefrontal Cortices During Conflict Between Abstract Rules.

    PubMed

    Boschin, Erica A; Brkic, Merima M; Simons, Jon S; Buckley, Mark J

    2017-01-01

    Distinct patterns of activity within the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (dlPFC) reported in neuroimaging studies during tasks involving conflict between competing responses have often been cited as evidence for their key contributions to conflict-monitoring and behavioral adaptation, respectively. However, supporting evidence from neuropsychological patients has been scarce and contradictory. We administered a well-studied analog of the Wisconsin Card Sorting Test, designed to elicit conflict between 2 abstract rules, to a cohort of 6 patients with damage to ACC or dlPFC. Patients who had sustained more significant damage to the ACC were not impaired either on a measure of "conflict cost" nor on measures of "conflict-induced behavioral adaptation." In contrast, damage to dlPFC did not affect the conflict cost measure but abolished the patients' ability to adapt their behavior following exposure to conflict, compared with controls. This pattern of results complements the findings from nonhuman primates with more circumscribed lesions to ACC or dlPFC on the same task and provides converging evidence that ACC is not necessary for performance when conflict is elicited between 2 abstract rules, whereas dlPFC plays a fundamental role in behavioral adaptation. © The Author 2016. Published by Oxford University Press.

  10. Discontinuous categories affect information-integration but not rule-based category learning.

    PubMed

    Maddox, W Todd; Filoteo, J Vincent; Lauritzen, J Scott; Connally, Emily; Hejl, Kelli D

    2005-07-01

    Three experiments were conducted that provide a direct examination of within-category discontinuity manipulations on the implicit, procedural-based learning and the explicit, hypothesis-testing systems proposed in F. G. Ashby, L. A. Alfonso-Reese, A. U. Turken, and E. M. Waldron's (1998) competition between verbal and implicit systems model. Discontinuous categories adversely affected information-integration but not rule-based category learning. Increasing the magnitude of the discontinuity did not lead to a significant decline in performance. The distance to the bound provides a reasonable description of the generalization profile associated with the hypothesis-testing system, whereas the distance to the bound plus the distance to the trained response region provides a reasonable description of the generalization profile associated with the procedural-based learning system. These results suggest that within-category discontinuity differentially impacts information-integration but not rule-based category learning and provides information regarding the detailed processing characteristics of each category learning system. ((c) 2005 APA, all rights reserved).

  11. Learning of Rule Ensembles for Multiple Attribute Ranking Problems

    NASA Astrophysics Data System (ADS)

    Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman; Szeląg, Marcin

    In this paper, we consider the multiple attribute ranking problem from a Machine Learning perspective. We propose two approaches to statistical learning of an ensemble of decision rules from decision examples provided by the Decision Maker in terms of pairwise comparisons of some objects. The first approach consists in learning a preference function defining a binary preference relation for a pair of objects. The result of application of this function on all pairs of objects to be ranked is then exploited using the Net Flow Score procedure, giving a linear ranking of objects. The second approach consists in learning a utility function for single objects. The utility function also gives a linear ranking of objects. In both approaches, the learning is based on the boosting technique. The presented approaches to Preference Learning share good properties of the decision rule preference model and have good performance in the massive-data learning problems. As Preference Learning and Multiple Attribute Decision Aiding share many concepts and methodological issues, in the introduction, we review some aspects bridging these two fields. To illustrate the two approaches proposed in this paper, we solve with them a toy example concerning the ranking of a set of cars evaluated by multiple attributes. Then, we perform a large data experiment on real data sets. The first data set concerns credit rating. Since recent research in the field of Preference Learning is motivated by the increasing role of modeling preferences in recommender systems and information retrieval, we chose two other massive data sets from this area - one comes from movie recommender system MovieLens, and the other concerns ranking of text documents from 20 Newsgroups data set.

  12. Differential impact of relevant and irrelevant dimension primes on rule-based and information-integration category learning.

    PubMed

    Grimm, Lisa R; Maddox, W Todd

    2013-11-01

    Research has identified multiple category-learning systems with each being "tuned" for learning categories with different task demands and each governed by different neurobiological systems. Rule-based (RB) classification involves testing verbalizable rules for category membership while information-integration (II) classification requires the implicit learning of stimulus-response mappings. In the first study to directly test rule priming with RB and II category learning, we investigated the influence of the availability of information presented at the beginning of the task. Participants viewed lines that varied in length, orientation, and position on the screen, and were primed to focus on stimulus dimensions that were relevant or irrelevant to the correct classification rule. In Experiment 1, we used an RB category structure, and in Experiment 2, we used an II category structure. Accuracy and model-based analyses suggested that a focus on relevant dimensions improves RB task performance later in learning while a focus on an irrelevant dimension improves II task performance early in learning. © 2013.

  13. Proof Rules for Automated Compositional Verification through Learning

    NASA Technical Reports Server (NTRS)

    Barringer, Howard; Giannakopoulou, Dimitra; Pasareanu, Corina S.

    2003-01-01

    Compositional proof systems not only enable the stepwise development of concurrent processes but also provide a basis to alleviate the state explosion problem associated with model checking. An assume-guarantee style of specification and reasoning has long been advocated to achieve compositionality. However, this style of reasoning is often non-trivial, typically requiring human input to determine appropriate assumptions. In this paper, we present novel assume- guarantee rules in the setting of finite labelled transition systems with blocking communication. We show how these rules can be applied in an iterative and fully automated fashion within a framework based on learning.

  14. Abstraction and Assume-Guarantee Reasoning for Automated Software Verification

    NASA Technical Reports Server (NTRS)

    Chaki, S.; Clarke, E.; Giannakopoulou, D.; Pasareanu, C. S.

    2004-01-01

    Compositional verification and abstraction are the key techniques to address the state explosion problem associated with model checking of concurrent software. A promising compositional approach is to prove properties of a system by checking properties of its components in an assume-guarantee style. This article proposes a framework for performing abstraction and assume-guarantee reasoning of concurrent C code in an incremental and fully automated fashion. The framework uses predicate abstraction to extract and refine finite state models of software and it uses an automata learning algorithm to incrementally construct assumptions for the compositional verification of the abstract models. The framework can be instantiated with different assume-guarantee rules. We have implemented our approach in the COMFORT reasoning framework and we show how COMFORT out-performs several previous software model checking approaches when checking safety properties of non-trivial concurrent programs.

  15. Topic categorisation of statements in suicide notes with integrated rules and machine learning.

    PubMed

    Kovačević, Aleksandar; Dehghan, Azad; Keane, John A; Nenadic, Goran

    2012-01-01

    We describe and evaluate an automated approach used as part of the i2b2 2011 challenge to identify and categorise statements in suicide notes into one of 15 topics, including Love, Guilt, Thankfulness, Hopelessness and Instructions. The approach combines a set of lexico-syntactic rules with a set of models derived by machine learning from a training dataset. The machine learning models rely on named entities, lexical, lexico-semantic and presentation features, as well as the rules that are applicable to a given statement. On a testing set of 300 suicide notes, the approach showed the overall best micro F-measure of up to 53.36%. The best precision achieved was 67.17% when only rules are used, whereas best recall of 50.57% was with integrated rules and machine learning. While some topics (eg, Sorrow, Anger, Blame) prove challenging, the performance for relatively frequent (eg, Love) and well-scoped categories (eg, Thankfulness) was comparatively higher (precision between 68% and 79%), suggesting that automated text mining approaches can be effective in topic categorisation of suicide notes.

  16. Deficits in Category Learning in Older Adults: Rule-Based Versus Clustering Accounts

    PubMed Central

    2017-01-01

    Memory research has long been one of the key areas of investigation for cognitive aging researchers but only in the last decade or so has categorization been used to understand age differences in cognition. Categorization tasks focus more heavily on the grouping and organization of items in memory, and often on the process of learning relationships through trial and error. Categorization studies allow researchers to more accurately characterize age differences in cognition: whether older adults show declines in the way in which they represent categories with simple rules or declines in representing categories by similarity to past examples. In the current study, young and older adults participated in a set of classic category learning problems, which allowed us to distinguish between three hypotheses: (a) rule-complexity: categories were represented exclusively with rules and older adults had differential difficulty when more complex rules were required, (b) rule-specific: categories could be represented either by rules or by similarity, and there were age deficits in using rules, and (c) clustering: similarity was mainly used and older adults constructed a less-detailed representation by lumping more items into fewer clusters. The ordinal levels of performance across different conditions argued against rule-complexity, as older adults showed greater deficits on less complex categories. The data also provided evidence against rule-specificity, as single-dimensional rules could not explain age declines. Instead, computational modeling of the data indicated that older adults utilized fewer conceptual clusters of items in memory than did young adults. PMID:28816474

  17. Finding Influential Users in Social Media Using Association Rule Learning

    NASA Astrophysics Data System (ADS)

    Erlandsson, Fredrik; Bródka, Piotr; Borg, Anton; Johnson, Henric

    2016-04-01

    Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.

  18. Extracting rate changes in transcriptional regulation from MEDLINE abstracts.

    PubMed

    Liu, Wenting; Miao, Kui; Li, Guangxia; Chang, Kuiyu; Zheng, Jie; Rajapakse, Jagath C

    2014-01-01

    Time delays are important factors that are often neglected in gene regulatory network (GRN) inference models. Validating time delays from knowledge bases is a challenge since the vast majority of biological databases do not record temporal information of gene regulations. Biological knowledge and facts on gene regulations are typically extracted from bio-literature with specialized methods that depend on the regulation task. In this paper, we mine evidences for time delays related to the transcriptional regulation of yeast from the PubMed abstracts. Since the vast majority of abstracts lack quantitative time information, we can only collect qualitative evidences of time delays. Specifically, the speed-up or delay in transcriptional regulation rate can provide evidences for time delays (shorter or longer) in GRN. Thus, we focus on deriving events related to rate changes in transcriptional regulation. A corpus of yeast regulation related abstracts was manually labeled with such events. In order to capture these events automatically, we create an ontology of sub-processes that are likely to result in transcription rate changes by combining textual patterns and biological knowledge. We also propose effective feature extraction methods based on the created ontology to identify the direct evidences with specific details of these events. Our ontologies outperform existing state-of-the-art gene regulation ontologies in the automatic rule learning method applied to our corpus. The proposed deterministic ontology rule-based method can achieve comparable performance to the automatic rule learning method based on decision trees. This demonstrates the effectiveness of our ontology in identifying rate-changing events. We also tested the effectiveness of the proposed feature mining methods on detecting direct evidence of events. Experimental results show that the machine learning method on these features achieves an F1-score of 71.43%. The manually labeled corpus of events

  19. Learning multiple rules simultaneously: Affixes are more salient than reduplications.

    PubMed

    Gervain, Judit; Endress, Ansgar D

    2017-04-01

    Language learners encounter numerous opportunities to learn regularities, but need to decide which of these regularities to learn, because some are not productive in their native language. Here, we present an account of rule learning based on perceptual and memory primitives (Endress, Dehaene-Lambertz, & Mehler, Cognition, 105(3), 577-614, 2007; Endress, Nespor, & Mehler, Trends in Cognitive Sciences, 13(8), 348-353, 2009), suggesting that learners preferentially learn regularities that are more salient to them, and that the pattern of salience reflects the frequency of language features across languages. We contrast this view with previous artificial grammar learning research, which suggests that infants "choose" the regularities they learn based on rational, Bayesian criteria (Frank & Tenenbaum, Cognition, 120(3), 360-371, 2013; Gerken, Cognition, 98(3)B67-B74, 2006, Cognition, 115(2), 362-366, 2010). In our experiments, adult participants listened to syllable strings starting with a syllable reduplication and always ending with the same "affix" syllable, or to syllable strings starting with this "affix" syllable and ending with the "reduplication". Both affixation and reduplication are frequently used for morphological marking across languages. We find three crucial results. First, participants learned both regularities simultaneously. Second, affixation regularities seemed easier to learn than reduplication regularities. Third, regularities in sequence offsets were easier to learn than regularities at sequence onsets. We show that these results are inconsistent with previous Bayesian rule learning models, but mesh well with the perceptual or memory primitives view. Further, we show that the pattern of salience revealed in our experiments reflects the distribution of regularities across languages. Ease of acquisition might thus be one determinant of the frequency of regularities across languages.

  20. A hierarchical structure for representing and learning fuzzy rules

    NASA Technical Reports Server (NTRS)

    Yager, Ronald R.

    1993-01-01

    Yager provides an example in which the flat representation of fuzzy if-then rules leads to unsatisfactory results. Consider a rule base consisting to two rules: if U is 12 the V is 29; if U is (10-15) the V is (25-30). If U = 12 we would get V is G where G = (25-30). The application of the defuzzification process leads to a selection of V = 27.5. Thus we see that the very specific instruction was not followed. The problem with the technique used is that the most specific information was swamped by the less specific information. In this paper we shall provide for a new structure for the representation of fuzzy if-then rules. The representational form introduced here is called a Hierarchical Prioritized Structure (HPS) representation. Most importantly in addition to overcoming the problem illustrated in the previous example this HPS representation has an inherent capability to emulate the learning of general rules and provides a reasonable accurate cognitive mapping of how human beings store information.

  1. Characterizing Rule-Based Category Learning Deficits in Patients with Parkinson's Disease

    ERIC Educational Resources Information Center

    Filoteo, J. Vincent; Maddox, W. Todd; Ing, A. David; Song, David D.

    2007-01-01

    Parkinson's disease (PD) patients and normal controls were tested in three category learning experiments to determine if previously observed rule-based category learning impairments in PD patients were due to deficits in selective attention or working memory. In Experiment 1, optimal categorization required participants to base their decision on a…

  2. An implementation and analysis of the Abstract Syntax Notation One and the basic encoding rules

    NASA Technical Reports Server (NTRS)

    Harvey, James D.; Weaver, Alfred C.

    1990-01-01

    The details of abstract syntax notation one standard (ASN.1) and the basic encoding rules standard (BER) that collectively solve the problem of data transfer across incompatible host environments are presented, and a compiler that was built to automate their use is described. Experiences with this compiler are also discussed which provide a quantitative analysis of the performance costs associated with the application of these standards. An evaluation is offered as to how well suited ASN.1 and BER are in solving the common data representation problem.

  3. Search for Minimal and Semi-Minimal Rule Sets in Incremental Learning of Context-Free and Definite Clause Grammars

    NASA Astrophysics Data System (ADS)

    Imada, Keita; Nakamura, Katsuhiko

    This paper describes recent improvements to Synapse system for incremental learning of general context-free grammars (CFGs) and definite clause grammars (DCGs) from positive and negative sample strings. An important feature of our approach is incremental learning, which is realized by a rule generation mechanism called “bridging” based on bottom-up parsing for positive samples and the search for rule sets. The sizes of rule sets and the computation time depend on the search strategies. In addition to the global search for synthesizing minimal rule sets and serial search, another method for synthesizing semi-optimum rule sets, we incorporate beam search to the system for synthesizing semi-minimal rule sets. The paper shows several experimental results on learning CFGs and DCGs, and we analyze the sizes of rule sets and the computation time.

  4. Promoting middle school students’ abstract-thinking ability through cognitive apprenticeship instruction in mathematics learning

    NASA Astrophysics Data System (ADS)

    Yusepa, B. G. P.; Kusumah, Y. S.; Kartasasmita, B. G.

    2018-01-01

    The aim of this study is to get an in-depth understanding of students’ abstract-thinking ability in mathematics learning. This study was an experimental research with pre-test and post-test control group design. The subject of this study was eighth-grade students from two junior high schools in Bandung. In each schools, two parallel groups were selected and assigned into control and experimental groups. The experimental group was exposed to Cognitive Apprenticeship Instruction (CAI) treatment, whereas the control group was exposed to conventional learning. The results showed that abstract-thinking ability of students in experimental group was better than that of those in control group in which it could be observed from the overall and school level. It could be concluded that CAI could be a good alternative learning model to enhance students’ abstract-thinking ability.

  5. Newly learned word forms are abstract and integrated immediately after acquisition

    PubMed Central

    Kapnoula, Efthymia C.; McMurray, Bob

    2015-01-01

    A hotly debated question in word learning concerns the conditions under which newly learned words compete or interfere with familiar words during spoken word recognition. This has recently been described as a key marker of the integration of a new word into the lexicon and was thought to require consolidation Dumay & Gaskell, (Psychological Science, 18, 35–39, 2007; Gaskell & Dumay, Cognition, 89, 105–132, 2003). Recently, however, Kapnoula, Packard, Gupta, and McMurray, (Cognition, 134, 85–99, 2015) showed that interference can be observed immediately after a word is first learned, implying very rapid integration of new words into the lexicon. It is an open question whether these kinds of effects derive from episodic traces of novel words or from more abstract and lexicalized representations. Here we addressed this question by testing inhibition for newly learned words using training and test stimuli presented in different talker voices. During training, participants were exposed to a set of nonwords spoken by a female speaker. Immediately after training, we assessed the ability of the novel word forms to inhibit familiar words, using a variant of the visual world paradigm. Crucially, the test items were produced by a male speaker. An analysis of fixations showed that even with a change in voice, newly learned words interfered with the recognition of similar known words. These findings show that lexical competition effects from newly learned words spread across different talker voices, which suggests that newly learned words can be sufficiently lexicalized, and abstract with respect to talker voice, without consolidation. PMID:26202702

  6. A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems

    DTIC Science & Technology

    1990-11-01

    Intelligence Systems," in Distributed Artifcial Intelligence , vol. II, L. Gasser and M. Huhns (eds), Pitman, London, 1989, pp. 413-430. Shaw, M. Harrow, B...IDTIC FILE COPY A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems N Michael I. Shaw...SUBTITLE 5. FUNDING NUMBERS A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems 6

  7. Relationship between mathematical abstraction in learning parallel coordinates concept and performance in learning analytic geometry of pre-service mathematics teachers: an investigation

    NASA Astrophysics Data System (ADS)

    Nurhasanah, F.; Kusumah, Y. S.; Sabandar, J.; Suryadi, D.

    2018-05-01

    As one of the non-conventional mathematics concepts, Parallel Coordinates is potential to be learned by pre-service mathematics teachers in order to give them experiences in constructing richer schemes and doing abstraction process. Unfortunately, the study related to this issue is still limited. This study wants to answer a research question “to what extent the abstraction process of pre-service mathematics teachers in learning concept of Parallel Coordinates could indicate their performance in learning Analytic Geometry”. This is a case study that part of a larger study in examining mathematical abstraction of pre-service mathematics teachers in learning non-conventional mathematics concept. Descriptive statistics method is used in this study to analyze the scores from three different tests: Cartesian Coordinate, Parallel Coordinates, and Analytic Geometry. The participants in this study consist of 45 pre-service mathematics teachers. The result shows that there is a linear association between the score on Cartesian Coordinate and Parallel Coordinates. There also found that the higher levels of the abstraction process in learning Parallel Coordinates are linearly associated with higher student achievement in Analytic Geometry. The result of this study shows that the concept of Parallel Coordinates has a significant role for pre-service mathematics teachers in learning Analytic Geometry.

  8. Reimagining the learned intermediary rule for the new pharmaceutical marketplace.

    PubMed

    Hall, Timothy S

    2004-01-01

    For the past decade, the learned intermediary rule--the rule of tort law that provides that drug manufacturers may satisfy their duty to warn of a drug's dangers by warning the prescribing physician rather than the end user of the drug--has been the subject of vigorous academic debate. That debate has been largely moot, however, as the courts have proven reluctant to make significant inroads on the protection offered by the Rule to drug manufacturers. This Article proposes a new approach to the Rule. Part I discusses the history and overwhelming adoption of the Rule pursuant to the Restatement (Second) of Torts. Part II argues that changes in the health care delivery system have resulted in a legal system that introduces market distortions by effectively immunizing the pharmaceutical industry from the legal and social consequences of its own actions. Part III then sets forth a reconceptualization of the Rule, which preserves the Rule's benefits with respect to the drug industry, the health care system, and the goals of tort law, while also strengthening the protection the tort system offers to individuals injured by prescription drugs.

  9. Concreteness Fading of Algebraic Instruction: Effects on Learning

    ERIC Educational Resources Information Center

    Ottmar, Erin; Landy, David

    2017-01-01

    Learning algebra is difficult for many students in part because of an emphasis on the memorization of abstract rules. Algebraic reasoners across expertise levels often rely on perceptual-motor strategies to make these rules meaningful and memorable. However, in many cases, rules are provided as patterns to be memorized verbally, with little overt…

  10. Bothered by abstractness or engaged by cohesion? Experts' explanations enhance novices' deep-learning.

    PubMed

    Lachner, Andreas; Nückles, Matthias

    2015-03-01

    Experts' explanations have been shown to better enhance novices' transfer as compared with advanced students' explanations. Based on research on expertise and text comprehension, we investigated whether the abstractness or the cohesion of experts' and intermediates' explanations accounted for novices' learning. In Study 1, we showed that the superior cohesion of experts' explanations accounted for most of novices' transfer, whereas the degree of abstractness did not impact novices' transfer performance. In Study 2, we investigated novices' processing while learning with experts' and intermediates' explanations. We found that novices studying experts' explanations actively self-regulated their processing of the explanations, as they showed mainly deep-processing activities, whereas novices learning with intermediates' explanations were mainly engaged in shallow-processing activities by paraphrasing the explanations. Thus, we concluded that subject-matter expertise is a crucial prerequisite for instructors. Despite the abstract character of experts' explanations, their subject-matter expertise enables them to generate highly cohesive explanations that serve as a valuable scaffold for students' construction of flexible knowledge by engaging them in deep-level processing. PsycINFO Database Record (c) 2015 APA, all rights reserved.

  11. Statistical Learning Is Constrained to Less Abstract Patterns in Complex Sensory Input (but not the Least)

    PubMed Central

    Emberson, Lauren L.; Rubinstein, Dani

    2016-01-01

    The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1—dog1, bird2—dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1— dog_picture1, bird_picture2—dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs

  12. Genetic learning in rule-based and neural systems

    NASA Technical Reports Server (NTRS)

    Smith, Robert E.

    1993-01-01

    The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

  13. Fuzzy CMAC With incremental Bayesian Ying-Yang learning and dynamic rule construction.

    PubMed

    Nguyen, M N

    2010-04-01

    Inspired by the philosophy of ancient Chinese Taoism, Xu's Bayesian ying-yang (BYY) learning technique performs clustering by harmonizing the training data (yang) with the solution (ying). In our previous work, the BYY learning technique was applied to a fuzzy cerebellar model articulation controller (FCMAC) to find the optimal fuzzy sets; however, this is not suitable for time series data analysis. To address this problem, we propose an incremental BYY learning technique in this paper, with the idea of sliding window and rule structure dynamic algorithms. Three contributions are made as a result of this research. First, an online expectation-maximization algorithm incorporated with the sliding window is proposed for the fuzzification phase. Second, the memory requirement is greatly reduced since the entire data set no longer needs to be obtained during the prediction process. Third, the rule structure dynamic algorithm with dynamically initializing, recruiting, and pruning rules relieves the "curse of dimensionality" problem that is inherent in the FCMAC. Because of these features, the experimental results of the benchmark data sets of currency exchange rates and Mackey-Glass show that the proposed model is more suitable for real-time streaming data analysis.

  14. Automatic de-identification of French clinical records: comparison of rule-based and machine-learning approaches.

    PubMed

    Grouin, Cyril; Zweigenbaum, Pierre

    2013-01-01

    In this paper, we present a comparison of two approaches to automatically de-identify medical records written in French: a rule-based system and a machine-learning based system using a conditional random fields (CRF) formalism. Both systems have been designed to process nine identifiers in a corpus of medical records in cardiology. We performed two evaluations: first, on 62 documents in cardiology, and on 10 documents in foetopathology - produced by optical character recognition (OCR) - to evaluate the robustness of our systems. We achieved a 0.843 (rule-based) and 0.883 (machine-learning) exact match overall F-measure in cardiology. While the rule-based system allowed us to achieve good results on nominative (first and last names) and numerical data (dates, phone numbers, and zip codes), the machine-learning approach performed best on more complex categories (postal addresses, hospital names, medical devices, and towns). On the foetopathology corpus, although our systems have not been designed for this corpus and despite OCR character recognition errors, we obtained promising results: a 0.681 (rule-based) and 0.638 (machine-learning) exact-match overall F-measure. This demonstrates that existing tools can be applied to process new documents of lower quality.

  15. Applying the Rule Space Model to Develop a Learning Progression for Thermochemistry

    ERIC Educational Resources Information Center

    Chen, Fu; Zhang, Shanshan; Guo, Yanfang; Xin, Tao

    2017-01-01

    We used the Rule Space Model, a cognitive diagnostic model, to measure the learning progression for thermochemistry for senior high school students. We extracted five attributes and proposed their hierarchical relationships to model the construct of thermochemistry at four levels using a hypothesized learning progression. For this study, we…

  16. Exploration of SWRL Rule Bases through Visualization, Paraphrasing, and Categorization of Rules

    NASA Astrophysics Data System (ADS)

    Hassanpour, Saeed; O'Connor, Martin J.; Das, Amar K.

    Rule bases are increasingly being used as repositories of knowledge content on the Semantic Web. As the size and complexity of these rule bases increases, developers and end users need methods of rule abstraction to facilitate rule management. In this paper, we describe a rule abstraction method for Semantic Web Rule Language (SWRL) rules that is based on lexical analysis and a set of heuristics. Our method results in a tree data structure that we exploit in creating techniques to visualize, paraphrase, and categorize SWRL rules. We evaluate our approach by applying it to several biomedical ontologies that contain SWRL rules, and show how the results reveal rule patterns within the rule base. We have implemented our method as a plug-in tool for Protégé-OWL, the most widely used ontology modeling software for the Semantic Web. Our tool can allow users to rapidly explore content and patterns in SWRL rule bases, enabling their acquisition and management.

  17. How learning to abstract shapes neural sound representations

    PubMed Central

    Ley, Anke; Vroomen, Jean; Formisano, Elia

    2014-01-01

    The transformation of acoustic signals into abstract perceptual representations is the essence of the efficient and goal-directed neural processing of sounds in complex natural environments. While the human and animal auditory system is perfectly equipped to process the spectrotemporal sound features, adequate sound identification and categorization require neural sound representations that are invariant to irrelevant stimulus parameters. Crucially, what is relevant and irrelevant is not necessarily intrinsic to the physical stimulus structure but needs to be learned over time, often through integration of information from other senses. This review discusses the main principles underlying categorical sound perception with a special focus on the role of learning and neural plasticity. We examine the role of different neural structures along the auditory processing pathway in the formation of abstract sound representations with respect to hierarchical as well as dynamic and distributed processing models. Whereas most fMRI studies on categorical sound processing employed speech sounds, the emphasis of the current review lies on the contribution of empirical studies using natural or artificial sounds that enable separating acoustic and perceptual processing levels and avoid interference with existing category representations. Finally, we discuss the opportunities of modern analyses techniques such as multivariate pattern analysis (MVPA) in studying categorical sound representations. With their increased sensitivity to distributed activation changes—even in absence of changes in overall signal level—these analyses techniques provide a promising tool to reveal the neural underpinnings of perceptually invariant sound representations. PMID:24917783

  18. Implementation of a spike-based perceptron learning rule using TiO2-x memristors.

    PubMed

    Mostafa, Hesham; Khiat, Ali; Serb, Alexander; Mayr, Christian G; Indiveri, Giacomo; Prodromakis, Themis

    2015-01-01

    Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic "cognitive" capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO2-x memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP) rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episode.

  19. The Effect of Context on Training: Is Learning Situated?

    DTIC Science & Technology

    1994-09-13

    not underlie the central processes of ordinary everyday cognition ? We think not." There are numerous examples where abstract instruction has been shown... instruction , concrete examples, and abstract rules and procedures. Claims made by proponents of Situated Learning Theory suggest that training must be... instruction . This argues against apprenticeship learning during early stages of acquisition for many skills. Further, too much fidelity in simulation may

  20. The effect of negative performance stereotypes on learning.

    PubMed

    Rydell, Robert J; Rydell, Michael T; Boucher, Kathryn L

    2010-12-01

    Stereotype threat (ST) research has focused exclusively on how negative group stereotypes reduce performance. The present work examines if pejorative stereotypes about women in math inhibit their ability to learn the mathematical rules and operations necessary to solve math problems. In Experiment 1, women experiencing ST had difficulty encoding math-related information into memory and, therefore, learned fewer mathematical rules and showed poorer math performance than did controls. In Experiment 2, women experiencing ST while learning modular arithmetic (MA) performed more poorly than did controls on easy MA problems; this effect was due to reduced learning of the mathematical operations underlying MA. In Experiment 3, ST reduced women's, but not men's, ability to learn abstract mathematical rules and to transfer these rules to a second, isomorphic task. This work provides the first evidence that negative stereotypes about women in math reduce their level of mathematical learning and demonstrates that reduced learning due to stereotype threat can lead to poorer performance in negatively stereotyped domains. PsycINFO Database Record (c) 2010 APA, all rights reserved.

  1. A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music.

    PubMed

    Giraldo, Sergio I; Ramirez, Rafael

    2016-01-01

    Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules.

  2. A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music

    PubMed Central

    Giraldo, Sergio I.; Ramirez, Rafael

    2016-01-01

    Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules

  3. The Interactive Effects of the Availability of Objectives and/or Rules on Computer-Based Learning: A Replication.

    ERIC Educational Resources Information Center

    Merrill, Paul F.; And Others

    To replicate and extend the results of a previous study, this project investigated the effects of behavioral objectives and/or rules on computer-based learning task performance. The 133 subjects were randomly assigned to an example-only, objective-example, rule example, or objective-rule example group. The availability of rules and/or objectives…

  4. Learning about Regiochemistry from a Hydrogen-Atom Abstraction Reaction in Water

    ERIC Educational Resources Information Center

    Sears-Dundes, Christopher; Huon, Yoeup; Hotz, Richard P.; Pinhas, Allan R.

    2011-01-01

    An experiment has been developed in which the hydrogen-atom abstraction and the coupling of propionitrile, using Fenton's reagent, are investigated. Students learn about the regiochemistry of radical formation, the stereochemistry of product formation, and the interpretation of GC-MS data, in a safe reaction that can be easily completed in one…

  5. Transcranial infrared laser stimulation improves rule-based, but not information-integration, category learning in humans.

    PubMed

    Blanco, Nathaniel J; Saucedo, Celeste L; Gonzalez-Lima, F

    2017-03-01

    This is the first randomized, controlled study comparing the cognitive effects of transcranial laser stimulation on category learning tasks. Transcranial infrared laser stimulation is a new non-invasive form of brain stimulation that shows promise for wide-ranging experimental and neuropsychological applications. It involves using infrared laser to enhance cerebral oxygenation and energy metabolism through upregulation of the respiratory enzyme cytochrome oxidase, the primary infrared photon acceptor in cells. Previous research found that transcranial infrared laser stimulation aimed at the prefrontal cortex can improve sustained attention, short-term memory, and executive function. In this study, we directly investigated the influence of transcranial infrared laser stimulation on two neurobiologically dissociable systems of category learning: a prefrontal cortex mediated reflective system that learns categories using explicit rules, and a striatally mediated reflexive learning system that forms gradual stimulus-response associations. Participants (n=118) received either active infrared laser to the lateral prefrontal cortex or sham (placebo) stimulation, and then learned one of two category structures-a rule-based structure optimally learned by the reflective system, or an information-integration structure optimally learned by the reflexive system. We found that prefrontal rule-based learning was substantially improved following transcranial infrared laser stimulation as compared to placebo (treatment X block interaction: F(1, 298)=5.117, p=0.024), while information-integration learning did not show significant group differences (treatment X block interaction: F(1, 288)=1.633, p=0.202). These results highlight the exciting potential of transcranial infrared laser stimulation for cognitive enhancement and provide insight into the neurobiological underpinnings of category learning. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. How Do Infants and Toddlers Learn the Rules? Family Discipline and Young Children

    ERIC Educational Resources Information Center

    Smith, Anne B.

    2004-01-01

    This paper examines the issue of how under three year-olds learn the rules of appropriate behaviour in the light of sociocultural, attachment, social learning, ecological theory and sociology of childhood theories. Discipline involves teaching children how to behave acceptably in their family and society, while physical punishment is the use of…

  7. Cognitive Control over Learning: Creating, Clustering, and Generalizing Task-Set Structure

    ERIC Educational Resources Information Center

    Collins, Anne G. E.; Frank, Michael J.

    2013-01-01

    Learning and executive functions such as task-switching share common neural substrates, notably prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for…

  8. Differential Modifications of Synaptic Weights During Odor Rule Learning: Dynamics of Interaction Between the Piriform Cortex with Lower and Higher Brain Areas

    PubMed Central

    Cohen, Yaniv; Wilson, Donald A.; Barkai, Edi

    2015-01-01

    Learning of a complex olfactory discrimination (OD) task results in acquisition of rule learning after prolonged training. Previously, we demonstrated enhanced synaptic connectivity between the piriform cortex (PC) and its ascending and descending inputs from the olfactory bulb (OB) and orbitofrontal cortex (OFC) following OD rule learning. Here, using recordings of evoked field postsynaptic potentials in behaving animals, we examined the dynamics by which these synaptic pathways are modified during rule acquisition. We show profound differences in synaptic connectivity modulation between the 2 input sources. During rule acquisition, the ascending synaptic connectivity from the OB to the anterior and posterior PC is simultaneously enhanced. Furthermore, post-training stimulation of the OB enhanced learning rate dramatically. In sharp contrast, the synaptic input in the descending pathway from the OFC was significantly reduced until training completion. Once rule learning was established, the strength of synaptic connectivity in the 2 pathways resumed its pretraining values. We suggest that acquisition of olfactory rule learning requires a transient enhancement of ascending inputs to the PC, synchronized with a parallel decrease in the descending inputs. This combined short-lived modulation enables the PC network to reorganize in a manner that enables it to first acquire and then maintain the rule. PMID:23960200

  9. Mixing Languages during Learning? Testing the One Subject-One Language Rule.

    PubMed

    Antón, Eneko; Thierry, Guillaume; Duñabeitia, Jon Andoni

    2015-01-01

    In bilingual communities, mixing languages is avoided in formal schooling: even if two languages are used on a daily basis for teaching, only one language is used to teach each given academic subject. This tenet known as the one subject-one language rule avoids mixing languages in formal schooling because it may hinder learning. The aim of this study was to test the scientific ground of this assumption by investigating the consequences of acquiring new concepts using a method in which two languages are mixed as compared to a purely monolingual method. Native balanced bilingual speakers of Basque and Spanish-adults (Experiment 1) and children (Experiment 2)-learnt new concepts by associating two different features to novel objects. Half of the participants completed the learning process in a multilingual context (one feature was described in Basque and the other one in Spanish); while the other half completed the learning phase in a purely monolingual context (both features were described in Spanish). Different measures of learning were taken, as well as direct and indirect indicators of concept consolidation. We found no evidence in favor of the non-mixing method when comparing the results of two groups in either experiment, and thus failed to give scientific support for the educational premise of the one subject-one language rule.

  10. Mixing Languages during Learning? Testing the One Subject—One Language Rule

    PubMed Central

    2015-01-01

    In bilingual communities, mixing languages is avoided in formal schooling: even if two languages are used on a daily basis for teaching, only one language is used to teach each given academic subject. This tenet known as the one subject-one language rule avoids mixing languages in formal schooling because it may hinder learning. The aim of this study was to test the scientific ground of this assumption by investigating the consequences of acquiring new concepts using a method in which two languages are mixed as compared to a purely monolingual method. Native balanced bilingual speakers of Basque and Spanish—adults (Experiment 1) and children (Experiment 2)—learnt new concepts by associating two different features to novel objects. Half of the participants completed the learning process in a multilingual context (one feature was described in Basque and the other one in Spanish); while the other half completed the learning phase in a purely monolingual context (both features were described in Spanish). Different measures of learning were taken, as well as direct and indirect indicators of concept consolidation. We found no evidence in favor of the non-mixing method when comparing the results of two groups in either experiment, and thus failed to give scientific support for the educational premise of the one subject—one language rule. PMID:26107624

  11. Towards application of rule learning to the meta-analysis of clinical data: an example of the metabolic syndrome.

    PubMed

    Wojtusiak, Janusz; Michalski, Ryszard S; Simanivanh, Thipkesone; Baranova, Ancha V

    2009-12-01

    Systematic reviews and meta-analysis of published clinical datasets are important part of medical research. By combining results of multiple studies, meta-analysis is able to increase confidence in its conclusions, validate particular study results, and sometimes lead to new findings. Extensive theory has been built on how to aggregate results from multiple studies and arrive to the statistically valid conclusions. Surprisingly, very little has been done to adopt advanced machine learning methods to support meta-analysis. In this paper we describe a novel machine learning methodology that is capable of inducing accurate and easy to understand attributional rules from aggregated data. Thus, the methodology can be used to support traditional meta-analysis in systematic reviews. Most machine learning applications give primary attention to predictive accuracy of the learned knowledge, and lesser attention to its understandability. Here we employed attributional rules, the special form of rules that are relatively easy to interpret for medical experts who are not necessarily trained in statistics and meta-analysis. The methodology has been implemented and initially tested on a set of publicly available clinical data describing patients with metabolic syndrome (MS). The objective of this application was to determine rules describing combinations of clinical parameters used for metabolic syndrome diagnosis, and to develop rules for predicting whether particular patients are likely to develop secondary complications of MS. The aggregated clinical data was retrieved from 20 separate hospital cohorts that included 12 groups of patients with present liver disease symptoms and 8 control groups of healthy subjects. The total of 152 attributes were used, most of which were measured, however, in different studies. Twenty most common attributes were selected for the rule learning process. By applying the developed rule learning methodology we arrived at several different possible

  12. Assessment of Matrix Multiplication Learning with a Rule-Based Analytical Model--"A Bayesian Network Representation"

    ERIC Educational Resources Information Center

    Zhang, Zhidong

    2016-01-01

    This study explored an alternative assessment procedure to examine learning trajectories of matrix multiplication. It took rule-based analytical and cognitive task analysis methods specifically to break down operation rules for a given matrix multiplication. Based on the analysis results, a hierarchical Bayesian network, an assessment model,…

  13. Rules and mechanisms for efficient two-stage learning in neural circuits.

    PubMed

    Teşileanu, Tiberiu; Ölveczky, Bence; Balasubramanian, Vijay

    2017-04-04

    Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in 'tutor' circuits ( e.g., LMAN) should match plasticity mechanisms in 'student' circuits ( e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.

  14. Recently learned foreign abstract and concrete nouns are represented in distinct cortical networks similar to the native language.

    PubMed

    Mayer, Katja M; Macedonia, Manuela; von Kriegstein, Katharina

    2017-09-01

    In the native language, abstract and concrete nouns are represented in distinct areas of the cerebral cortex. Currently, it is unknown whether this is also the case for abstract and concrete nouns of a foreign language. Here, we taught adult native speakers of German 45 abstract and 45 concrete nouns of a foreign language. After learning the nouns for 5 days, participants performed a vocabulary translation task during functional magnetic resonance imaging. Translating abstract nouns in contrast to concrete nouns elicited responses in regions that are also responsive to abstract nouns in the native language: the left inferior frontal gyrus and the left middle and superior temporal gyri. Concrete nouns elicited larger responses in the angular gyri bilaterally and the left parahippocampal gyrus than abstract nouns. The cluster in the left angular gyrus showed psychophysiological interaction (PPI) with the left lingual gyrus. The left parahippocampal gyrus showed PPI with the posterior cingulate cortex. Similar regions have been previously found for concrete nouns in the native language. The results reveal similarities in the cortical representation of foreign language nouns with the representation of native language nouns that already occur after 5 days of vocabulary learning. Furthermore, we showed that verbal and enriched learning methods were equally suitable to teach foreign abstract and concrete nouns. Hum Brain Mapp 38:4398-4412, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  15. Developing a Learning Progression for Number Sense Based on the Rule Space Model in China

    ERIC Educational Resources Information Center

    Chen, Fu; Yan, Yue; Xin, Tao

    2017-01-01

    The current study focuses on developing the learning progression of number sense for primary school students, and it applies a cognitive diagnostic model, the rule space model, to data analysis. The rule space model analysis firstly extracted nine cognitive attributes and their hierarchy model from the analysis of previous research and the…

  16. Striatal degeneration impairs language learning: evidence from Huntington's disease.

    PubMed

    De Diego-Balaguer, R; Couette, M; Dolbeau, G; Dürr, A; Youssov, K; Bachoud-Lévi, A-C

    2008-11-01

    Although the role of the striatum in language processing is still largely unclear, a number of recent proposals have outlined its specific contribution. Different studies report evidence converging to a picture where the striatum may be involved in those aspects of rule-application requiring non-automatized behaviour. This is the main characteristic of the earliest phases of language acquisition that require the online detection of distant dependencies and the creation of syntactic categories by means of rule learning. Learning of sequences and categorization processes in non-language domains has been known to require striatal recruitment. Thus, we hypothesized that the striatum should play a prominent role in the extraction of rules in learning a language. We studied 13 pre-symptomatic gene-carriers and 22 early stage patients of Huntington's disease (pre-HD), both characterized by a progressive degeneration of the striatum and 21 late stage patients Huntington's disease (18 stage II, two stage III and one stage IV) where cortical degeneration accompanies striatal degeneration. When presented with a simplified artificial language where words and rules could be extracted, early stage Huntington's disease patients (stage I) were impaired in the learning test, demonstrating a greater impairment in rule than word learning compared to the 20 age- and education-matched controls. Huntington's disease patients at later stages were impaired both on word and rule learning. While spared in their overall performance, gene-carriers having learned a set of abstract artificial language rules were then impaired in the transfer of those rules to similar artificial language structures. The correlation analyses among several neuropsychological tests assessing executive function showed that rule learning correlated with tests requiring working memory and attentional control, while word learning correlated with a test involving episodic memory. These learning impairments significantly

  17. Effects of neonatal inferior prefrontal and medial temporal lesions on learning the rule for delayed nonmatching-to-sample.

    PubMed

    Málková, L; Bachevalier, J; Webster, M; Mishkin, M

    2000-01-01

    The ability of rhesus monkeys to master the rule for delayed nonmatching-to-sample (DNMS) has a protracted ontogenetic development, reaching adult levels of proficiency around 4 to 5 years of age (Bachevalier, 1990). To test the possibility that this slow development could be due, at least in part, to immaturity of the prefrontal component of a temporo-prefrontal circuit important for DNMS rule learning (Kowalska, Bachevalier, & Mishkin, 1991; Weinstein, Saunders, & Mishkin, 1988), monkeys with neonatal lesions of the inferior prefrontal convexity were compared on DNMS with both normal controls and animals given neonatal lesions of the medial temporal lobe. Consistent with our previous results (Bachevalier & Mishkin, 1994; Málková, Mishkin, & Bachevalier, 1995), the neonatal medial temporal lesions led to marked impairment in rule learning (as well as in recognition memory with long delays and list lengths) at both 3 months and 2 years of age. By contrast, the neonatal inferior convexity lesions yielded no impairment in rule-learning at 3 months and only a mild impairment at 2 years, a finding that also contrasts sharply with the marked effects of the same lesion made in adulthood. This pattern of sparing closely resembles the one found earlier after neonatal lesions to the cortical visual area TE (Bachevalier & Mishkin, 1994; Málková et al., 1995). The functional sparing at 3 months probably reflects the fact that the temporo-prefrontal circuit is nonfunctional at this early age, resulting in a total dependency on medial temporal contributions to rule learning. With further development, however, this circuit begins to provide a supplementary route for learning.

  18. Abstracting/Annotating. ERIC Processing Manual, Section VI.

    ERIC Educational Resources Information Center

    Brandhorst, Ted, Ed.

    Rules and guidelines are provided for the preparation of abstracts and annotations for documents and journal articles entering the ERIC database. Various types of abstracts are defined, including the Informative, Indicative, and mixed Informative-Indicative. Advice is given on how to select the abstract type appropriate for the particular…

  19. Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

    NASA Astrophysics Data System (ADS)

    Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

    2018-02-01

    Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

  20. A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks

    PubMed Central

    Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

    2015-01-01

    Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored

  1. A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.

    PubMed

    Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

    2015-08-01

    Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored

  2. Transfer Learning of Classification Rules for Biomarker Discovery and Verification from Molecular Profiling Studies

    PubMed Central

    Ganchev, Philip; Malehorn, David; Bigbee, William L.; Gopalakrishnan, Vanathi

    2013-01-01

    We present a novel framework for integrative biomarker discovery from related but separate data sets created in biomarker profiling studies. The framework takes prior knowledge in the form of interpretable, modular rules, and uses them during the learning of rules on a new data set. The framework consists of two methods of transfer of knowledge from source to target data: transfer of whole rules and transfer of rule structures. We evaluated the methods on three pairs of data sets: one genomic and two proteomic. We used standard measures of classification performance and three novel measures of amount of transfer. Preliminary evaluation shows that whole-rule transfer improves classification performance over using the target data alone, especially when there is more source data than target data. It also improves performance over using the union of the data sets. PMID:21571094

  3. Learning rules for spike timing-dependent plasticity depend on dendritic synapse location.

    PubMed

    Letzkus, Johannes J; Kampa, Björn M; Stuart, Greg J

    2006-10-11

    Previous studies focusing on the temporal rules governing changes in synaptic strength during spike timing-dependent synaptic plasticity (STDP) have paid little attention to the fact that synaptic inputs are distributed across complex dendritic trees. During STDP, propagation of action potentials (APs) back to the site of synaptic input is thought to trigger plasticity. However, in pyramidal neurons, backpropagation of single APs is decremental, whereas high-frequency bursts lead to generation of distal dendritic calcium spikes. This raises the question whether STDP learning rules depend on synapse location and firing mode. Here, we investigate this issue at synapses between layer 2/3 and layer 5 pyramidal neurons in somatosensory cortex. We find that low-frequency pairing of single APs at positive times leads to a distance-dependent shift to long-term depression (LTD) at distal inputs. At proximal sites, this LTD could be converted to long-term potentiation (LTP) by dendritic depolarizations suprathreshold for BAC-firing or by high-frequency AP bursts. During AP bursts, we observed a progressive, distance-dependent shift in the timing requirements for induction of LTP and LTD, such that distal synapses display novel timing rules: they potentiate when inputs are activated after burst onset (negative timing) but depress when activated before burst onset (positive timing). These findings could be explained by distance-dependent differences in the underlying dendritic voltage waveforms driving NMDA receptor activation during STDP induction. Our results suggest that synapse location within the dendritic tree is a crucial determinant of STDP, and that synapses undergo plasticity according to local rather than global learning rules.

  4. Distributed synaptic weights in a LIF neural network and learning rules

    NASA Astrophysics Data System (ADS)

    Perthame, Benoît; Salort, Delphine; Wainrib, Gilles

    2017-09-01

    Leaky integrate-and-fire (LIF) models are mean-field limits, with a large number of neurons, used to describe neural networks. We consider inhomogeneous networks structured by a connectivity parameter (strengths of the synaptic weights) with the effect of processing the input current with different intensities. We first study the properties of the network activity depending on the distribution of synaptic weights and in particular its discrimination capacity. Then, we consider simple learning rules and determine the synaptic weight distribution it generates. We outline the role of noise as a selection principle and the capacity to memorize a learned signal.

  5. Rules and mechanisms for efficient two-stage learning in neural circuits

    PubMed Central

    Teşileanu, Tiberiu; Ölveczky, Bence; Balasubramanian, Vijay

    2017-01-01

    Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in ‘tutor’ circuits (e.g., LMAN) should match plasticity mechanisms in ‘student’ circuits (e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning. DOI: http://dx.doi.org/10.7554/eLife.20944.001 PMID:28374674

  6. Learning "Rules" of Practice within the Context of the Practicum Triad: A Case Study of Learning to Teach

    ERIC Educational Resources Information Center

    Chalies, Sebastien; Escalie, Guillaume; Stefano, Bertone; Clarke, Anthony

    2012-01-01

    This case study sought to determine the professional development circumstances in which a preservice teacher learned rules of practice (Wittgenstein, 1996) on practicum while interacting with a cooperating teacher and university supervisor. Borrowing from a theoretical conceptualization of teacher professional development based on the postulates…

  7. Visual perceptual learning by operant conditioning training follows rules of contingency.

    PubMed

    Kim, Dongho; Seitz, Aaron R; Watanabe, Takeo

    2015-01-01

    Visual perceptual learning (VPL) can occur as a result of a repetitive stimulus-reward pairing in the absence of any task. This suggests that rules that guide Conditioning, such as stimulus-reward contingency (e.g. that stimulus predicts the likelihood of reward), may also guide the formation of VPL. To address this question, we trained subjects with an operant conditioning task in which there were contingencies between the response to one of three orientations and the presence of reward. Results showed that VPL only occurred for positive contingencies, but not for neutral or negative contingencies. These results suggest that the formation of VPL is influenced by similar rules that guide the process of Conditioning.

  8. Visual perceptual learning by operant conditioning training follows rules of contingency

    PubMed Central

    Kim, Dongho; Seitz, Aaron R; Watanabe, Takeo

    2015-01-01

    Visual perceptual learning (VPL) can occur as a result of a repetitive stimulus-reward pairing in the absence of any task. This suggests that rules that guide Conditioning, such as stimulus-reward contingency (e.g. that stimulus predicts the likelihood of reward), may also guide the formation of VPL. To address this question, we trained subjects with an operant conditioning task in which there were contingencies between the response to one of three orientations and the presence of reward. Results showed that VPL only occurred for positive contingencies, but not for neutral or negative contingencies. These results suggest that the formation of VPL is influenced by similar rules that guide the process of Conditioning. PMID:26028984

  9. Innovation Abstracts, Volume XV, 1993.

    ERIC Educational Resources Information Center

    Roueche, Suanne D., Ed.

    1993-01-01

    This volume of 30 one- to two-page abstracts from 1993 highlights a variety of innovative approaches to teaching and learning in the community college. Topics covered in the abstracts include: (1) role-playing to encourage critical thinking; (2) team learning techniques to cultivate business skills; (3) librarian-instructor partnerships to create…

  10. Evolving learning rules and emergence of cooperation in spatial prisoner's dilemma.

    PubMed

    Moyano, Luis G; Sánchez, Angel

    2009-07-07

    In the evolutionary Prisoner's dilemma (PD) game, agents play with each other and update their strategies in every generation according to some microscopic dynamical rule. In its spatial version, agents do not play with every other but, instead, interact only with their neighbours, thus mimicking the existing of a social or contact network that defines who interacts with whom. In this work, we explore evolutionary, spatial PD systems consisting of two types of agents, each with a certain update (reproduction, learning) rule. We investigate two different scenarios: in the first case, update rules remain fixed for the entire evolution of the system; in the second case, agents update both strategy and update rule in every generation. We show that in a well-mixed population the evolutionary outcome is always full defection. We subsequently focus on two-strategy competition with nearest-neighbour interactions on the contact network and synchronised update of strategies. Our results show that, for an important range of the parameters of the game, the final state of the system is largely different from that arising from the usual setup of a single, fixed dynamical rule. Furthermore, the results are also very different if update rules are fixed or evolve with the strategies. In these respect, we have studied representative update rules, finding that some of them may become extinct while others prevail. We describe the new and rich variety of final outcomes that arise from this co-evolutionary dynamics. We include examples of other neighbourhoods and asynchronous updating that confirm the robustness of our conclusions. Our results pave the way to an evolutionary rationale for modelling social interactions through game theory with a preferred set of update rules.

  11. Dog Is a Dog Is a Dog: Infant Rule Learning Is Not Specific to Language

    ERIC Educational Resources Information Center

    Saffran, Jenny R.; Pollak, Seth D.; Seibel, Rebecca L.; Shkolnik, Anna

    2007-01-01

    Human infants possess powerful learning mechanisms used for the acquisition of language. To what extent are these mechanisms domain specific? One well-known infant language learning mechanism is the ability to detect and generalize rule-like similarity patterns, such as ABA or ABB [Marcus, G. F., Vijayan, S., Rao, S. B., & Vishton, P. M. (1999).…

  12. Teaching with Concrete and Abstract Visual Representations: Effects on Students' Problem Solving, Problem Representations, and Learning Perceptions

    ERIC Educational Resources Information Center

    Moreno, Roxana; Ozogul, Gamze; Reisslein, Martin

    2011-01-01

    In 3 experiments, we examined the effects of using concrete and/or abstract visual problem representations during instruction on students' problem-solving practice, near transfer, problem representations, and learning perceptions. In Experiments 1 and 2, novice students learned about electrical circuit analysis with an instructional program that…

  13. Learning the rules of the rock-paper-scissors game: chimpanzees versus children.

    PubMed

    Gao, Jie; Su, Yanjie; Tomonaga, Masaki; Matsuzawa, Tetsuro

    2018-01-01

    The present study aimed to investigate whether chimpanzees (Pan troglodytes) could learn a transverse pattern by being trained in the rules of the rock-paper-scissors game in which "paper" beats "rock," "rock" beats "scissors," and "scissors" beats "paper." Additionally, this study compared the learning processes between chimpanzees and children. Seven chimpanzees were tested using a computer-controlled task. They were trained to choose the stronger of two options according to the game rules. The chimpanzees first engaged in the paper-rock sessions until they reached the learning criterion. Subsequently, they engaged in the rock-scissors and scissors-paper sessions, before progressing to sessions with all three pairs mixed. Five of the seven chimpanzees completed training after a mean of 307 sessions, which indicates that they learned the circular pattern. The chimpanzees required more scissors-paper sessions (14.29 ± 6.89), the third learnt pair, than paper-rock (1.71 ± 0.18) and rock-scissors (3.14 ± 0.70) sessions, suggesting they had difficulty finalizing the circularity. The chimpanzees then received generalization tests using new stimuli, which they learned quickly. A similar procedure was performed with children (35-71 months, n = 38) who needed the same number of trials for all three pairs during single-paired sessions. Their accuracy during the mixed-pair sessions improved with age and was better than chance from 50 months of age, which indicates that the ability to solve the transverse patterning problem might develop at around 4 years of age. The present findings show that chimpanzees were able to learn the task but had difficulties with circularity, whereas children learned the task more easily and developed the relevant ability at approximately 4 years of age. Furthermore, the chimpanzees' performance during the mixed-pair sessions was similar to that of 4-year-old children during the corresponding stage of training.

  14. Moral empiricism and the bias for act-based rules.

    PubMed

    Ayars, Alisabeth; Nichols, Shaun

    2017-10-01

    Previous studies on rule learning show a bias in favor of act-based rules, which prohibit intentionally producing an outcome but not merely allowing the outcome. Nichols, Kumar, Lopez, Ayars, and Chan (2016) found that exposure to a single sample violation in which an agent intentionally causes the outcome was sufficient for participants to infer that the rule was act-based. One explanation is that people have an innate bias to think rules are act-based. We suggest an alternative empiricist account: since most rules that people learn are act-based, people form an overhypothesis (Goodman, 1955) that rules are typically act-based. We report three studies that indicate that people can use information about violations to form overhypotheses about rules. In study 1, participants learned either three "consequence-based" rules that prohibited allowing an outcome or three "act-based" rules that prohibiting producing the outcome; in a subsequent learning task, we found that participants who had learned three consequence-based rules were more likely to think that the new rule prohibited allowing an outcome. In study 2, we presented participants with either 1 consequence-based rule or 3 consequence-based rules, and we found that those exposed to 3 such rules were more likely to think that a new rule was also consequence based. Thus, in both studies, it seems that learning 3 consequence-based rules generates an overhypothesis to expect new rules to be consequence-based. In a final study, we used a more subtle manipulation. We exposed participants to examples act-based or accident-based (strict liability) laws and then had them learn a novel rule. We found that participants who were exposed to the accident-based laws were more likely to think a new rule was accident-based. The fact that participants' bias for act-based rules can be shaped by evidence from other rules supports the idea that the bias for act-based rules might be acquired as an overhypothesis from the

  15. Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure.

    PubMed

    Lustgarten, Jonathan Lyle; Balasubramanian, Jeya Balaji; Visweswaran, Shyam; Gopalakrishnan, Vanathi

    2017-03-01

    The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene

  16. Analogical transfer as guided by an abstraction process: the case of learning by doing in text editing.

    PubMed

    Sander, E; Richard, J F

    1997-11-01

    The authors proposed that not only are the first attempts to solve a problem made by analogy but also that progress in learning can be guided by referring to more abstract knowledge, which affords new possibilities. Two experiments investigated this view in a situation of learning how to use a text editor. Experiments 1A to 1C identified the knowledge associated with 3 domains hypothesized as sources of transfer at increasing levels of abstraction (typewriting, writing in general, manipulating objects). Experiment 2 tested whether participants first use their knowledge about typewriting, then about writing in general, and then about manipulating objects. The data showed that the order of acquisition of text editor functions appeared to be strongly related to this hierarchy, supporting the idea that part of learning consists of discovering properties of objects by accessing increasingly general domains.

  17. Rules and construction effects in learning the argument structure of verbs.

    PubMed

    Demuth, Katherine; Machobane, Malillo; Moloi, Francina

    2003-11-01

    Theorists of language acquisition have long debated the means by which children learn the argument structure of verbs (e.g. Bowerman, 1974, 1990; Pinker, 1984, 1989; Tomasello, 1992). Central to this controversy has been the possible role of verb semantics, especially in learning which verbs undergo dative-shift alternation in languages like English. The learning problem is somewhat simplified in Bantu double object constructions, where all applicative verbs show the same order of postverbal objects. However, Bantu languages differ as to what that order is, some placing the benefactive argument first, and others placing the animate argument first. Learning the language-specific word-order restrictions on Bantu double object applicative constructions is therefore more akin to setting a parameter (cf. Hyams, 1986). This study examined 100 three- to eight-year-old children's knowledge of word order restrictions in Sesotho double object applicatives. Performance on forced choice elicited production tasks found that four-year-olds showed evidence of rule learning, although eight-year-olds had not yet attained adult levels of performance. Further investigation found lexical construction effects for three-year-olds. These findings suggest that learning the argument structure of verbs, even when lexical semantics is not involved, may be more sensitive to lexical construction effects than previously thought.

  18. A Burst-Based “Hebbian” Learning Rule at Retinogeniculate Synapses Links Retinal Waves to Activity-Dependent Refinement

    PubMed Central

    Butts, Daniel A; Kanold, Patrick O; Shatz, Carla J

    2007-01-01

    Patterned spontaneous activity in the developing retina is necessary to drive synaptic refinement in the lateral geniculate nucleus (LGN). Using perforated patch recordings from neurons in LGN slices during the period of eye segregation, we examine how such burst-based activity can instruct this refinement. Retinogeniculate synapses have a novel learning rule that depends on the latencies between pre- and postsynaptic bursts on the order of one second: coincident bursts produce long-lasting synaptic enhancement, whereas non-overlapping bursts produce mild synaptic weakening. It is consistent with “Hebbian” development thought to exist at this synapse, and we demonstrate computationally that such a rule can robustly use retinal waves to drive eye segregation and retinotopic refinement. Thus, by measuring plasticity induced by natural activity patterns, synaptic learning rules can be linked directly to their larger role in instructing the patterning of neural connectivity. PMID:17341130

  19. Effects of Visual and Verbal Stimuli on Children's Learning of Concrete and Abstract Prose.

    ERIC Educational Resources Information Center

    Hannafin, Michael J.; Carey, James O.

    A total of 152 fourth grade students participated in a study examining the effects of visual-only, verbal-only, and combined audiovisual prose presentations and different elaboration strategy conditions on student learning of abstract and concrete prose. The students saw and/or heard a short animated story, during which they were instructed to…

  20. Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

    PubMed

    Burbank, Kendra S

    2015-12-01

    The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.

  1. Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons

    PubMed Central

    Burbank, Kendra S.

    2015-01-01

    The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field’s Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks. PMID:26633645

  2. Identifying relevant data for a biological database: handcrafted rules versus machine learning.

    PubMed

    Sehgal, Aditya Kumar; Das, Sanmay; Noto, Keith; Saier, Milton H; Elkan, Charles

    2011-01-01

    With well over 1,000 specialized biological databases in use today, the task of automatically identifying novel, relevant data for such databases is increasingly important. In this paper, we describe practical machine learning approaches for identifying MEDLINE documents and Swiss-Prot/TrEMBL protein records, for incorporation into a specialized biological database of transport proteins named TCDB. We show that both learning approaches outperform rules created by hand by a human expert. As one of the first case studies involving two different approaches to updating a deployed database, both the methods compared and the results will be of interest to curators of many specialized databases.

  3. Preparing for the Worst: Psychological Excellence of First Responders - A Katrina Lessons Learned Study

    DTIC Science & Technology

    2008-01-01

    cases on human cognition and performance. For instance, when you learn to fly an airplane, you will be instructed to use a simple rule to avoid...Existing Training Technologies; First Responders; Katrina; Lesson Learned 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER... student . Based in Maryland, the training institute prepares first responders using online learning courses or training exercises. Such topics

  4. Some aspects of using new techniques of teaching/learning in education in optics (Abstract only)

    NASA Astrophysics Data System (ADS)

    Suchanska, Malgorzata

    2003-11-01

    The deep learning in Optics can be encouraged by stimulating and considerate teaching. It means that teacher should demonstrate his/her personal commitment to the subject and stress its meaning, relevance and importance to the students. It is also important to allow students to be creative in solving problems and in interpretation of its contents. In order to help the students to become more creative persons it is necessary to enhance the learning process of modern knowledge in Optics, to design and conduct experiments, stimulate passions and interests, allow an access to the e-learning system (Internet) and introduce the psychological training (creativity, communication, lateral thinking etc.) (Abstract only available)

  5. Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients

    PubMed Central

    2013-01-01

    Background Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting. Results Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging. Conclusions The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four

  6. Logic Learning Machine creates explicit and stable rules stratifying neuroblastoma patients.

    PubMed

    Cangelosi, Davide; Blengio, Fabiola; Versteeg, Rogier; Eggert, Angelika; Garaventa, Alberto; Gambini, Claudio; Conte, Massimo; Eva, Alessandra; Muselli, Marco; Varesio, Luigi

    2013-01-01

    Neuroblastoma is the most common pediatric solid tumor. About fifty percent of high risk patients die despite treatment making the exploration of new and more effective strategies for improving stratification mandatory. Hypoxia is a condition of low oxygen tension occurring in poorly vascularized areas of the tumor associated with poor prognosis. We had previously defined a robust gene expression signature measuring the hypoxic component of neuroblastoma tumors (NB-hypo) which is a molecular risk factor. We wanted to develop a prognostic classifier of neuroblastoma patients' outcome blending existing knowledge on clinical and molecular risk factors with the prognostic NB-hypo signature. Furthermore, we were interested in classifiers outputting explicit rules that could be easily translated into the clinical setting. Shadow Clustering (SC) technique, which leads to final models called Logic Learning Machine (LLM), exhibits a good accuracy and promises to fulfill the aims of the work. We utilized this algorithm to classify NB-patients on the bases of the following risk factors: Age at diagnosis, INSS stage, MYCN amplification and NB-hypo. The algorithm generated explicit classification rules in good agreement with existing clinical knowledge. Through an iterative procedure we identified and removed from the dataset those examples which caused instability in the rules. This workflow generated a stable classifier very accurate in predicting good and poor outcome patients. The good performance of the classifier was validated in an independent dataset. NB-hypo was an important component of the rules with a strength similar to that of tumor staging. The novelty of our work is to identify stability, explicit rules and blending of molecular and clinical risk factors as the key features to generate classification rules for NB patients to be conveyed to the clinic and to be used to design new therapies. We derived, through LLM, a set of four stable rules identifying a new

  7. An Eye-Tracking Study of Learning from Science Text with Concrete and Abstract Illustrations

    ERIC Educational Resources Information Center

    Mason, Lucia; Pluchino, Patrik; Tornatora, Maria Caterina; Ariasi, Nicola

    2013-01-01

    This study investigated the online process of reading and the offline learning from an illustrated science text. The authors examined the effects of using a concrete or abstract picture to illustrate a text and adopted eye-tracking methodology to trace text and picture processing. They randomly assigned 59 eleventh-grade students to 3 reading…

  8. Learning Abstract Physical Concepts from Experience: Design and Use of an RC Circuit

    NASA Astrophysics Data System (ADS)

    Parra, Alfredo; Ordenes, Jorge; de la Fuente, Milton

    2018-05-01

    Science learning for undergraduate students requires grasping a great number of theoretical concepts in a rather short time. In our experience, this is especially difficult when students are required to simultaneously use abstract concepts, mathematical reasoning, and graphical analysis, such as occurs when learning about RC circuits. We present a simple experimental model in this work that allows students to easily design, build, and analyze RC circuits, thus providing an opportunity to test personal ideas, build graphical descriptions, and explore the meaning of the respective mathematical models, ultimately gaining a better grasp of the concepts involved. The result suggests that the simple setup indeed helps untrained students to visualize the essential points of this kind of circuit.

  9. Sensitivity to linguistic register in 20-month-olds: Understanding the register-listener relationship and its abstract rules

    PubMed Central

    Kobayashi, Tessei; Itakura, Shoji

    2018-01-01

    Linguistic register reflects changes in speech that depend on the situation, especially the status of listeners and listener-speaker relationships. Following the sociolinguistic rules of register is essential in establishing and maintaining social interactions. Recent research suggests that children over 3 years of age can understand appropriate register-listener relationships as well as the fact that people change register depending on their listeners. However, given previous findings that infants under 2 years of age have already formed both social and speech categories, it may be possible that even younger children can also understand appropriate register-listener relationships. The present study used Infant-Directed Speech (IDS) and formal Adult-Directed Speech (ADS) to examine whether 20-month-old toddlers can understand register-listener relationships. In Experiment 1, we used a violation-of-expectation method to examine whether 20-month-olds understand the individual associations between linguistic registers and listeners. Results showed that the toddlers looked significantly longer at a scene in which the adult was talked to in IDS than when the infant was talked to in IDS. In contrast, there was no difference when the adult and the infant were talked to in formal ADS. In Experiments 2 and 3, we used a habituation switch paradigm to examine whether 20-month-olds understand the abstract rule that a change of register depends on listeners rather than on speakers. Results showed that the toddlers looked significantly longer at the scene where the register rule was violated. The present findings provide new evidence that even 20-month-olds already understand that people change their way of speaking based on listeners, although their understanding of individual register-listener relationships is immature. PMID:29630608

  10. Cerebellar contribution to higher and lower order rule learning and cognitive flexibility in mice.

    PubMed

    Dickson, P E; Cairns, J; Goldowitz, D; Mittleman, G

    2017-03-14

    Cognitive flexibility has traditionally been considered a frontal lobe function. However, converging evidence suggests involvement of a larger brain circuit which includes the cerebellum. Reciprocal pathways connecting the cerebellum to the prefrontal cortex provide a biological substrate through which the cerebellum may modulate higher cognitive functions, and it has been observed that cognitive inflexibility and cerebellar pathology co-occur in psychiatric disorders (e.g., autism, schizophrenia, addiction). However, the degree to which the cerebellum contributes to distinct forms of cognitive flexibility and rule learning is unknown. We tested lurcher↔wildtype aggregation chimeras which lose 0-100% of cerebellar Purkinje cells during development on a touchscreen-mediated attentional set-shifting task to assess the contribution of the cerebellum to higher and lower order rule learning and cognitive flexibility. Purkinje cells, the sole output of the cerebellar cortex, ranged from 0 to 108,390 in tested mice. Reversal learning and extradimensional set-shifting were impaired in mice with⩾95% Purkinje cell loss. Cognitive deficits were unrelated to motor deficits in ataxic mice. Acquisition of a simple visual discrimination and an attentional-set were unrelated to Purkinje cells. A positive relationship was observed between Purkinje cells and errors when exemplars from a novel, non-relevant dimension were introduced. Collectively, these data suggest that the cerebellum contributes to higher order cognitive flexibility, lower order cognitive flexibility, and attention to novel stimuli, but not the acquisition of higher and lower order rules. These data indicate that the cerebellar pathology observed in psychiatric disorders may underlie deficits involving cognitive flexibility and attention to novel stimuli. Copyright © 2016. Published by Elsevier Ltd.

  11. Applying cognitive developmental psychology to middle school physics learning: The rule assessment method

    NASA Astrophysics Data System (ADS)

    Hallinen, Nicole R.; Chi, Min; Chin, Doris B.; Prempeh, Joe; Blair, Kristen P.; Schwartz, Daniel L.

    2013-01-01

    Cognitive developmental psychology often describes children's growing qualitative understanding of the physical world. Physics educators may be able to use the relevant methods to advantage for characterizing changes in students' qualitative reasoning. Siegler developed the "rule assessment" method for characterizing levels of qualitative understanding for two factor situations (e.g., volume and mass for density). The method assigns children to rule levels that correspond to the degree they notice and coordinate the two factors. Here, we provide a brief tutorial plus a demonstration of how we have used this method to evaluate instructional outcomes with middle-school students who learned about torque, projectile motion, and collisions using different instructional methods with simulations.

  12. Innovation Abstracts: Volume XI, Numbers 1-30.

    ERIC Educational Resources Information Center

    Roueche, Suanne D., Ed.

    1989-01-01

    This series of one- to two-page abstracts highlights a variety of innovative approaches to teaching and learning in the community college. Topics covered in the abstracts include: (1) cooperative planning for institutional excellence; (2) rewarding scholarship among community college faculty; (3) in-class debates as a learning strategy; (4)…

  13. Modulated Hebb-Oja learning rule--a method for principal subspace analysis.

    PubMed

    Jankovic, Marko V; Ogawa, Hidemitsu

    2006-03-01

    This paper presents analysis of the recently proposed modulated Hebb-Oja (MHO) method that performs linear mapping to a lower-dimensional subspace. Principal component subspace is the method that will be analyzed. Comparing to some other well-known methods for yielding principal component subspace (e.g., Oja's Subspace Learning Algorithm), the proposed method has one feature that could be seen as desirable from the biological point of view--synaptic efficacy learning rule does not need the explicit information about the value of the other efficacies to make individual efficacy modification. Also, the simplicity of the "neural circuits" that perform global computations and a fact that their number does not depend on the number of input and output neurons, could be seen as good features of the proposed method.

  14. The use of misclassification costs to learn rule-based decision support models for cost-effective hospital admission strategies.

    PubMed

    Ambrosino, R; Buchanan, B G; Cooper, G F; Fine, M J

    1995-01-01

    Cost-effective health care is at the forefront of today's important health-related issues. A research team at the University of Pittsburgh has been interested in lowering the cost of medical care by attempting to define a subset of patients with community-acquire pneumonia for whom outpatient therapy is appropriate and safe. Sensitivity and specificity requirements for this domain make it difficult to use rule-based learning algorithms with standard measures of performance based on accuracy. This paper describes the use of misclassification costs to assist a rule-based machine-learning program in deriving a decision-support aid for choosing outpatient therapy for patients with community-acquired pneumonia.

  15. Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations

    PubMed Central

    Kaplan, Jonas T.; Man, Kingson; Greening, Steven G.

    2015-01-01

    Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC), and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application. PMID:25859202

  16. Abstract Interpreters for Free

    NASA Astrophysics Data System (ADS)

    Might, Matthew

    In small-step abstract interpretations, the concrete and abstract semantics bear an uncanny resemblance. In this work, we present an analysis-design methodology that both explains and exploits that resemblance. Specifically, we present a two-step method to convert a small-step concrete semantics into a family of sound, computable abstract interpretations. The first step re-factors the concrete state-space to eliminate recursive structure; this refactoring of the state-space simultaneously determines a store-passing-style transformation on the underlying concrete semantics. The second step uses inference rules to generate an abstract state-space and a Galois connection simultaneously. The Galois connection allows the calculation of the "optimal" abstract interpretation. The two-step process is unambiguous, but nondeterministic: at each step, analysis designers face choices. Some of these choices ultimately influence properties such as flow-, field- and context-sensitivity. Thus, under the method, we can give the emergence of these properties a graph-theoretic characterization. To illustrate the method, we systematically abstract the continuation-passing style lambda calculus to arrive at two distinct families of analyses. The first is the well-known k-CFA family of analyses. The second consists of novel "environment-centric" abstract interpretations, none of which appear in the literature on static analysis of higher-order programs.

  17. Is It Really Abstract?

    ERIC Educational Resources Information Center

    Kernan, Christine

    2011-01-01

    For this author, one of the most enjoyable aspects of teaching elementary art is the willingness of students to embrace the different styles of art introduced to them. In this article, she describes a project that allows upper-elementary students to learn about abstract art and the lives of some of the master abstract artists, implement the idea…

  18. Incidental Learning of Melodic Structure of North Indian Music

    ERIC Educational Resources Information Center

    Rohrmeier, Martin; Widdess, Richard

    2017-01-01

    Musical knowledge is largely implicit. It is acquired without awareness of its complex rules, through interaction with a large number of samples during musical enculturation. Whereas several studies explored implicit learning of mostly abstract and less ecologically valid features of Western music, very little work has been done with respect to…

  19. A new modulated Hebbian learning rule--biologically plausible method for local computation of a principal subspace.

    PubMed

    Jankovic, Marko; Ogawa, Hidemitsu

    2003-08-01

    This paper presents one possible implementation of a transformation that performs linear mapping to a lower-dimensional subspace. Principal component subspace will be the one that will be analyzed. Idea implemented in this paper represents generalization of the recently proposed infinity OH neural method for principal component extraction. The calculations in the newly proposed method are performed locally--a feature which is usually considered as desirable from the biological point of view. Comparing to some other wellknown methods, proposed synaptic efficacy learning rule requires less information about the value of the other efficacies to make single efficacy modification. Synaptic efficacies are modified by implementation of Modulated Hebb-type (MH) learning rule. Slightly modified MH algorithm named Modulated Hebb Oja (MHO) algorithm, will be also introduced. Structural similarity of the proposed network with part of the retinal circuit will be presented, too.

  20. Neural networks supporting switching, hypothesis testing, and rule application

    PubMed Central

    Liu, Zhiya; Braunlich, Kurt; Wehe, Hillary S.; Seger, Carol A.

    2015-01-01

    We identified dynamic changes in recruitment of neural connectivity networks across three phases of a flexible rule learning and set-shifting task similar to the Wisconsin Card Sort Task: switching, rule learning via hypothesis testing, and rule application. During fMRI scanning, subjects viewed pairs of stimuli that differed across four dimensions (letter, color, size, screen location), chose one stimulus, and received feedback. Subjects were informed that the correct choice was determined by a simple unidimensional rule, for example “choose the blue letter.” Once each rule had been learned and correctly applied for 4-7 trials, subjects were cued via either negative feedback or visual cues to switch to learning a new rule. Task performance was divided into three phases: Switching (first trial after receiving the switch cue), hypothesis testing (subsequent trials through the last error trial), and rule application (correct responding after the rule was learned). We used both univariate analysis to characterize activity occurring within specific regions of the brain, and a multivariate method, constrained principal component analysis for fMRI (fMRI-CPCA), to investigate how distributed regions coordinate to subserve different processes. As hypothesized, switching was subserved by a limbic network including the ventral striatum, thalamus, and parahippocampal gyrus, in conjunction with cortical salience network regions including the anterior cingulate and frontoinsular cortex. Activity in the ventral striatum was associated with switching regardless of how switching was cued; visually cued shifts were associated with additional visual cortical activity. After switching, as subjects moved into the hypothesis testing phase, a broad fronto-parietal-striatal network (associated with the cognitive control, dorsal attention, and salience networks) increased in activity. This network was sensitive to rule learning speed, with greater extended activity for the slowest

  1. Neural networks supporting switching, hypothesis testing, and rule application.

    PubMed

    Liu, Zhiya; Braunlich, Kurt; Wehe, Hillary S; Seger, Carol A

    2015-10-01

    We identified dynamic changes in recruitment of neural connectivity networks across three phases of a flexible rule learning and set-shifting task similar to the Wisconsin Card Sort Task: switching, rule learning via hypothesis testing, and rule application. During fMRI scanning, subjects viewed pairs of stimuli that differed across four dimensions (letter, color, size, screen location), chose one stimulus, and received feedback. Subjects were informed that the correct choice was determined by a simple unidimensional rule, for example "choose the blue letter". Once each rule had been learned and correctly applied for 4-7 trials, subjects were cued via either negative feedback or visual cues to switch to learning a new rule. Task performance was divided into three phases: Switching (first trial after receiving the switch cue), hypothesis testing (subsequent trials through the last error trial), and rule application (correct responding after the rule was learned). We used both univariate analysis to characterize activity occurring within specific regions of the brain, and a multivariate method, constrained principal component analysis for fMRI (fMRI-CPCA), to investigate how distributed regions coordinate to subserve different processes. As hypothesized, switching was subserved by a limbic network including the ventral striatum, thalamus, and parahippocampal gyrus, in conjunction with cortical salience network regions including the anterior cingulate and frontoinsular cortex. Activity in the ventral striatum was associated with switching regardless of how switching was cued; visually cued shifts were associated with additional visual cortical activity. After switching, as subjects moved into the hypothesis testing phase, a broad fronto-parietal-striatal network (associated with the cognitive control, dorsal attention, and salience networks) increased in activity. This network was sensitive to rule learning speed, with greater extended activity for the slowest

  2. Biased Feedback in Spatial Recall Yields a Violation of Delta Rule Learning

    PubMed Central

    Lipinski, John; Spencer, John P.; Samuelson, Larissa K.

    2010-01-01

    This study investigates whether inductive processes influencing spatial memory performance generalize to supervised learning scenarios with differential feedback. After providing a location memory response in a spatial recall task, participants received visual feedback showing the target location. In critical blocks, feedback was systematically biased either 4° towards the vertical axis (Towards condition) or 4° further away from the vertical axis (Away condition). Results showed that the weaker teaching signal (i.e., a smaller difference between the remembered location and the feedback location) in the Away condition produced a stronger experience-dependent change over blocks than in the Towards condition. This violates delta rule learning. Subsequent simulations of the Dynamic Field Theory of spatial cognition provide a theoretically unified account of these results. PMID:20702881

  3. Biased feedback in spatial recall yields a violation of delta rule learning.

    PubMed

    Lipinski, John; Spencer, John P; Samuelson, Larissa K

    2010-08-01

    This study investigates whether inductive processes influencing spatial memory performance generalize to supervised learning scenarios with differential feedback. After providing a location memory response in a spatial recall task, participants received visual feedback showing the target location. In critical blocks, feedback was systematically biased either 4 degrees toward the vertical axis (toward condition) or 4 degrees farther away from the vertical axis (away condition). Results showed that the weaker teaching signal (i.e., a smaller difference between the remembered location and the feedback location) produced a stronger experience-dependent change over blocks in the away condition than in the toward condition. This violates delta rule learning. Subsequent simulations of the dynamic field theory of spatial cognition provide a theoretically unified account of these results.

  4. Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules

    PubMed Central

    Frémaux, Nicolas; Gerstner, Wulfram

    2016-01-01

    Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide “when” to create new memories in response to a flow of sensory stimuli. In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discuss some experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity. We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators. PMID:26834568

  5. Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules.

    PubMed

    Frémaux, Nicolas; Gerstner, Wulfram

    2015-01-01

    Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide "when" to create new memories in response to a flow of sensory stimuli. In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discuss some experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity. We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators.

  6. Margined winner-take-all: New learning rule for pattern recognition.

    PubMed

    Fukushima, Kunihiko

    2018-01-01

    The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector) is used for this purpose. This paper proposes a new learning rule called margined Winner-Take-All (mWTA) for training the deepest layer. Every time when a training pattern is presented during the learning, if the result of recognition by WTA (Winner-Take-All) is an error, a new cell is generated in the deepest layer. Here we put a certain amount of margin to the WTA. In other words, only during the learning, a certain amount of handicap is given to cells of classes other than that of the training vector, and the winner is chosen under this handicap. By introducing the margin to the WTA, we can generate a compact set of cells, with which a high recognition rate can be obtained with a small computational cost. The ability of this mWTA is demonstrated by computer simulation. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. CACNA1C gene regulates behavioral strategies in operant rule learning.

    PubMed

    Koppe, Georgia; Mallien, Anne Stephanie; Berger, Stefan; Bartsch, Dusan; Gass, Peter; Vollmayr, Barbara; Durstewitz, Daniel

    2017-06-01

    Behavioral experiments are usually designed to tap into a specific cognitive function, but animals may solve a given task through a variety of different and individual behavioral strategies, some of them not foreseen by the experimenter. Animal learning may therefore be seen more as the process of selecting among, and adapting, potential behavioral policies, rather than mere strengthening of associative links. Calcium influx through high-voltage-gated Ca2+ channels is central to synaptic plasticity, and altered expression of Cav1.2 channels and the CACNA1C gene have been associated with severe learning deficits and psychiatric disorders. Given this, we were interested in how specifically a selective functional ablation of the Cacna1c gene would modulate the learning process. Using a detailed, individual-level analysis of learning on an operant cue discrimination task in terms of behavioral strategies, combined with Bayesian selection among computational models estimated from the empirical data, we show that a Cacna1c knockout does not impair learning in general but has a much more specific effect: the majority of Cacna1c knockout mice still managed to increase reward feedback across trials but did so by adapting an outcome-based strategy, while the majority of matched controls adopted the experimentally intended cue-association rule. Our results thus point to a quite specific role of a single gene in learning and highlight that much more mechanistic insight could be gained by examining response patterns in terms of a larger repertoire of potential behavioral strategies. The results may also have clinical implications for treating psychiatric disorders.

  8. Innovation Abstracts: Volume XII, Numbers 1-30, 1990.

    ERIC Educational Resources Information Center

    Roueche, Susanne D., Ed.

    1990-01-01

    This series of one- to two-page abstracts highlights a variety of innovative approaches to teaching and learning in the community college. Topics covered in the abstracts include: (1) academic partnerships pairing "high-risk" students with a concerned faculty member, counselor, or administrator; (2) teacher-to-teacher learning partnerships; (3)…

  9. Innovation Abstracts; Volume XIV, 1992.

    ERIC Educational Resources Information Center

    Roueche, Suanne D., Ed.

    1992-01-01

    This series of 30 one- to two-page abstracts covering 1992 highlights a variety of innovative approaches to teaching and learning in the community college. Topics covered in the abstracts include: (1) faculty recognition and orientation; (2) the Amado M. Pena, Jr., Scholarship Program; (3) innovative teaching techniques, with individual abstracts…

  10. Rule-Based Statistical Calculations on a Database Abstract.

    DTIC Science & Technology

    1983-06-01

    quadruples 17 L.6.6. Our methds ~ in distributed systems 17 L.6.7. Easy extensions 17 17. The datibms abstract as a database 17 17.1.w S orae mu is 1.7.2...the largest item in the intersection of two sets cannot be any larger that the minima of the maxima of the two sets for some numeric attribute. On the...from "range analysis" of arbitrary numeric attributes. Suppose the length range of tankers is from 300 to 1000 feet and that of American ships 50 to

  11. Analysis of Rules for Islamic Inheritance Law in Indonesia Using Hybrid Rule Based Learning

    NASA Astrophysics Data System (ADS)

    Khosyi'ah, S.; Irfan, M.; Maylawati, D. S.; Mukhlas, O. S.

    2018-01-01

    Along with the development of human civilization in Indonesia, the changes and reform of Islamic inheritance law so as to conform to the conditions and culture cannot be denied. The distribution of inheritance in Indonesia can be done automatically by storing the rule of Islamic inheritance law in the expert system. In this study, we analyze the knowledge of experts in Islamic inheritance in Indonesia and represent it in the form of rules using rule-based Forward Chaining (FC) and Davis-Putman-Logemann-Loveland (DPLL) algorithms. By hybridizing FC and DPLL algorithms, the rules of Islamic inheritance law in Indonesia are clearly defined and measured. The rules were conceptually validated by some experts in Islamic laws and informatics. The results revealed that generally all rules were ready for use in an expert system.

  12. The Acquisition of Abstract Words by Young Infants

    ERIC Educational Resources Information Center

    Bergelson, Elika; Swingley, Daniel

    2013-01-01

    Young infants' learning of words for abstract concepts like "all gone" and "eat," in contrast to their learning of more concrete words like "apple" and "shoe," may follow a relatively protracted developmental course. We examined whether infants know such abstract words. Parents named one of two events shown in side-by-side videos while their…

  13. Aqui y Alla (Here and There) Information-Based Learning Corridors between Tennessee and Puerto Rico: The Five Golden Rules in Intercultural Education

    ERIC Educational Resources Information Center

    Mehra, Bharat; Allard, Suzie; Qayyum, M. Asim; Barclay-McLaughlin, Gina

    2008-01-01

    This article proposes five information-based Golden Rules in intercultural education that represent a holistic approach to creating learning corridors across geographically dispersed academic communities. The Golden Rules are generated through qualitative analysis, grounded theory application, reflective practice, and critical research to…

  14. Innovation Abstracts, Volume XVI, 1994.

    ERIC Educational Resources Information Center

    Roueche, Suanne D., Ed.

    1994-01-01

    This volume of 30 one- to two-page abstracts highlights a variety of innovative approaches to teaching and learning in the community college. Topics covered in the abstracts include: (1) music in the biology classroom; (2) pairing English as a second language and freshman composition students in writing activities; (3) moot court exercises in…

  15. Habituation: a non-associative learning rule design for spiking neurons and an autonomous mobile robots implementation.

    PubMed

    Cyr, André; Boukadoum, Mounir

    2013-03-01

    This paper presents a novel bio-inspired habituation function for robots under control by an artificial spiking neural network. This non-associative learning rule is modelled at the synaptic level and validated through robotic behaviours in reaction to different stimuli patterns in a dynamical virtual 3D world. Habituation is minimally represented to show an attenuated response after exposure to and perception of persistent external stimuli. Based on current neurosciences research, the originality of this rule includes modulated response to variable frequencies of the captured stimuli. Filtering out repetitive data from the natural habituation mechanism has been demonstrated to be a key factor in the attention phenomenon, and inserting such a rule operating at multiple temporal dimensions of stimuli increases a robot's adaptive behaviours by ignoring broader contextual irrelevant information.

  16. On the fusion of tuning parameters of fuzzy rules and neural network

    NASA Astrophysics Data System (ADS)

    Mamuda, Mamman; Sathasivam, Saratha

    2017-08-01

    Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

  17. CACNA1C gene regulates behavioral strategies in operant rule learning

    PubMed Central

    Berger, Stefan; Bartsch, Dusan; Gass, Peter

    2017-01-01

    Behavioral experiments are usually designed to tap into a specific cognitive function, but animals may solve a given task through a variety of different and individual behavioral strategies, some of them not foreseen by the experimenter. Animal learning may therefore be seen more as the process of selecting among, and adapting, potential behavioral policies, rather than mere strengthening of associative links. Calcium influx through high-voltage-gated Ca2+ channels is central to synaptic plasticity, and altered expression of Cav1.2 channels and the CACNA1C gene have been associated with severe learning deficits and psychiatric disorders. Given this, we were interested in how specifically a selective functional ablation of the Cacna1c gene would modulate the learning process. Using a detailed, individual-level analysis of learning on an operant cue discrimination task in terms of behavioral strategies, combined with Bayesian selection among computational models estimated from the empirical data, we show that a Cacna1c knockout does not impair learning in general but has a much more specific effect: the majority of Cacna1c knockout mice still managed to increase reward feedback across trials but did so by adapting an outcome-based strategy, while the majority of matched controls adopted the experimentally intended cue-association rule. Our results thus point to a quite specific role of a single gene in learning and highlight that much more mechanistic insight could be gained by examining response patterns in terms of a larger repertoire of potential behavioral strategies. The results may also have clinical implications for treating psychiatric disorders. PMID:28604818

  18. Attentional effects on rule extraction and consolidation from speech

    PubMed Central

    López-Barroso, Diana; Cucurell, David; Rodríguez-Fornells, Antoni; de Diego-Balaguer, Ruth

    2016-01-01

    Incidental learning plays a crucial role in the initial phases of language acquisition. However the knowledge derived from implicit learning, which is based on prediction-based mechanisms, may become explicit. The role that attention plays in the formation of implicit and explicit knowledge of the learned material is unclear. In the present study, we investigated the role that attention plays in the acquisition of non-adjacent rule learning from speech. In addition, we also tested whether the amount of attention during learning changes the representation of the learned material after a 24 h delay containing sleep. For that, we developed an experiment run on two consecutive days consisting on the exposure to an artificial language that contained non-adjacent dependencies (rules) between words whereas different conditions were established to manipulate the amount of attention given to the rules (target and non-target conditions). Furthermore, we used both indirect and direct measures of learning that are more sensitive to implicit and explicit knowledge, respectively. Whereas the indirect measures indicated that learning of the rules occurred regardless of attention, more explicit judgments after learning showed differences in the type of learning reached under the two attention conditions. 24 hours later, indirect measures showed no further improvements during additional language exposure and explicit judgments indicated that only the information more robustly learned in the previous day, was consolidated. PMID:27031495

  19. A theory of local learning, the learning channel, and the optimality of backpropagation.

    PubMed

    Baldi, Pierre; Sadowski, Peter

    2016-11-01

    In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying the space of local learning rules is obtained by first specifying the nature of the local variables, and then the functional form that ties them together into each learning rule. Such a framework enables also the systematic discovery of new learning rules and exploration of relationships between learning rules and group symmetries. We study polynomial local learning rules stratified by their degree and analyze their behavior and capabilities in both linear and non-linear units and networks. Stacking local learning rules in deep feedforward networks leads to deep local learning. While deep local learning can learn interesting representations, it cannot learn complex input-output functions, even when targets are available for the top layer. Learning complex input-output functions requires local deep learning where target information is communicated to the deep layers through a backward learning channel. The nature of the communicated information about the targets and the structure of the learning channel partition the space of learning algorithms. For any learning algorithm, the capacity of the learning channel can be defined as the number of bits provided about the error gradient per weight, divided by the number of required operations per weight. We estimate the capacity associated with several learning algorithms and show that backpropagation outperforms them by simultaneously maximizing the information rate and minimizing the computational cost. This result is also shown to be true for recurrent networks, by unfolding them in time. The theory clarifies the concept of Hebbian learning, establishes the power and limitations of local learning rules

  20. Binary translation using peephole translation rules

    DOEpatents

    Bansal, Sorav; Aiken, Alex

    2010-05-04

    An efficient binary translator uses peephole translation rules to directly translate executable code from one instruction set to another. In a preferred embodiment, the translation rules are generated using superoptimization techniques that enable the translator to automatically learn translation rules for translating code from the source to target instruction set architecture.

  1. Learning to Learn about Uncertain Feedback

    ERIC Educational Resources Information Center

    Faraut, Mailys C. M.; Procyk, Emmanuel; Wilson, Charles R. E.

    2016-01-01

    Unexpected outcomes can reflect noise in the environment or a change in the current rules. We should ignore noise but shift strategy after rule changes. How we learn to do this is unclear, but one possibility is that it relies on learning to learn in uncertain environments. We propose that acquisition of latent task structure during learning to…

  2. Enabling Active Learning. Conference Programme and Abstracts of the Association for Learning Technology Conference (1st, Hull, England, United Kingdom, September 19-21, 1994).

    ERIC Educational Resources Information Center

    Heath, Simon, Ed.

    This program for the 1994 Association for Learning Technology Conference provides a conference schedule and summarizes the presentations of the discussion workshops, hands-on workshops, live demonstrations, and poster sessions. Abstracts of the following papers presented at the conference are included: "The Conceptualisation Cycle" (J.…

  3. Processing prosodic structure by adults with language-based learning disability.

    PubMed

    Bahl, Megha; Plante, Elena; Gerken, LouAnn

    2009-01-01

    Two experiments investigated the ability of adults with a history of language-based learning disability (hLLD) and their normal language (NL) peers to learn prosodic patterns of a novel language. Participants were exposed to stimuli from an artificial language and tested on items that required generalization of the stress patterns and the hierarchical principles of stress assignment that could be inferred from the input. In Study 1, the NL group successfully generalized the patterns of stress heard during familiarization, but failed to show generalization of the hierarchical principles. The hLLD group performed at chance for both types of generalization items. In Study 2, the intensity of stress elements was increased. The performance of the NL group improved whereas the hLLD groups' performance decreased on both types of generalization items. The results indicate that NL adults are able to successfully abstract the complex hierarchical rules of stress if the prosodic cues are made sufficiently salient, but this same task is difficult for adults with hLLD. The reader will be able to understand: (1) the difference in the ability of hLLD and NL adults to process stress assignment in an implicit learning context and (2) that typical adults can abstract complex hierarchical rules of stress assignment when provided with strong cues.

  4. Inter-synaptic learning of combination rules in a cortical network model

    PubMed Central

    Lavigne, Frédéric; Avnaïm, Francis; Dumercy, Laurent

    2014-01-01

    Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS) learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons. Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network. PMID:25221529

  5. Multistrategy learning: A case study

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Domingos, P.

    1996-12-31

    Two of the most popular approaches to induction are instance-based learning (IBL) and rule generation. Their strengths and weaknesses are largely complementary. IBL methods are able to identify small details in the instance space, but have trouble with attributes that are relevant in some parts of the space but not others. Conversely, rule induction methods may overlook small exception regions, but are able to select different attributes in different parts of the instance space. The two methods have been unified in the RISE algorithm. RISE views instances as maximally specific rules, forms more general rules by gradually clustering instances ofmore » the same class, and classifies a test example by letting the nearest rule win. This approach potentially combines the advantages of rule induction and IBL, and has indeed been observed to be more accurate than each on a large number of bench-mark datasets. However, it is important to determine if this performance is indeed due to the hypothesized advantages, and to define the situations in which RISE`s bias will and will not be preferable to those of the individual approaches. This abstract reports experiments to this end in artificial domains.« less

  6. Learning the Rules of the Game

    NASA Astrophysics Data System (ADS)

    Smith, Donald A.

    2018-03-01

    Games have often been used in the classroom to teach physics ideas and concepts, but there has been less published on games that can be used to teach scientific thinking. D. Maloney and M. Masters describe an activity in which students attempt to infer rules to a game from a history of moves, but the students don't actually play the game. Giving the list of moves allows the instructor to emphasize the important fact that nature usually gives us incomplete data sets, but it does make the activity less immersive. E. Kimmel suggested letting students attempt to figure out the rules to Reversi by playing it, but this game only has two players, which makes it difficult to apply in a classroom setting. Kimmel himself admits the choice of Reversi is somewhat arbitrary. There are games, however, that are designed to make the process of figuring out the rules an integral aspect of play. These games involve more people and require only a deck or two of cards. I present here an activity constructed around the card game Mao, which can be used to help students recognize aspects of scientific thinking. The game is particularly good at illustrating the importance of falsification tests (questions designed to elicit a negative answer) over verification tests (examples that confirm what is already suspected) for illuminating the underlying rules.

  7. Attentional effects on rule extraction and consolidation from speech.

    PubMed

    López-Barroso, Diana; Cucurell, David; Rodríguez-Fornells, Antoni; de Diego-Balaguer, Ruth

    2016-07-01

    Incidental learning plays a crucial role in the initial phases of language acquisition. However the knowledge derived from implicit learning, which is based on prediction-based mechanisms, may become explicit. The role that attention plays in the formation of implicit and explicit knowledge of the learned material is unclear. In the present study, we investigated the role that attention plays in the acquisition of non-adjacent rule learning from speech. In addition, we also tested whether the amount of attention during learning changes the representation of the learned material after a 24h delay containing sleep. For that, we developed an experiment run on two consecutive days consisting on the exposure to an artificial language that contained non-adjacent dependencies (rules) between words whereas different conditions were established to manipulate the amount of attention given to the rules (target and non-target conditions). Furthermore, we used both indirect and direct measures of learning that are more sensitive to implicit and explicit knowledge, respectively. Whereas the indirect measures indicated that learning of the rules occurred regardless of attention, more explicit judgments after learning showed differences in the type of learning reached under the two attention conditions. 24 hours later, indirect measures showed no further improvements during additional language exposure and explicit judgments indicated that only the information more robustly learned in the previous day, was consolidated. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

  8. Movement rules for individual-based models of stream fish

    Treesearch

    Steven F. Railsback; Roland H. Lamberson; Bret C. Harvey; Walter E. Duffy

    1999-01-01

    Abstract - Spatially explicit individual-based models (IBMs) use movement rules to determine when an animal departs its current location and to determine its movement destination; these rules are therefore critical to accurate simulations. Movement rules typically define some measure of how an individual's expected fitness varies among locations, under the...

  9. Abstraction, ethics and software: Why don`t the rules work?

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Warwick, S.

    1994-12-31

    A theory is presented that one of the reasons why the use of unlicensed software is so widespread and unstigmatized is that legislatures, courts and other bodies which create policy operate at a higher level of abstraction than do individuals, and that abstraction is a key factor in the divergence of societal behavior from that condoned by legal statute. This theory is explored through a pilot study consisting of medium depth interviews with two volunteers who had used unlicensed software. Their attitudes, understanding of the law, and characterization of the their use of unlicensed software as based on {open_quotes}need{close_quotes} ismore » reported. In addition, the concept of face is examined, and how it is maintained while violating law. It is suggested that further studies, using multiple methodologies, (in-depth interview, focus groups, and surveys) be conducted prior to developing further policy or legislation regarding intellectual property protection for software.« less

  10. 37 CFR 1.72 - Title and abstract.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 37 Patents, Trademarks, and Copyrights 1 2014-07-01 2014-07-01 false Title and abstract. 1.72 Section 1.72 Patents, Trademarks, and Copyrights UNITED STATES PATENT AND TRADEMARK OFFICE, DEPARTMENT OF COMMERCE GENERAL RULES OF PRACTICE IN PATENT CASES National Processing Provisions Specification § 1.72...

  11. Brain Regions Involved in the Learning and Application of Reward Rules in a Two-Deck Gambling Task

    ERIC Educational Resources Information Center

    Hartstra, E.; Oldenburg, J. F. E.; Van Leijenhorst, L.; Rombouts, S. A. R. B.; Crone, E. A.

    2010-01-01

    Decision-making involves the ability to choose between competing actions that are associated with uncertain benefits and penalties. The Iowa Gambling Task (IGT), which mimics real-life decision-making, involves learning a reward-punishment rule over multiple trials. Patients with damage to ventromedial prefrontal cortex (VMPFC) show deficits…

  12. Mathematical Abstraction: Constructing Concept of Parallel Coordinates

    NASA Astrophysics Data System (ADS)

    Nurhasanah, F.; Kusumah, Y. S.; Sabandar, J.; Suryadi, D.

    2017-09-01

    Mathematical abstraction is an important process in teaching and learning mathematics so pre-service mathematics teachers need to understand and experience this process. One of the theoretical-methodological frameworks for studying this process is Abstraction in Context (AiC). Based on this framework, abstraction process comprises of observable epistemic actions, Recognition, Building-With, Construction, and Consolidation called as RBC + C model. This study investigates and analyzes how pre-service mathematics teachers constructed and consolidated concept of Parallel Coordinates in a group discussion. It uses AiC framework for analyzing mathematical abstraction of a group of pre-service teachers consisted of four students in learning Parallel Coordinates concepts. The data were collected through video recording, students’ worksheet, test, and field notes. The result shows that the students’ prior knowledge related to concept of the Cartesian coordinate has significant role in the process of constructing Parallel Coordinates concept as a new knowledge. The consolidation process is influenced by the social interaction between group members. The abstraction process taken place in this group were dominated by empirical abstraction that emphasizes on the aspect of identifying characteristic of manipulated or imagined object during the process of recognizing and building-with.

  13. The application of top-down abstraction learning using prediction as a supervisory signal to cyber security

    NASA Astrophysics Data System (ADS)

    Mugan, Jonathan; Khalili, Aram E.

    2014-05-01

    Current computer systems are dumb automatons, and their blind execution of instructions makes them open to attack. Their inability to reason means that they don't consider the larger, constantly changing context outside their immediate inputs. Their nearsightedness is particularly dangerous because, in our complex systems, it is difficult to prevent all exploitable situations. Additionally, the lack of autonomous oversight of our systems means they are unable to fight through attacks. Keeping adversaries completely out of systems may be an unreasonable expectation, and our systems need to adapt to attacks and other disruptions to achieve their objectives. What is needed is an autonomous controller within the computer system that can sense the state of the system and reason about that state. In this paper, we present Self-Awareness Through Predictive Abstraction Modeling (SATPAM). SATPAM uses prediction to learn abstractions that allow it to recognize the right events at the right level of detail. These abstractions allow SATPAM to break the world into small, relatively independent, pieces that allow employment of existing reasoning methods. SATPAM goes beyond classification-based machine learning and statistical anomaly detection to be able to reason about the system, and SATPAM's knowledge representation and reasoning is more like that of a human. For example, humans intuitively know that the color of a car is not relevant to any mechanical problem, and SATPAM provides a plausible method whereby a machine can acquire such reasoning patterns. In this paper, we present the initial experimental results using SATPAM.

  14. Automatic learning of rules. A practical example of using artificial intelligence to improve computer-based detection of myocardial infarction and left ventricular hypertrophy in the 12-lead ECG.

    PubMed

    Kaiser, W; Faber, T S; Findeis, M

    1996-01-01

    The authors developed a computer program that detects myocardial infarction (MI) and left ventricular hypertrophy (LVH) in two steps: (1) by extracting parameter values from a 10-second, 12-lead electrocardiogram, and (2) by classifying the extracted parameter values with rule sets. Every disease has its dedicated set of rules. Hence, there are separate rule sets for anterior MI, inferior MI, and LVH. If at least one rule is satisfied, the disease is said to be detected. The computer program automatically develops these rule sets. A database (learning set) of healthy subjects and patients with MI, LVH, and mixed MI+LVH was used. After defining the rule type, initial limits, and expected quality of the rules (positive predictive value, minimum number of patients), the program creates a set of rules by varying the limits. The general rule type is defined as: disease = lim1l < p1 < or = lim1u and lim2l < p2 < or = lim2u and ... limnl < pn < or = limnu. When defining the rule types, only the parameters (p1 ... pn) that are known as clinical electrocardiographic criteria (amplitudes [mV] of Q, R, and T waves and ST-segment; duration [ms] of Q wave; frontal angle [degrees]) were used. This allowed for submitting the learned rule sets to an independent investigator for medical verification. It also allowed the creation of explanatory texts with the rules. These advantages are not offered by the neurons of a neural network. The learned rules were checked against a test set and the following results were obtained: MI: sensitivity 76.2%, positive predictive value 98.6%; LVH: sensitivity 72.3%, positive predictive value 90.9%. The specificity ratings for MI are better than 98%; for LVH, better than 90%.

  15. Retinal hemorrhage detection by rule-based and machine learning approach.

    PubMed

    Di Xiao; Shuang Yu; Vignarajan, Janardhan; Dong An; Mei-Ling Tay-Kearney; Kanagasingam, Yogi

    2017-07-01

    Robust detection of hemorrhages (HMs) in color fundus image is important in an automatic diabetic retinopathy grading system. Detection of the hemorrhages that are close to or connected with retinal blood vessels was found to be challenge. However, most methods didn't put research on it, even some of them mentioned this issue. In this paper, we proposed a novel hemorrhage detection method based on rule-based and machine learning methods. We focused on the improvement of detection of the hemorrhages that are close to or connected with retinal blood vessels, besides detecting the independent hemorrhage regions. A preliminary test for detecting HM presence was conducted on the images from two databases. We achieved sensitivity and specificity of 93.3% and 88% as well as 91.9% and 85.6% on the two datasets.

  16. Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data.

    PubMed

    Pesesky, Mitchell W; Hussain, Tahir; Wallace, Meghan; Patel, Sanket; Andleeb, Saadia; Burnham, Carey-Ann D; Dantas, Gautam

    2016-01-01

    The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and

  17. Trading Rules on Stock Markets Using Genetic Network Programming with Reinforcement Learning and Importance Index

    NASA Astrophysics Data System (ADS)

    Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki

    Genetic Network Programming (GNP) is an evolutionary computation which represents its solutions using graph structures. Since GNP can create quite compact programs and has an implicit memory function, it has been clarified that GNP works well especially in dynamic environments. In addition, a study on creating trading rules on stock markets using GNP with Importance Index (GNP-IMX) has been done. IMX is a new element which is a criterion for decision making. In this paper, we combined GNP-IMX with Actor-Critic (GNP-IMX&AC) and create trading rules on stock markets. Evolution-based methods evolve their programs after enough period of time because they must calculate fitness values, however reinforcement learning can change programs during the period, therefore the trading rules can be created efficiently. In the simulation, the proposed method is trained using the stock prices of 10 brands in 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. The simulation results show that the proposed method can obtain larger profits than GNP-IMX without AC and Buy&Hold.

  18. An Integrated Planning Representation Using Macros, Abstractions, and Cases

    NASA Technical Reports Server (NTRS)

    Baltes, Jacky; MacDonald, Bruce

    1992-01-01

    Planning will be an essential part of future autonomous robots and integrated intelligent systems. This paper focuses on learning problem solving knowledge in planning systems. The system is based on a common representation for macros, abstractions, and cases. Therefore, it is able to exploit both classical and case based techniques. The general operators in a successful plan derivation would be assessed for their potential usefulness, and some stored. The feasibility of this approach was studied through the implementation of a learning system for abstraction. New macros are motivated by trying to improve the operatorset. One heuristic used to improve the operator set is generating operators with more general preconditions than existing ones. This heuristic leads naturally to abstraction hierarchies. This investigation showed promising results on the towers of Hanoi problem. The paper concludes by describing methods for learning other problem solving knowledge. This knowledge can be represented by allowing operators at different levels of abstraction in a refinement.

  19. Improving drivers' knowledge of road rules using digital games.

    PubMed

    Li, Qing; Tay, Richard

    2014-04-01

    Although a proficient knowledge of the road rules is important to safe driving, many drivers do not retain the knowledge acquired after they have obtained their licenses. Hence, more innovative and appealing methods are needed to improve drivers' knowledge of the road rules. This study examines the effect of game based learning on drivers' knowledge acquisition and retention. We find that playing an entertaining game that is designed to impart knowledge of the road rules not only improves players' knowledge but also helps them retain such knowledge. Hence, learning by gaming appears to be a promising learning approach for driver education. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. High stimulus variability in nonnative speech learning supports formation of abstract categories: evidence from Japanese geminates.

    PubMed

    Sadakata, Makiko; McQueen, James M

    2013-08-01

    This study reports effects of a high-variability training procedure on nonnative learning of a Japanese geminate-singleton fricative contrast. Thirty native speakers of Dutch took part in a 5-day training procedure in which they identified geminate and singleton variants of the Japanese fricative /s/. Participants were trained with either many repetitions of a limited set of words recorded by a single speaker (low-variability training) or with fewer repetitions of a more variable set of words recorded by multiple speakers (high-variability training). Both types of training enhanced identification of speech but not of nonspeech materials, indicating that learning was domain specific. High-variability training led to superior performance in identification but not in discrimination tests, and supported better generalization of learning as shown by transfer from the trained fricatives to the identification of untrained stops and affricates. Variability thus helps nonnative listeners to form abstract categories rather than to enhance early acoustic analysis.

  1. Reducing Abstraction When Learning Graph Theory

    ERIC Educational Resources Information Center

    Hazzan, Orit; Hadar, Irit

    2005-01-01

    This article presents research on students' understanding of basic concepts in Graph Theory. Students' understanding is analyzed through the lens of the theoretical framework of reducing abstraction (Hazzan, 1999). As it turns out, in spite of the relative simplicity of the concepts that are introduced in the introductory part of a traditional…

  2. An investigation of care-based vs. rule-based morality in frontotemporal dementia, Alzheimer's disease, and healthy controls.

    PubMed

    Carr, Andrew R; Paholpak, Pongsatorn; Daianu, Madelaine; Fong, Sylvia S; Mather, Michelle; Jimenez, Elvira E; Thompson, Paul; Mendez, Mario F

    2015-11-01

    Behavioral changes in dementia, especially behavioral variant frontotemporal dementia (bvFTD), may result in alterations in moral reasoning. Investigators have not clarified whether these alterations reflect differential impairment of care-based vs. rule-based moral behavior. This study investigated 18 bvFTD patients, 22 early onset Alzheimer's disease (eAD) patients, and 20 healthy age-matched controls on care-based and rule-based items from the Moral Behavioral Inventory and the Social Norms Questionnaire, neuropsychological measures, and magnetic resonance imaging (MRI) regions of interest. There were significant group differences with the bvFTD patients rating care-based morality transgressions less severely than the eAD group and rule-based moral behavioral transgressions more severely than controls. Across groups, higher care-based morality ratings correlated with phonemic fluency on neuropsychological tests, whereas higher rule-based morality ratings correlated with increased difficulty set-shifting and learning new rules to tasks. On neuroimaging, severe care-based reasoning correlated with cortical volume in right anterior temporal lobe, and rule-based reasoning correlated with decreased cortical volume in the right orbitofrontal cortex. Together, these findings suggest that frontotemporal disease decreases care-based morality and facilitates rule-based morality possibly from disturbed contextual abstraction and set-shifting. Future research can examine whether frontal lobe disorders and bvFTD result in a shift from empathic morality to the strong adherence to conventional rules. Published by Elsevier Ltd.

  3. Oxytocin conditions trait-based rule adherence

    PubMed Central

    De Dreu, Carsten K.W.

    2017-01-01

    Abstract Rules, whether in the form of norms, taboos or laws, regulate and coordinate human life. Some rules, however, are arbitrary and adhering to them can be personally costly. Rigidly sticking to such rules can be considered maladaptive. Here, we test whether, at the neurobiological level, (mal)adaptive rule adherence is reduced by oxytocin—a hypothalamic neuropeptide that biases the biobehavioural approach-avoidance system. Participants (N = 139) self-administered oxytocin or placebo intranasally, and reported their need for structure and approach-avoidance sensitivity. Next, participants made binary decisions and were given an arbitrary rule that demanded to forgo financial benefits. Under oxytocin, participants violated the rule more often, especially when they had high need for structure and high approach sensitivity. Possibly, oxytocin dampens the need for a highly structured environment and enables individuals to flexibly trade-off internal desires against external restrictions. Implications for the treatment of clinical disorders marked by maladaptive rule adherence are discussed. PMID:27664999

  4. Comparison of Natural Language Processing Rules-based and Machine-learning Systems to Identify Lumbar Spine Imaging Findings Related to Low Back Pain.

    PubMed

    Tan, W Katherine; Hassanpour, Saeed; Heagerty, Patrick J; Rundell, Sean D; Suri, Pradeep; Huhdanpaa, Hannu T; James, Kathryn; Carrell, David S; Langlotz, Curtis P; Organ, Nancy L; Meier, Eric N; Sherman, Karen J; Kallmes, David F; Luetmer, Patrick H; Griffith, Brent; Nerenz, David R; Jarvik, Jeffrey G

    2018-03-28

    To evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems. We used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N = 178,333 reports, we randomly selected N = 871 to form a reference-standard dataset, consisting of N = 413 x-ray reports and N = 458 MR reports. Using standardized criteria, four spine experts annotated the presence of 26 findings, where 71 reports were annotated by all four experts and 800 were each annotated by two experts. We calculated inter-rater agreement and finding prevalence from annotated data. We randomly split the annotated data into development (80%) and testing (20%) sets. We developed an NLP system from both rule-based and machine-learned models. We validated the system using accuracy metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The multirater annotated dataset achieved inter-rater agreement of Cohen's kappa > 0.60 (substantial agreement) for 25 of 26 findings, with finding prevalence ranging from 3% to 89%. In the testing sample, rule-based and machine-learned predictions both had comparable average specificity (0.97 and 0.95, respectively). The machine-learned approach had a higher average sensitivity (0.94, compared to 0.83 for rules-based), and a higher overall AUC (0.98, compared to 0.90 for rules-based). Our NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC. Copyright © 2018 The Association of University Radiologists. All rights reserved.

  5. Rule Breaking in the Child Care Centre: Tensions for Children and Teachers

    ERIC Educational Resources Information Center

    Brennan, Margaret

    2016-01-01

    Research suggests that young children transgress conventional rules in every culture and society. In this article, the argument is made that rule teaching and learning provide insight into how children learn to be part of a group. The research question addressed is, "Why do some children transgress the rules if their actions risk jeopardising…

  6. Spatial abstraction for autonomous robot navigation.

    PubMed

    Epstein, Susan L; Aroor, Anoop; Evanusa, Matthew; Sklar, Elizabeth I; Parsons, Simon

    2015-09-01

    Optimal navigation for a simulated robot relies on a detailed map and explicit path planning, an approach problematic for real-world robots that are subject to noise and error. This paper reports on autonomous robots that rely on local spatial perception, learning, and commonsense rationales instead. Despite realistic actuator error, learned spatial abstractions form a model that supports effective travel.

  7. The Effectiveness of Education and Schooling Activities with Respect to Learning Styles on the Learning of Abstract and Tangible Concepts of Social Studies by Students

    ERIC Educational Resources Information Center

    Seker, Mustafa

    2013-01-01

    This research reviews the effects of education and schooling activities that are conducted with respect to different learning styles on the success of teaching abstract and tangible concepts of 6th Grade Social Studies, and researches whether the demographic variables (age, gender) of the students had any effect on this success levels. To do so, 2…

  8. Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

    PubMed

    Li, Yang; Li, Guoqing; Wang, Zhenhao

    2015-01-01

    In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

  9. An Investigation of Care-Based vs. Rule-Based Morality in Frontotemporal Dementia, Alzheimer’s Disease, and Healthy Controls

    PubMed Central

    Carr, Andrew R.; Paholpak, Pongsatorn; Daianu, Madelaine; Fong, Sylvia S.; Mather, Michelle; Jimenez, Elvira E.; Thompson, Paul; Mendez, Mario F.

    2015-01-01

    Behavioral changes in dementia, especially behavioral variant frontotemporal dementia (bvFTD), may result in alterations in moral reasoning. Investigators have not clarified whether these alterations reflect differential impairment of care-based vs. rule-based moral behavior. This study investigated 18 bvFTD patients, 22 early onset Alzheimer’s disease (eAD) patients, and 20 healthy age-matched controls on care-based and rule-based items from the Moral Behavioral Inventory and the Social Norms Questionnaire, neuropsychological measures, and magnetic resonance imaging (MRI) regions of interest. There were significant group differences with the bvFTD patients rating care-based morality transgressions less severely than the eAD group and rule-based moral behavioral transgressions more severely than controls. Across groups, higher care-based morality ratings correlated with phonemic fluency on neuropsychological tests, whereas higher rule-based morality ratings correlated with increased difficulty set-shifting and learning new rules to tasks. On neuroimaging, severe care-based reasoning correlated with cortical volume in right anterior temporal lobe, and rule-based reasoning correlated with decreased cortical volume in the right orbitofrontal cortex. Together, these findings suggest that frontotemporal disease decreases care-based morality and facilitates rule-based morality possibly from disturbed contextual abstraction and set-shifting. Future research can examine whether frontal lobe disorders and bvFTD result in a shift from empathic morality to the strong adherence to conventional rules. PMID:26432341

  10. Using Group Explorer in teaching abstract algebra

    NASA Astrophysics Data System (ADS)

    Schubert, Claus; Gfeller, Mary; Donohue, Christopher

    2013-04-01

    This study explores the use of Group Explorer in an undergraduate mathematics course in abstract algebra. The visual nature of Group Explorer in representing concepts in group theory is an attractive incentive to use this software in the classroom. However, little is known about students' perceptions on this technology in learning concepts in abstract algebra. A total of 26 participants in an undergraduate course studying group theory were surveyed regarding their experiences using Group Explorer. Findings indicate that all participants believed that the software was beneficial to their learning and described their attitudes regarding the software in terms of using the technology and its helpfulness in learning concepts. A multiple regression analysis reveals that representational fluency of concepts with the software correlated significantly with participants' understanding of group concepts yet, participants' attitudes about Group Explorer and technology in general were not significant factors.

  11. Learning from escaped prescribed fire reviews [Abstract

    Treesearch

    Anne Black; Dave Thomas; James Saveland

    2011-01-01

    Over the past decade, the wildland fire community has developed a number of innovative methods for conducting a review following escape of a prescribed fire. The stated purpose been to identify methods that not only meet policy requirements, but to reduce future escapes. Implicit is the assumption that a review leads to learning. Yet, as organizational learning expert...

  12. Birth of an Abstraction: A Dynamical Systems Account of the Discovery of an Elsewhere Principle in a Category Learning Task

    ERIC Educational Resources Information Center

    Tabor, Whitney; Cho, Pyeong W.; Dankowicz, Harry

    2013-01-01

    Human participants and recurrent ("connectionist") neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular ("strong") classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the…

  13. Abstract Algebra for Teachers: An Evaluative Case Study

    ERIC Educational Resources Information Center

    Hoffman, Andrew Joseph

    2017-01-01

    This manuscript describes the study of an abstract algebra course for preservice secondary mathematics teachers (PSMTs). Often, courses in abstract algebra have not been viewed as productive, beneficial learning experiences for future teachers, both by researchers and PSMTs themselves. This despite calls for increased content knowledge for…

  14. Interactive Synthesis of Code Level Security Rules

    DTIC Science & Technology

    2017-04-01

    Interactive Synthesis of Code-Level Security Rules A Thesis Presented by Leo St. Amour to The Department of Computer Science in partial fulfillment...of the requirements for the degree of Master of Science in Computer Science Northeastern University Boston, Massachusetts April 2017 DISTRIBUTION...Abstract of the Thesis Interactive Synthesis of Code-Level Security Rules by Leo St. Amour Master of Science in Computer Science Northeastern University

  15. Learning the Rules: The Anatomy of Children's Relationships.

    ERIC Educational Resources Information Center

    Bigelow, Brian J.; Tesson, Geoffrey; Lewko, John H.

    This book explores the process and characteristics of children's personal and social relationships. To determine what relationships mean to children and how children manage those relationships, a recursive interviewing technique was used with nearly a thousand children to detail children's social rules. Those rules cover a range of social issues,…

  16. Fuzzy Q-Learning for Generalization of Reinforcement Learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1996-01-01

    Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.

  17. Abstraction in perceptual symbol systems.

    PubMed Central

    Barsalou, Lawrence W

    2003-01-01

    After reviewing six senses of abstraction, this article focuses on abstractions that take the form of summary representations. Three central properties of these abstractions are established: ( i ) type-token interpretation; (ii) structured representation; and (iii) dynamic realization. Traditional theories of representation handle interpretation and structure well but are not sufficiently dynamical. Conversely, connectionist theories are exquisitely dynamic but have problems with structure. Perceptual symbol systems offer an approach that implements all three properties naturally. Within this framework, a loose collection of property and relation simulators develops to represent abstractions. Type-token interpretation results from binding a property simulator to a region of a perceived or simulated category member. Structured representation results from binding a configuration of property and relation simulators to multiple regions in an integrated manner. Dynamic realization results from applying different subsets of property and relation simulators to category members on different occasions. From this standpoint, there are no permanent or complete abstractions of a category in memory. Instead, abstraction is the skill to construct temporary online interpretations of a category's members. Although an infinite number of abstractions are possible, attractors develop for habitual approaches to interpretation. This approach provides new ways of thinking about abstraction phenomena in categorization, inference, background knowledge and learning. PMID:12903648

  18. Functional networks inference from rule-based machine learning models.

    PubMed

    Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

    2016-01-01

    Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

  19. Mass Communication: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," January through June 1980 (Vol. 40 Nos. 7 through 12).

    ERIC Educational Resources Information Center

    ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

    This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 55 titles deal with a variety of topics, including the following: (1) the prime time access rule; (2) media education; (3) magazine and children's advertising; (4) Irish national and Third World cinema; (5) international radio…

  20. Innovation Abstracts, Volume VIII, Numbers 1-29.

    ERIC Educational Resources Information Center

    Roueche, Suanne D., Ed.

    1986-01-01

    This series of one- and two-page abstracts highlights a variety of innovative approaches to teaching and learning in the community college. Topics covered in the articles include: the use of "trigger films" in group learning situations; letter writing as a means of maintaining group cohesion in a nontraditional classroom; creative grading; the use…

  1. A Comparison of the neural correlates that underlie rule-based and information-integration category learning.

    PubMed

    Carpenter, Kathryn L; Wills, Andy J; Benattayallah, Abdelmalek; Milton, Fraser

    2016-10-01

    The influential competition between verbal and implicit systems (COVIS) model proposes that category learning is driven by two competing neural systems-an explicit, verbal, system, and a procedural-based, implicit, system. In the current fMRI study, participants learned either a conjunctive, rule-based (RB), category structure that is believed to engage the explicit system, or an information-integration category structure that is thought to preferentially recruit the implicit system. The RB and information-integration category structures were matched for participant error rate, the number of relevant stimulus dimensions, and category separation. Under these conditions, considerable overlap in brain activation, including the prefrontal cortex, basal ganglia, and the hippocampus, was found between the RB and information-integration category structures. Contrary to the predictions of COVIS, the medial temporal lobes and in particular the hippocampus, key regions for explicit memory, were found to be more active in the information-integration condition than in the RB condition. No regions were more activated in RB than information-integration category learning. The implications of these results for theories of category learning are discussed. Hum Brain Mapp 37:3557-3574, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  2. Biclustering Learning of Trading Rules.

    PubMed

    Huang, Qinghua; Wang, Ting; Tao, Dacheng; Li, Xuelong

    2015-10-01

    Technical analysis with numerous indicators and patterns has been regarded as important evidence for making trading decisions in financial markets. However, it is extremely difficult for investors to find useful trading rules based on numerous technical indicators. This paper innovatively proposes the use of biclustering mining to discover effective technical trading patterns that contain a combination of indicators from historical financial data series. This is the first attempt to use biclustering algorithm on trading data. The mined patterns are regarded as trading rules and can be classified as three trading actions (i.e., the buy, the sell, and no-action signals) with respect to the maximum support. A modified K nearest neighborhood ( K -NN) method is applied to classification of trading days in the testing period. The proposed method [called biclustering algorithm and the K nearest neighbor (BIC- K -NN)] was implemented on four historical datasets and the average performance was compared with the conventional buy-and-hold strategy and three previously reported intelligent trading systems. Experimental results demonstrate that the proposed trading system outperforms its counterparts and will be useful for investment in various financial markets.

  3. Learning Optimal Individualized Treatment Rules from Electronic Health Record Data

    PubMed Central

    Wang, Yuanjia; Wu, Peng; Liu, Ying; Weng, Chunhua; Zeng, Donglin

    2016-01-01

    Medical research is experiencing a paradigm shift from “one-size-fits-all” strategy to a precision medicine approach where the right therapy, for the right patient, and at the right time, will be prescribed. We propose a statistical method to estimate the optimal individualized treatment rules (ITRs) that are tailored according to subject-specific features using electronic health records (EHR) data. Our approach merges statistical modeling and medical domain knowledge with machine learning algorithms to assist personalized medical decision making using EHR. We transform the estimation of optimal ITR into a classification problem and account for the non-experimental features of the EHR data and confounding by clinical indication. We create a broad range of feature variables that reflect both patient health status and healthcare data collection process. Using EHR data collected at Columbia University clinical data warehouse, we construct a decision tree for choosing the best second line therapy for treating type 2 diabetes patients. PMID:28503676

  4. Evolving fuzzy rules for relaxed-criteria negotiation.

    PubMed

    Sim, Kwang Mong

    2008-12-01

    In the literature on automated negotiation, very few negotiation agents are designed with the flexibility to slightly relax their negotiation criteria to reach a consensus more rapidly and with more certainty. Furthermore, these relaxed-criteria negotiation agents were not equipped with the ability to enhance their performance by learning and evolving their relaxed-criteria negotiation rules. The impetus of this work is designing market-driven negotiation agents (MDAs) that not only have the flexibility of relaxing bargaining criteria using fuzzy rules, but can also evolve their structures by learning new relaxed-criteria fuzzy rules to improve their negotiation outcomes as they participate in negotiations in more e-markets. To this end, an evolutionary algorithm for adapting and evolving relaxed-criteria fuzzy rules was developed. Implementing the idea in a testbed, two kinds of experiments for evaluating and comparing EvEMDAs (MDAs with relaxed-criteria rules that are evolved using the evolutionary algorithm) and EMDAs (MDAs with relaxed-criteria rules that are manually constructed) were carried out through stochastic simulations. Empirical results show that: 1) EvEMDAs generally outperformed EMDAs in different types of e-markets and 2) the negotiation outcomes of EvEMDAs generally improved as they negotiated in more e-markets.

  5. Phonological Memory and Rule Learning

    ERIC Educational Resources Information Center

    Williams, John N.; Lovatt, Peter

    2005-01-01

    Our research reflects the current trend to relate individual differences in second language learning to underlying cognitive processes e.g., Robinson, 2002. We believe that such investigations, apart from being of practical importance, can also shed light on the cognitive mechanisms underlying the language learning process. Here we focus on the…

  6. Striving for Excellence. The International Conference of the Learning Disabilities Association of America (Atlanta, Georgia, March 4-7, 1992). Research Poster Session Abstract. Volume 1.

    ERIC Educational Resources Information Center

    Russell, Steven C., Comp.

    Eleven abstracts of research projects related to individuals with learning disabilities are compiled in this booklet. The research projects were presented in poster sessions at the March 1992 International Conference of the Learning Disabilities Association of America. Titles and authors of poster sessions include: "Perceptual and Verbal Skills of…

  7. Alteration of a motor learning rule under mirror-reversal transformation does not depend on the amplitude of visual error.

    PubMed

    Kasuga, Shoko; Kurata, Makiko; Liu, Meigen; Ushiba, Junichi

    2015-05-01

    Human's sophisticated motor learning system paradoxically interferes with motor performance when visual information is mirror-reversed (MR), because normal movement error correction further aggravates the error. This error-increasing mechanism makes performing even a simple reaching task difficult, but is overcome by alterations in the error correction rule during the trials. To isolate factors that trigger learners to change the error correction rule, we manipulated the gain of visual angular errors when participants made arm-reaching movements with mirror-reversed visual feedback, and compared the rule alteration timing between groups with normal or reduced gain. Trial-by-trial changes in the visual angular error was tracked to explain the timing of the change in the error correction rule. Under both gain conditions, visual angular errors increased under the MR transformation, and suddenly decreased after 3-5 trials with increase. The increase became degressive at different amplitude between the two groups, nearly proportional to the visual gain. The findings suggest that the alteration of the error-correction rule is not dependent on the amplitude of visual angular errors, and possibly determined by the number of trials over which the errors increased or statistical property of the environment. The current results encourage future intensive studies focusing on the exact rule-change mechanism. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

  8. Rule-based and information-integration perceptual category learning in children with attention-deficit/hyperactivity disorder.

    PubMed

    Huang-Pollock, Cynthia L; Maddox, W Todd; Tam, Helen

    2014-07-01

    Suboptimal functioning of the basal ganglia is implicated in attention-deficit/hyperactivity disorder (ADHD). These structures are important to the acquisition of associative knowledge, leading some to theorize that associative learning deficits might be expected, despite the fact that most extant research in ADHD has focused on effortful control. We present 2 studies that examined the acquisition of explicit rule-based (RB) and associative information integration (II) category learning among school-age children with ADHD. In Study 1, we found deficits in both RB and II category learning tasks among children with ADHD (n = 81) versus controls (n = 42). Children with ADHD tended to sort by the more salient but irrelevant dimension (in the RB paradigm) and were unable to acquire a consistent sorting strategy (in the II paradigm). To disentangle whether the deficit was localized to II category learning versus a generalized inability to consider more than 1 stimulus dimension, in Study 2 children completed a conjunctive RB paradigm that required consideration of 2 stimulus dimensions. Children with ADHD (n = 50) continued to underperform controls (n = 33). Results provide partial support for neurocognitive developmental theories of ADHD that suggest that associative learning deficits should be found, and highlight the importance of using analytic approaches that go beyond asking whether an ADHD-related deficit exists to why such deficits exist.

  9. The Roles of Innate Information, Learning Rules and Plasticity in Migratory Bird Orientation

    NASA Astrophysics Data System (ADS)

    Able, Kenneth P.; Able, Mary A.

    This paper and the following three papers were presented at the RIN97 Conference held in Oxford under the auspices of the Animal Navigation Special Interest Group, April 1997. The full proceedings, under the title Orientation and Navigation - Birds, Humans and Other Animals, can be obtained from the Director (£30 to Members, £50 to non-Members).Studies of the compass mechanisms involved in the migratory orientation of birds have revealed a complex web of interactions, both during the development of orientation behaviour in young birds and in mature individuals exhibiting migratory activity. In young birds, the acquisition of compass orientation capabilities involves the interplay of apparently genetically programmed information with a suite of innate learning rules. The latter canalise the ways in which experience with relevant orientation information from the environment impinges on development. There are many general similarities with the development of singing behaviour in songbirds, although that system is more thoroughly understood, especially at the neuronal level.Here we shall attempt to synthesise what is known about the development of compass mechanisms in a framework of innate information and learning rules. The way in which orientation behaviour develops leaves open the possibility for plasticity that enables birds to compensate for variability in the environmental cues that form the basis of their compasses. For at least some components of the system, behavioural plasticity remains into adulthood, allowing the bird on migration to respond in apparently adaptive ways to spatial and temporal variability in orientation information that it may encounter while enroute. We have studied these questions in the Savannah sparrow (Passerculus sandwichensis), a medium-distance North American emberizine nocturnal migrant. We will focus on that species, relating the results of our work to relevant studies on others.

  10. How a Visual Language of Abstract Shapes Facilitates Cultural and International Border Crossings

    ERIC Educational Resources Information Center

    Conroy, Arthur Thomas, III

    2016-01-01

    This article describes a visual language comprised of abstract shapes that has been shown to be effective in communicating prior knowledge between and within members of a small team or group. The visual language includes a set of geometric shapes and rules that guide the construction of the abstract diagrams that are the external representation of…

  11. Making Implicit Metalevel Rules of the Discourse on Function Explicit Topics of Reflection in the Classroom to Foster Student Learning

    ERIC Educational Resources Information Center

    Güçler, Beste

    2016-01-01

    Despite the existence of extensive literature on functions, fewer studies used sociocultural views to explore the development of student learning about the concept. This study uses a discursive lens to examine whether an instructional approach that specifically attends to particular metalevel rules in the mathematical discourse on functions…

  12. Portable design rules for bulk CMOS

    NASA Technical Reports Server (NTRS)

    Griswold, T. W.

    1982-01-01

    It is pointed out that for the past several years, one school of IC designers has used a simplified set of nMOS geometric design rules (GDR) which is 'portable', in that it can be used by many different nMOS manufacturers. The present investigation is concerned with a preliminary set of design rules for bulk CMOS which has been verified for simple test structures. The GDR are defined in terms of Caltech Intermediate Form (CIF), which is a geometry-description language that defines simple geometrical objects in layers. The layers are abstractions of physical mask layers. The design rules do not presume the existence of any particular design methodology. Attention is given to p-well and n-well CMOS processes, bulk CMOS and CMOS-SOS, CMOS geometric rules, and a description of the advantages of CMOS technology.

  13. Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control

    PubMed Central

    Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

    2014-01-01

    Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms. PMID:25389391

  14. Neuromodulatory adaptive combination of correlation-based learning in cerebellum and reward-based learning in basal ganglia for goal-directed behavior control.

    PubMed

    Dasgupta, Sakyasingha; Wörgötter, Florentin; Manoonpong, Poramate

    2014-01-01

    Goal-directed decision making in biological systems is broadly based on associations between conditional and unconditional stimuli. This can be further classified as classical conditioning (correlation-based learning) and operant conditioning (reward-based learning). A number of computational and experimental studies have well established the role of the basal ganglia in reward-based learning, where as the cerebellum plays an important role in developing specific conditioned responses. Although viewed as distinct learning systems, recent animal experiments point toward their complementary role in behavioral learning, and also show the existence of substantial two-way communication between these two brain structures. Based on this notion of co-operative learning, in this paper we hypothesize that the basal ganglia and cerebellar learning systems work in parallel and interact with each other. We envision that such an interaction is influenced by reward modulated heterosynaptic plasticity (RMHP) rule at the thalamus, guiding the overall goal directed behavior. Using a recurrent neural network actor-critic model of the basal ganglia and a feed-forward correlation-based learning model of the cerebellum, we demonstrate that the RMHP rule can effectively balance the outcomes of the two learning systems. This is tested using simulated environments of increasing complexity with a four-wheeled robot in a foraging task in both static and dynamic configurations. Although modeled with a simplified level of biological abstraction, we clearly demonstrate that such a RMHP induced combinatorial learning mechanism, leads to stabler and faster learning of goal-directed behaviors, in comparison to the individual systems. Thus, in this paper we provide a computational model for adaptive combination of the basal ganglia and cerebellum learning systems by way of neuromodulated plasticity for goal-directed decision making in biological and bio-mimetic organisms.

  15. A forecast-based STDP rule suitable for neuromorphic implementation.

    PubMed

    Davies, S; Galluppi, F; Rast, A D; Furber, S B

    2012-08-01

    Artificial neural networks increasingly involve spiking dynamics to permit greater computational efficiency. This becomes especially attractive for on-chip implementation using dedicated neuromorphic hardware. However, both spiking neural networks and neuromorphic hardware have historically found difficulties in implementing efficient, effective learning rules. The best-known spiking neural network learning paradigm is Spike Timing Dependent Plasticity (STDP) which adjusts the strength of a connection in response to the time difference between the pre- and post-synaptic spikes. Approaches that relate learning features to the membrane potential of the post-synaptic neuron have emerged as possible alternatives to the more common STDP rule, with various implementations and approximations. Here we use a new type of neuromorphic hardware, SpiNNaker, which represents the flexible "neuromimetic" architecture, to demonstrate a new approach to this problem. Based on the standard STDP algorithm with modifications and approximations, a new rule, called STDP TTS (Time-To-Spike) relates the membrane potential with the Long Term Potentiation (LTP) part of the basic STDP rule. Meanwhile, we use the standard STDP rule for the Long Term Depression (LTD) part of the algorithm. We show that on the basis of the membrane potential it is possible to make a statistical prediction of the time needed by the neuron to reach the threshold, and therefore the LTP part of the STDP algorithm can be triggered when the neuron receives a spike. In our system these approximations allow efficient memory access, reducing the overall computational time and the memory bandwidth required. The improvements here presented are significant for real-time applications such as the ones for which the SpiNNaker system has been designed. We present simulation results that show the efficacy of this algorithm using one or more input patterns repeated over the whole time of the simulation. On-chip results show that

  16. Mental Ability and Mismatch Negativity: Pre-Attentive Discrimination of Abstract Feature Conjunctions in Auditory Sequences

    ERIC Educational Resources Information Center

    Houlihan, Michael; Stelmack, Robert M.

    2012-01-01

    The relation between mental ability and the ability to detect violations of an abstract, third-order conjunction rule was examined using event-related potential measures, specifically mismatch negativity (MMN). The primary objective was to determine whether the extraction of invariant relations based on abstract conjunctions between two…

  17. From Abstract to Concrete Norms in Agent Institutions

    NASA Technical Reports Server (NTRS)

    Grossi, Davide; Dignum, Frank

    2004-01-01

    Norms specifying constraints over institutions are stated in such a form that allows them to regulate a wide range of situations over time without need for modification. To guarantee this stability, the formulation of norms need to abstract from a variety of concrete aspects, which are instead relevant for the actual operationalization of institutions. If agent institutions are to be built, which comply with a set of abstract requirements, how can those requirements be translated in more concrete constraints the impact of which can be described directly in the institution? In this work we make use of logical methods in order to provide a formal characterization of the translation rules that operate the connection between abstract and concrete norms. On the basis of this characterization, a comprehensive formalization of the notion of institution is also provided.

  18. Predictions of the spontaneous symmetry-breaking theory for visual code completeness and spatial scaling in single-cell learning rules.

    PubMed

    Webber, C J

    2001-05-01

    This article shows analytically that single-cell learning rules that give rise to oriented and localized receptive fields, when their synaptic weights are randomly and independently initialized according to a plausible assumption of zero prior information, will generate visual codes that are invariant under two-dimensional translations, rotations, and scale magnifications, provided that the statistics of their training images are sufficiently invariant under these transformations. Such codes span different image locations, orientations, and size scales with equal economy. Thus, single-cell rules could account for the spatial scaling property of the cortical simple-cell code. This prediction is tested computationally by training with natural scenes; it is demonstrated that a single-cell learning rule can give rise to simple-cell receptive fields spanning the full range of orientations, image locations, and spatial frequencies (except at the extreme high and low frequencies at which the scale invariance of the statistics of digitally sampled images must ultimately break down, because of the image boundary and the finite pixel resolution). Thus, no constraint on completeness, or any other coupling between cells, is necessary to induce the visual code to span wide ranges of locations, orientations, and size scales. This prediction is made using the theory of spontaneous symmetry breaking, which we have previously shown can also explain the data-driven self-organization of a wide variety of transformation invariances in neurons' responses, such as the translation invariance of complex cell response.

  19. ALT-C 95: Changing Education, Changing Technology. Conference Abstracts of the Association for Learning Technology Conference (2nd, Milton Keynes, England, United Kingdom, September 11-13, 1995).

    ERIC Educational Resources Information Center

    Hawkridge, David, Ed.

    This program for the 1995 Association for Learning Technology Conference summarizes the presentations of the discussions, demonstrations, workshops, and poster sessions. Abstracts of the following papers presented at the conference are included: "New Structures for Learning" (Patrick Allen & Kate Sankey); "Multiple System…

  20. Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences.

    PubMed

    Bhat, Ajaz Ahmad; Mohan, Vishwanathan; Sandini, Giulio; Morasso, Pietro

    2016-07-01

    Emerging studies indicate that several species such as corvids, apes and children solve 'The Crow and the Pitcher' task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause-effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended 'learning-prediction-abstraction' loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration. © 2016 The Author(s).

  1. Phonological Concept Learning.

    PubMed

    Moreton, Elliott; Pater, Joe; Pertsova, Katya

    2017-01-01

    Linguistic and non-linguistic pattern learning have been studied separately, but we argue for a comparative approach. Analogous inductive problems arise in phonological and visual pattern learning. Evidence from three experiments shows that human learners can solve them in analogous ways, and that human performance in both cases can be captured by the same models. We test GMECCS (Gradual Maximum Entropy with a Conjunctive Constraint Schema), an implementation of the Configural Cue Model (Gluck & Bower, ) in a Maximum Entropy phonotactic-learning framework (Goldwater & Johnson, ; Hayes & Wilson, ) with a single free parameter, against the alternative hypothesis that learners seek featurally simple algebraic rules ("rule-seeking"). We study the full typology of patterns introduced by Shepard, Hovland, and Jenkins () ("SHJ"), instantiated as both phonotactic patterns and visual analogs, using unsupervised training. Unlike SHJ, Experiments 1 and 2 found that both phonotactic and visual patterns that depended on fewer features could be more difficult than those that depended on more features, as predicted by GMECCS but not by rule-seeking. GMECCS also correctly predicted performance differences between stimulus subclasses within each pattern. A third experiment tried supervised training (which can facilitate rule-seeking in visual learning) to elicit simple rule-seeking phonotactic learning, but cue-based behavior persisted. We conclude that similar cue-based cognitive processes are available for phonological and visual concept learning, and hence that studying either kind of learning can lead to significant insights about the other. Copyright © 2015 Cognitive Science Society, Inc.

  2. Statistical Learning of Origin-Specific Statically Optimal Individualized Treatment Rules

    PubMed Central

    van der Laan, Mark J.; Petersen, Maya L.

    2008-01-01

    Consider a longitudinal observational or controlled study in which one collects chronological data over time on a random sample of subjects. The time-dependent process one observes on each subject contains time-dependent covariates, time-dependent treatment actions, and an outcome process or single final outcome of interest. A statically optimal individualized treatment rule (as introduced in van der Laan et. al. (2005), Petersen et. al. (2007)) is a treatment rule which at any point in time conditions on a user-supplied subset of the past, computes the future static treatment regimen that maximizes a (conditional) mean future outcome of interest, and applies the first treatment action of the latter regimen. In particular, Petersen et. al. (2007) clarified that, in order to be statically optimal, an individualized treatment rule should not depend on the observed treatment mechanism. Petersen et. al. (2007) further developed estimators of statically optimal individualized treatment rules based on a past capturing all confounding of past treatment history on outcome. In practice, however, one typically wishes to find individualized treatment rules responding to a user-supplied subset of the complete observed history, which may not be sufficient to capture all confounding. The current article provides an important advance on Petersen et. al. (2007) by developing locally efficient double robust estimators of statically optimal individualized treatment rules responding to such a user-supplied subset of the past. However, failure to capture all confounding comes at a price; the static optimality of the resulting rules becomes origin-specific. We explain origin-specific static optimality, and discuss the practical importance of the proposed methodology. We further present the results of a data analysis in which we estimate a statically optimal rule for switching antiretroviral therapy among patients infected with resistant HIV virus. PMID:19122792

  3. RCDPM 1992 Conference Book of Abstracts.

    ERIC Educational Resources Information Center

    1992

    This booklet contains 51 abstracts of papers presented at the 1992 conference for the Research Council for Diagnostic and Prescriptive Mathematics (RCDPM). Topics covered include: the use of expressive writing to enhance metacognition, adult assessment, cooperative learning assessment, visualization in problem solving, deaf students' beliefs about…

  4. Abstraction and generalization in statistical learning: implications for the relationship between semantic types and episodic tokens

    PubMed Central

    2017-01-01

    Statistical approaches to emergent knowledge have tended to focus on the process by which experience of individual episodes accumulates into generalizable experience across episodes. However, there is a seemingly opposite, but equally critical, process that such experience affords: the process by which, from a space of types (e.g. onions—a semantic class that develops through exposure to individual episodes involving individual onions), we can perceive or create, on-the-fly, a specific token (a specific onion, perhaps one that is chopped) in the absence of any prior perceptual experience with that specific token. This article reviews a selection of statistical learning studies that lead to the speculation that this process—the generation, on the basis of semantic memory, of a novel episodic representation—is itself an instance of a statistical, in fact associative, process. The article concludes that the same processes that enable statistical abstraction across individual episodes to form semantic memories also enable the generation, from those semantic memories, of representations that correspond to individual tokens, and of novel episodic facts about those tokens. Statistical learning is a window onto these deeper processes that underpin cognition. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872378

  5. Abstraction and generalization in statistical learning: implications for the relationship between semantic types and episodic tokens.

    PubMed

    Altmann, Gerry T M

    2017-01-05

    Statistical approaches to emergent knowledge have tended to focus on the process by which experience of individual episodes accumulates into generalizable experience across episodes. However, there is a seemingly opposite, but equally critical, process that such experience affords: the process by which, from a space of types (e.g. onions-a semantic class that develops through exposure to individual episodes involving individual onions), we can perceive or create, on-the-fly, a specific token (a specific onion, perhaps one that is chopped) in the absence of any prior perceptual experience with that specific token. This article reviews a selection of statistical learning studies that lead to the speculation that this process-the generation, on the basis of semantic memory, of a novel episodic representation-is itself an instance of a statistical, in fact associative, process. The article concludes that the same processes that enable statistical abstraction across individual episodes to form semantic memories also enable the generation, from those semantic memories, of representations that correspond to individual tokens, and of novel episodic facts about those tokens. Statistical learning is a window onto these deeper processes that underpin cognition.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

  6. A novel artificial immune clonal selection classification and rule mining with swarm learning model

    NASA Astrophysics Data System (ADS)

    Al-Sheshtawi, Khaled A.; Abdul-Kader, Hatem M.; Elsisi, Ashraf B.

    2013-06-01

    Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

  7. Generating Concise Rules for Human Motion Retrieval

    NASA Astrophysics Data System (ADS)

    Mukai, Tomohiko; Wakisaka, Ken-Ichi; Kuriyama, Shigeru

    This paper proposes a method for retrieving human motion data with concise retrieval rules based on the spatio-temporal features of motion appearance. Our method first converts motion clip into a form of clausal language that represents geometrical relations between body parts and their temporal relationship. A retrieval rule is then learned from the set of manually classified examples using inductive logic programming (ILP). ILP automatically discovers the essential rule in the same clausal form with a user-defined hypothesis-testing procedure. All motions are indexed using this clausal language, and the desired clips are retrieved by subsequence matching using the rule. Such rule-based retrieval offers reasonable performance and the rule can be intuitively edited in the same language form. Consequently, our method enables efficient and flexible search from a large dataset with simple query language.

  8. Importance of Fe and Mn Pipe Deposits to Lead and Copper Rule Compliance - abstract

    EPA Science Inventory

    When Madison, WI exceeded the lead Action Level in 1992, residential and off-line tests suggested that lead release into the water was more complex than a lead solubility mechanism. The water utility chose to address the Lead and Copper Rule (LCR) exceedance by implementing full ...

  9. A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.

    PubMed

    Siri, Benoît; Berry, Hugues; Cessac, Bruno; Delord, Bruno; Quoy, Mathias

    2008-12-01

    We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.

  10. Learning the Rules of the Game

    ERIC Educational Resources Information Center

    Smith, Donald A.

    2018-01-01

    Games have often been used in the classroom to teach physics ideas and concepts, but there has been less published on games that can be used to teach scientific thinking. D. Maloney and M. Masters describe an activity in which students attempt to infer rules to a game from a history of moves, but the students do not actually play the game. Giving…

  11. A grounded theory of abstraction in artificial intelligence.

    PubMed Central

    Zucker, Jean-Daniel

    2003-01-01

    In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author's view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed. PMID:12903672

  12. A grounded theory of abstraction in artificial intelligence.

    PubMed

    Zucker, Jean-Daniel

    2003-07-29

    In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another while preserving useful properties. Abstraction has been mainly studied in problem solving, theorem proving, knowledge representation (in particular for spatial and temporal reasoning) and machine learning. In such contexts, abstraction is defined as a mapping between formalisms that reduces the computational complexity of the task at stake. By analysing the notion of abstraction from an information quantity point of view, we pinpoint the differences and the complementary role of reformulation and abstraction in any representation change. We contribute to extending the existing semantic theories of abstraction to be grounded on perception, where the notion of information quantity is easier to characterize formally. In the author's view, abstraction is best represented using abstraction operators, as they provide semantics for classifying different abstractions and support the automation of representation changes. The usefulness of a grounded theory of abstraction in the cartography domain is illustrated. Finally, the importance of explicitly representing abstraction for designing more autonomous and adaptive systems is discussed.

  13. Discrimination theory of rule-governed behavior

    PubMed Central

    Cerutti, Daniel T.

    1989-01-01

    In rule-governed behavior, previously established elementary discriminations are combined in complex instructions and thus result in complex behavior. Discriminative combining and recombining of responses produce behavior with characteristics differing from those of behavior that is established through the effects of its direct consequences. For example, responding in instructed discrimination may be occasioned by discriminative stimuli that are temporally and situationally removed from the circumstances under which the discrimination is instructed. The present account illustrates properties of rule-governed behavior with examples from research in instructional control and imitation learning. Units of instructed behavior, circumstances controlling compliance with instructions, and rule-governed problem solving are considered. PMID:16812579

  14. Diverse strategy-learning styles promote cooperation in evolutionary spatial prisoner's dilemma game

    NASA Astrophysics Data System (ADS)

    Liu, Run-Ran; Jia, Chun-Xiao; Rong, Zhihai

    2015-11-01

    Observational learning and practice learning are two important learning styles and play important roles in our information acquisition. In this paper, we study a spacial evolutionary prisoner's dilemma game, where players can choose the observational learning rule or the practice learning rule when updating their strategies. In the proposed model, we use a parameter p controlling the preference of players choosing the observational learning rule, and found that there exists an optimal value of p leading to the highest cooperation level, which indicates that the cooperation can be promoted by these two learning rules collaboratively and one single learning rule is not favor the promotion of cooperation. By analysing the dynamical behavior of the system, we find that the observational learning rule can make the players residing on cooperative clusters more easily realize the bad sequence of mutual defection. However, a too high observational learning probability suppresses the players to form compact cooperative clusters. Our results highlight the importance of a strategy-updating rule, more importantly, the observational learning rule in the evolutionary cooperation.

  15. Automated rule-base creation via CLIPS-Induce

    NASA Technical Reports Server (NTRS)

    Murphy, Patrick M.

    1994-01-01

    Many CLIPS rule-bases contain one or more rule groups that perform classification. In this paper we describe CLIPS-Induce, an automated system for the creation of a CLIPS classification rule-base from a set of test cases. CLIPS-Induce consists of two components, a decision tree induction component and a CLIPS production extraction component. ID3, a popular decision tree induction algorithm, is used to induce a decision tree from the test cases. CLIPS production extraction is accomplished through a top-down traversal of the decision tree. Nodes of the tree are used to construct query rules, and branches of the tree are used to construct classification rules. The learned CLIPS productions may easily be incorporated into a large CLIPS system that perform tasks such as accessing a database or displaying information.

  16. Paper and Symposia Abstracts, 1972 Annual Meeting.

    ERIC Educational Resources Information Center

    Cunningham, Donald J., Ed.

    This compendium contains abstracts of approximately 700 papers which are classified under administration, curriculum and objectives, instruction and learning, measurement and research methodology, counseling and human development, history and historiography, social context of education, school evaluation and program development, or special…

  17. ARI Basic Research Program FY 1999-2000

    DTIC Science & Technology

    1999-06-01

    visual cues, reinforcement, and instruction concerning abstract , general rules. In our future research, we plan to examine the learning of novel...Watch, • Graduate student apprenticeship program - Consortium Research Fellows Program- with the Consortium of Metropolitan Washington Universities...do learn complex rules involving different levels of abstraction when given sufficient specific examples but that they also benefit from explicit

  18. Switching between Abstract Rules Reflects Disease Severity but Not Dopaminergic Status in Parkinson's Disease

    ERIC Educational Resources Information Center

    Kehagia, Angie A.; Cools, Roshan; Barker, Roger A.; Robbins, Trevor W.

    2009-01-01

    This study sought to disambiguate the impact of Parkinson's disease (PD) on cognitive control as indexed by task set switching, by addressing discrepancies in the literature pertaining to disease severity and paradigm heterogeneity. A task set is governed by a rule that determines how relevant stimuli (stimulus set) map onto specific responses…

  19. Rules, Technique, and Practical Knowledge: A Wittgensteinian Exploration of Vocational Learning

    ERIC Educational Resources Information Center

    Winch, Christopher

    2006-01-01

    In this essay, Christopher Winch explores the relevance of Ludwig Wittgenstein's account of rule-following to vocational education with particular reference to the often-made claim that any account of an activity in terms of rule-following implies rigidity and inflexibility. He argues that most rule-following is only successful when it involves a…

  20. Supporting Abstraction Processes in Problem Solving through Pattern-Oriented Instruction

    ERIC Educational Resources Information Center

    Muller, Orna; Haberman, Bruria

    2008-01-01

    Abstraction is a major concept in computer science and serves as a powerful tool in software development. Pattern-oriented instruction (POI) is a pedagogical approach that incorporates patterns in an introductory computer science course in order to structure the learning of algorithmic problem solving. This paper examines abstraction processes in…

  1. Rules of (Student) Engagement

    ERIC Educational Resources Information Center

    Buskist, William; Busler, Jessica N.; Kirby, Lauren A. J.

    2018-01-01

    Teachers often think of student engagement in terms of hands-on activities that get students involved in their courses. They seldom consider the larger aspects of the teaching--learning environment that often influence the extent to which students are willing to become engaged in their coursework. In this chapter, we describe five "rules of…

  2. Rule groupings: An approach towards verification of expert systems

    NASA Technical Reports Server (NTRS)

    Mehrotra, Mala

    1991-01-01

    Knowledge-based expert systems are playing an increasingly important role in NASA space and aircraft systems. However, many of NASA's software applications are life- or mission-critical and knowledge-based systems do not lend themselves to the traditional verification and validation techniques for highly reliable software. Rule-based systems lack the control abstractions found in procedural languages. Hence, it is difficult to verify or maintain such systems. Our goal is to automatically structure a rule-based system into a set of rule-groups having a well-defined interface to other rule-groups. Once a rule base is decomposed into such 'firewalled' units, studying the interactions between rules would become more tractable. Verification-aid tools can then be developed to test the behavior of each such rule-group. Furthermore, the interactions between rule-groups can be studied in a manner similar to integration testing. Such efforts will go a long way towards increasing our confidence in the expert-system software. Our research efforts address the feasibility of automating the identification of rule groups, in order to decompose the rule base into a number of meaningful units.

  3. Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule.

    PubMed

    Beyeler, Michael; Dutt, Nikil D; Krichmar, Jeffrey L

    2013-12-01

    Understanding how the human brain is able to efficiently perceive and understand a visual scene is still a field of ongoing research. Although many studies have focused on the design and optimization of neural networks to solve visual recognition tasks, most of them either lack neurobiologically plausible learning rules or decision-making processes. Here we present a large-scale model of a hierarchical spiking neural network (SNN) that integrates a low-level memory encoding mechanism with a higher-level decision process to perform a visual classification task in real-time. The model consists of Izhikevich neurons and conductance-based synapses for realistic approximation of neuronal dynamics, a spike-timing-dependent plasticity (STDP) synaptic learning rule with additional synaptic dynamics for memory encoding, and an accumulator model for memory retrieval and categorization. The full network, which comprised 71,026 neurons and approximately 133 million synapses, ran in real-time on a single off-the-shelf graphics processing unit (GPU). The network was constructed on a publicly available SNN simulator that supports general-purpose neuromorphic computer chips. The network achieved 92% correct classifications on MNIST in 100 rounds of random sub-sampling, which is comparable to other SNN approaches and provides a conservative and reliable performance metric. Additionally, the model correctly predicted reaction times from psychophysical experiments. Because of the scalability of the approach and its neurobiological fidelity, the current model can be extended to an efficient neuromorphic implementation that supports more generalized object recognition and decision-making architectures found in the brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. ChemStable: a web server for rule-embedded naïve Bayesian learning approach to predict compound stability.

    PubMed

    Liu, Zhihong; Zheng, Minghao; Yan, Xin; Gu, Qiong; Gasteiger, Johann; Tijhuis, Johan; Maas, Peter; Li, Jiabo; Xu, Jun

    2014-09-01

    Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H2O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the naïve Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naïve Bayesian learning from the data set, each ACF is assigned with an expected stable probability (p(s)) and an unstable probability (p(uns)). 13,340 ACFs, together with their p(s) and p(uns) data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p(s) and p(uns) values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes' theorem, based upon the p(s) and p(uns) values of the compound ACFs. We were able to achieve performance with an AUC value of 84% and a tenfold cross validation accuracy of 76.5%. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.

  5. Predicting Semantic Changes in Abstraction in Tutor Responses to Students

    ERIC Educational Resources Information Center

    Lipschultz, Michael; Litman, Diane; Katz, Sandra; Albacete, Patricia; Jordan, Pamela

    2014-01-01

    Post-problem reflective tutorial dialogues between human tutors and students are examined to predict when the tutor changed the level of abstraction from the student's preceding turn (i.e., used more general terms or more specific terms); such changes correlate with learning. Prior work examined lexical changes in abstraction. In this work, we…

  6. Abstracts of Research Papers 1977 AAHPER Convention.

    ERIC Educational Resources Information Center

    Sage, George H., Ed.

    This volume of abstracts describes papers written on the following topics: (1) Strength Physiology; (2) Learning Disabilities (motor); (3) Physiology - General; (4) Work Capacity; (5) Measurement and Recreation; (6) Biomechanics; (7) Professional Preparation (physical education); (8) Muscle Performance; (9) Sociology of Sport; (10) History of…

  7. A Proposed Multimedia Cone of Abstraction: Updating a Classic Instructional Design Theory

    ERIC Educational Resources Information Center

    Baukal, Charles E.; Ausburn, Floyd B.; Ausburn, Lynna J.

    2013-01-01

    Advanced multimedia techniques offer significant learning potential for students. Dale (1946, 1954, 1969) developed a Cone of Experience (CoE) which is a hierarchy of learning experiences ranging from direct participation to abstract symbolic expression. This paper updates the CoE for today's technology and learning context, specifically focused…

  8. When global structure "Explains Away" local grammar: a Bayesian account of rule-induction in tone sequences.

    PubMed

    Dawson, Colin; Gerken, Louann

    2011-09-01

    While many constraints on learning must be relatively experience-independent, past experience provides a rich source of guidance for subsequent learning. Discovering structure in some domain can inform a learner's future hypotheses about that domain. If a general property accounts for particular sub-patterns, a rational learner should not stipulate separate explanations for each detail without additional evidence, as the general structure has "explained away" the original evidence. In a grammar-learning experiment using tone sequences, manipulating learners' prior exposure to a tone environment affects their sensitivity to the grammar-defining feature, in this case consecutive repeated tones. Grammar-learning performance is worse if context melodies are "smooth" -- when small intervals occur more than large ones -- as Smoothness is a general property accounting for a high rate of repetition. We present an idealized Bayesian model as a "best case" benchmark for learning repetition grammars. When context melodies are Smooth, the model places greater weight on the small-interval constraint, and does not learn the repetition rule as well as when context melodies are not Smooth, paralleling the human learners. These findings support an account of abstract grammar-induction in which learners rationally assess the statistical evidence for underlying structure based on a generative model of the environment. Copyright © 2010 Elsevier B.V. All rights reserved.

  9. Video Self-Modeling to Teach Classroom Rules to Two Students with Asperger's

    ERIC Educational Resources Information Center

    Lang, Russell; Shogren, Karrie A.; Machalicek, Wendy; Rispoli, Mandy; O'Reilly, Mark; Baker, Sonia; Regester, April

    2009-01-01

    Classroom rules are an integral part of classroom management. Children with Asperger's may require systematic instruction to learn classroom rules, but may be placed in classrooms in which the rules are not explicitly taught. A multiple baseline design across students with probes for maintenance after the intervention ceased was used to evaluate…

  10. A Simple Computer-Aided Three-Dimensional Molecular Modeling for the Octant Rule

    ERIC Educational Resources Information Center

    Kang, Yinan; Kang, Fu-An

    2011-01-01

    The Moffitt-Woodward-Moscowitz-Klyne-Djerassi octant rule is one of the most successful empirical rules in organic chemistry. However, the lack of a simple effective modeling method for the octant rule in the past 50 years has posed constant difficulties for researchers, teachers, and students, particularly the young generations, to learn and…

  11. The impact of safety rules revisions on safety culture, incident rates, and liability claims in the U.S. railroad industry : a summary of lessons-learned

    DOT National Transportation Integrated Search

    2003-01-01

    The Federal Railroad Administration (FRA) Human Factors Research and Development (R&D) Program sponsored a lessons-learned study to examine the impact of safety rules revision on safety culture, incident rates, and liability claims in the railroad in...

  12. Adaptation and Extension of the Framework of Reducing Abstraction in the Case of Differential Equations

    ERIC Educational Resources Information Center

    Raychaudhuri, Debasree

    2014-01-01

    Although there is no consensus in regard to a unique meaning for abstraction, there is a recognition of the existence of several theories of abstraction, and that the ability to abstract is imperative to learning and doing meaningful mathematics. The theory of "reducing abstraction" maps the abstract nature of mathematics to the nature…

  13. Ellipsoidal fuzzy learning for smart car platoons

    NASA Astrophysics Data System (ADS)

    Dickerson, Julie A.; Kosko, Bart

    1993-12-01

    A neural-fuzzy system combined supervised and unsupervised learning to find and tune the fuzzy-rules. An additive fuzzy system approximates a function by covering its graph with fuzzy rules. A fuzzy rule patch can take the form of an ellipsoid in the input-output space. Unsupervised competitive learning found the statistics of data clusters. The covariance matrix of each synaptic quantization vector defined on ellipsoid centered at the centroid of the data cluster. Tightly clustered data gave smaller ellipsoids or more certain rules. Sparse data gave larger ellipsoids or less certain rules. Supervised learning tuned the ellipsoids to improve the approximation. The supervised neural system used gradient descent to find the ellipsoidal fuzzy patches. It locally minimized the mean-squared error of the fuzzy approximation. Hybrid ellipsoidal learning estimated the control surface for a smart car controller.

  14. Explanation-based learning in infancy.

    PubMed

    Baillargeon, Renée; DeJong, Gerald F

    2017-10-01

    In explanation-based learning (EBL), domain knowledge is leveraged in order to learn general rules from few examples. An explanation is constructed for initial exemplars and is then generalized into a candidate rule that uses only the relevant features specified in the explanation; if the rule proves accurate for a few additional exemplars, it is adopted. EBL is thus highly efficient because it combines both analytic and empirical evidence. EBL has been proposed as one of the mechanisms that help infants acquire and revise their physical rules. To evaluate this proposal, 11- and 12-month-olds (n = 260) were taught to replace their current support rule (that an object is stable when half or more of its bottom surface is supported) with a more sophisticated rule (that an object is stable when half or more of the entire object is supported). Infants saw teaching events in which asymmetrical objects were placed on a base, followed by static test displays involving a novel asymmetrical object and a novel base. When the teaching events were designed to facilitate EBL, infants learned the new rule with as few as two (12-month-olds) or three (11-month-olds) exemplars. When the teaching events were designed to impede EBL, however, infants failed to learn the rule. Together, these results demonstrate that even infants, with their limited knowledge about the world, benefit from the knowledge-based approach of EBL.

  15. 75 FR 13656 - Proposed Collection; Comment Request for Revenue Procedure RP-125212-09, Rules for Certain Rental...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-03-22

    ... Revenue Procedure RP- 125212-09, Rules for Certain Rental Real Estate Activities AGENCY: Internal Revenue..., Rules for Certain Rental Real Estate Activities. DATES: Written comments should be received on or before... . SUPPLEMENTARY INFORMATION: Title: RP-125212-09 Rules for Certain Rental Real Estate Activities. Abstract: This...

  16. Increasing the thermal stability of cellulase C using rules learned from thermophilic proteins: a pilot study.

    PubMed

    Németh, Attila; Kamondi, Szilárd; Szilágyi, András; Magyar, Csaba; Kovári, Zoltán; Závodszky, Péter

    2002-05-02

    Some structural features underlying the increased thermostability of enzymes from thermophilic organisms relative to their homologues from mesophiles are known from earlier studies. We used cellulase C from Clostridium thermocellum to test whether thermostability can be increased by mutations designed using rules learned from thermophilic proteins. Cellulase C has a TIM barrel fold with an additional helical subdomain. We designed and produced a number of mutants with the aim to increase its thermostability. Five mutants were designed to create new electrostatic interactions. They all retained catalytic activity but exhibited decreased thermostability relative to the wild-type enzyme. Here, the stabilizing contributions are obviously smaller than the destabilization caused by the introduction of the new side chains. In another mutant, the small helical subdomain was deleted. This mutant lost activity but its melting point was only 3 degrees C lower than that of the wild-type enzyme, which suggests that the subdomain is an independent folding unit and is important for catalytic function. A double mutant was designed to introduce a new disulfide bridge into the enzyme. This mutant is active and has an increased stability (deltaT(m)=3 degrees C, delta(deltaG(u))=1.73 kcal/mol) relative to the wild-type enzyme. Reduction of the disulfide bridge results in destabilization and an altered thermal denaturation behavior. We conclude that rules learned from thermophilic proteins cannot be used in a straightforward way to increase the thermostability of a protein. Creating a crosslink such as a disulfide bond is a relatively sure-fire method but the stabilization may be smaller than calculated due to coupled destabilizing effects.

  17. The Effects of the Concrete-Representational-Abstract Integration Strategy on the Ability of Students with Learning Disabilities to Multiply Linear Expressions within Area Problems

    ERIC Educational Resources Information Center

    Strickland, Tricia K.; Maccini, Paula

    2013-01-01

    We examined the effects of the Concrete-Representational-Abstract Integration strategy on the ability of secondary students with learning disabilities to multiply linear algebraic expressions embedded within contextualized area problems. A multiple-probe design across three participants was used. Results indicated that the integration of the…

  18. Toward a dual-learning systems model of speech category learning

    PubMed Central

    Chandrasekaran, Bharath; Koslov, Seth R.; Maddox, W. T.

    2014-01-01

    More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article, we describe a neurobiologically constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, unidimensional rules to more complex, reflexive, multi-dimensional rules. In a second application, we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions. PMID:25132827

  19. The transfer of category knowledge by macaques (Macaca mulatta) and humans (Homo sapiens).

    PubMed

    Zakrzewski, Alexandria C; Church, Barbara A; Smith, J David

    2018-02-01

    Cognitive psychologists distinguish implicit, procedural category learning (stimulus-response associations learned outside declarative cognition) from explicit-declarative category learning (conscious category rules). These systems are dissociated by category learning tasks with either a multidimensional, information-integration (II) solution or a unidimensional, rule-based (RB) solution. In the present experiments, humans and two monkeys learned II and RB category tasks fostering implicit and explicit learning, respectively. Then they received occasional transfer trials-never directly reinforced-drawn from untrained regions of the stimulus space. We hypothesized that implicit-procedural category learning-allied to associative learning-would transfer weakly because it is yoked to the training stimuli. This result was confirmed for humans and monkeys. We hypothesized that explicit category learning-allied to abstract category rules-would transfer robustly. This result was confirmed only for humans. That is, humans displayed explicit category knowledge that transferred flawlessly. Monkeys did not. This result illuminates the distinctive abstractness, stimulus independence, and representational portability of humans' explicit category rules. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  20. Comparison of rule induction, decision trees and formal concept analysis approaches for classification

    NASA Astrophysics Data System (ADS)

    Kotelnikov, E. V.; Milov, V. R.

    2018-05-01

    Rule-based learning algorithms have higher transparency and easiness to interpret in comparison with neural networks and deep learning algorithms. These properties make it possible to effectively use such algorithms to solve descriptive tasks of data mining. The choice of an algorithm depends also on its ability to solve predictive tasks. The article compares the quality of the solution of the problems with binary and multiclass classification based on the experiments with six datasets from the UCI Machine Learning Repository. The authors investigate three algorithms: Ripper (rule induction), C4.5 (decision trees), In-Close (formal concept analysis). The results of the experiments show that In-Close demonstrates the best quality of classification in comparison with Ripper and C4.5, however the latter two generate more compact rule sets.

  1. Development of Abstract Grammatical Categorization in Infants

    ERIC Educational Resources Information Center

    Cyr, Marilyn; Shi, Rushen

    2013-01-01

    This study examined abstract syntactic categorization in infants, using the case of grammatical gender. Ninety-six French-learning 14-, 17-, 20-, and 30-month-olds completed the study. In a preferential looking procedure infants were tested on their generalized knowledge of grammatical gender involving pseudonouns and gender-marking determiners.…

  2. Who Knows? Metacognitive Social Learning Strategies.

    PubMed

    Heyes, Cecilia

    2016-03-01

    To make good use of learning from others (social learning), we need to learn from the right others; from agents who know better than we do. Research on social learning strategies (SLSs) has identified rules that focus social learning on the right agents, and has shown that the behaviour of many animals conforms to these rules. However, it has not asked what the rules are made of, that is, about the cognitive processes implementing SLSs. Here, I suggest that most SLSs depend on domain-general, sensorimotor processes. However, some SLSs have the characteristics tacitly ascribed to all of them. These metacognitive SLSs represent 'who knows' in a conscious, reportable way, and have the power to promote cultural evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Targeted training of the decision rule benefits rule-guided behavior in Parkinson's disease.

    PubMed

    Ell, Shawn W

    2013-12-01

    The impact of Parkinson's disease (PD) on rule-guided behavior has received considerable attention in cognitive neuroscience. The majority of research has used PD as a model of dysfunction in frontostriatal networks, but very few attempts have been made to investigate the possibility of adapting common experimental techniques in an effort to identify the conditions that are most likely to facilitate successful performance. The present study investigated a targeted training paradigm designed to facilitate rule learning and application using rule-based categorization as a model task. Participants received targeted training in which there was no selective-attention demand (i.e., stimuli varied along a single, relevant dimension) or nontargeted training in which there was selective-attention demand (i.e., stimuli varied along a relevant dimension as well as an irrelevant dimension). Following training, all participants were tested on a rule-based task with selective-attention demand. During the test phase, PD patients who received targeted training performed similarly to control participants and outperformed patients who did not receive targeted training. As a preliminary test of the generalizability of the benefit of targeted training, a subset of the PD patients were tested on the Wisconsin card sorting task (WCST). PD patients who received targeted training outperformed PD patients who did not receive targeted training on several WCST performance measures. These data further characterize the contribution of frontostriatal circuitry to rule-guided behavior. Importantly, these data also suggest that PD patient impairment, on selective-attention-demanding tasks of rule-guided behavior, is not inevitable and highlight the potential benefit of targeted training.

  4. AVNM: A Voting based Novel Mathematical Rule for Image Classification.

    PubMed

    Vidyarthi, Ankit; Mittal, Namita

    2016-12-01

    In machine learning, the accuracy of the system depends upon classification result. Classification accuracy plays an imperative role in various domains. Non-parametric classifier like K-Nearest Neighbor (KNN) is the most widely used classifier for pattern analysis. Besides its easiness, simplicity and effectiveness characteristics, the main problem associated with KNN classifier is the selection of a number of nearest neighbors i.e. "k" for computation. At present, it is hard to find the optimal value of "k" using any statistical algorithm, which gives perfect accuracy in terms of low misclassification error rate. Motivated by the prescribed problem, a new sample space reduction weighted voting mathematical rule (AVNM) is proposed for classification in machine learning. The proposed AVNM rule is also non-parametric in nature like KNN. AVNM uses the weighted voting mechanism with sample space reduction to learn and examine the predicted class label for unidentified sample. AVNM is free from any initial selection of predefined variable and neighbor selection as found in KNN algorithm. The proposed classifier also reduces the effect of outliers. To verify the performance of the proposed AVNM classifier, experiments are made on 10 standard datasets taken from UCI database and one manually created dataset. The experimental result shows that the proposed AVNM rule outperforms the KNN classifier and its variants. Experimentation results based on confusion matrix accuracy parameter proves higher accuracy value with AVNM rule. The proposed AVNM rule is based on sample space reduction mechanism for identification of an optimal number of nearest neighbor selections. AVNM results in better classification accuracy and minimum error rate as compared with the state-of-art algorithm, KNN, and its variants. The proposed rule automates the selection of nearest neighbor selection and improves classification rate for UCI dataset and manually created dataset. Copyright © 2016 Elsevier

  5. Dual-modality impairment of implicit learning of letter-strings versus color-patterns in patients with schizophrenia.

    PubMed

    Chiu, Ming-Jang; Liu, Kristina; Hsieh, Ming H; Hwu, Hai-Gwo

    2005-12-12

    Implicit learning was reported to be intact in schizophrenia using artificial grammar learning. However, emerging evidence indicates that artificial grammar learning is not a unitary process. The authors used dual coding stimuli and schizophrenia clinical symptom dimensions to re-evaluate the effect of schizophrenia on various components of artificial grammar learning. Letter string and color pattern artificial grammar learning performances were compared between 63 schizophrenic patients and 27 comparison subjects. Four symptom dimensions derived from a Chinese Positive and Negative Symptom Scale ratings were correlated with patients' artificial grammar implicit learning performances along the two stimulus dimensions. Patients' explicit memory performances were assessed by verbal paired associates and visual reproduction subtests of the Wechsler Memory Scales Revised Version to provide a contrast to their implicit memory function. Schizophrenia severely hindered color pattern artificial grammar learning while the disease affected lexical string artificial grammar learning to a lesser degree after correcting the influences from age, education and the performance of explicit memory function of both verbal and visual modalities. Both learning performances correlated significantly with the severity of patients' schizophrenic clinical symptom dimensions that reflect poor abstract thinking, disorganized thinking, and stereotyped thinking. The results of this study suggested that schizophrenia affects various mechanisms of artificial grammar learning differently. Implicit learning, knowledge acquisition in the absence of conscious awareness, is not entirely intact in patients with schizophrenia. Schizophrenia affects implicit learning through an impairment of the ability of making abstractions from rules and at least in part decreasing the capacity for perceptual learning.

  6. Using Group Explorer in Teaching Abstract Algebra

    ERIC Educational Resources Information Center

    Schubert, Claus; Gfeller, Mary; Donohue, Christopher

    2013-01-01

    This study explores the use of Group Explorer in an undergraduate mathematics course in abstract algebra. The visual nature of Group Explorer in representing concepts in group theory is an attractive incentive to use this software in the classroom. However, little is known about students' perceptions on this technology in learning concepts in…

  7. Rule-governed behavior and behavioral anthropology

    PubMed Central

    Malott, Richard W.

    1988-01-01

    According to cultural materialism, cultural practices result from the materialistic outcomes of those practices, not from sociobiological, mentalistic, or mystical predispositions (e.g., Hindus worship cows because, in the long run, that worship results in more food, not less food). However, according to behavior analysis, such materialistic outcomes do not reinforce or punish the cultural practices, because such outcomes are too delayed, too improbable, or individually too small to directly reinforce or punish the cultural practices (e.g., the food increase is too delayed to reinforce the cow worship). Therefore, the molar, materialistic contingencies need the support of molecular, behavioral contingencies. And according to the present theory of rule-governed behavior, the statement of rules describing those molar, materialistic contingencies can establish the needed molecular contingencies. Given the proper behavioral history, such rule statements combine with noncompliance to produce a learned aversive condition (often labeled fear, anxiety, or guilt). The termination of this aversive condition reinforces compliance, just as its presentation punishes noncompliance (e.g., the termination of guilt reinforces the tending to a sick cow). In addition, supernatural rules often supplement these materialistic rules. Furthermore, the production of both materialistic and supernatural rules needs cultural designers who understand the molar, materialistic contingencies. PMID:22478012

  8. Dollars and Deadlines: Rule Reforms in Short Time Frames

    EPA Science Inventory

    ABSTRACT: In "At Last! Aye, and there's the Rub" (i.e., the target article for this commentary), Capron describes challenges related to the timing for development, adoption, and revision of the Common Rule. Specifically, what the National Commission thought would be a...

  9. Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA.

    PubMed

    Ngo, T-D; Tran, T-D; Le, M-T; Thai, K-M

    2016-09-01

    The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acctrain-diverse = 84%; accinternal-test = 92% and accexternal-test = 100%). For ligand promiscuity between P-gp and NorA, perceptual maps and pharmacophore models were generated for the detection of rules and features. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening in an attempt to restore drug sensitivity in cancer cells and bacteria.

  10. Learning and coding in biological neural networks

    NASA Astrophysics Data System (ADS)

    Fiete, Ila Rani

    How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and

  11. When more is less: Feedback effects in perceptual category learning

    PubMed Central

    Maddox, W. Todd; Love, Bradley C.; Glass, Brian D.; Filoteo, J. Vincent

    2008-01-01

    Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether their response was correct or incorrect, but are not informed of the correct category assignment. With full feedback subjects are informed of the correctness of their response and are also informed of the correct category assignment. An examination of the distinct neural circuits that subserve rule-based and information-integration category learning leads to the counterintuitive prediction that full feedback should facilitate rule-based learning but should also hinder information-integration learning. This prediction was supported in the experiment reported below. The implications of these results for theories of learning are discussed. PMID:18455155

  12. Learning in Artificial Neural Systems

    NASA Technical Reports Server (NTRS)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  13. Can Abstract Concepts Be Taught to All Students?

    ERIC Educational Resources Information Center

    Fields, Steve

    A review of the literature was conducted to determine if there are instructional treatments that are effective at fostering the learning (conceptualization) of abstract concepts and what personal and/or cognitive attributes favor conceptualization. Areas addressed include: reporting relative effectiveness of findings (including the reporting of…

  14. Mechanisms of Hierarchical Reinforcement Learning in Corticostriatal Circuits 1: Computational Analysis

    PubMed Central

    Badre, David

    2012-01-01

    Growing evidence suggests that the prefrontal cortex (PFC) is organized hierarchically, with more anterior regions having increasingly abstract representations. How does this organization support hierarchical cognitive control and the rapid discovery of abstract action rules? We present computational models at different levels of description. A neural circuit model simulates interacting corticostriatal circuits organized hierarchically. In each circuit, the basal ganglia gate frontal actions, with some striatal units gating the inputs to PFC and others gating the outputs to influence response selection. Learning at all of these levels is accomplished via dopaminergic reward prediction error signals in each corticostriatal circuit. This functionality allows the system to exhibit conditional if–then hypothesis testing and to learn rapidly in environments with hierarchical structure. We also develop a hybrid Bayesian-reinforcement learning mixture of experts (MoE) model, which can estimate the most likely hypothesis state of individual participants based on their observed sequence of choices and rewards. This model yields accurate probabilistic estimates about which hypotheses are attended by manipulating attentional states in the generative neural model and recovering them with the MoE model. This 2-pronged modeling approach leads to multiple quantitative predictions that are tested with functional magnetic resonance imaging in the companion paper. PMID:21693490

  15. Algerian Abstract

    NASA Image and Video Library

    2017-12-08

    Algerian Abstract - April 8th, 1985 Description: What look like pale yellow paint streaks slashing through a mosaic of mottled colors are ridges of wind-blown sand that make up Erg Iguidi, an area of ever-shifting sand dunes extending from Algeria into Mauritania in northwestern Africa. Erg Iguidi is one of several Saharan ergs, or sand seas, where individual dunes often surpass 500 meters-nearly a third of a mile-in both width and height. Credit: USGS/NASA/Landsat 5 To learn more about the Landsat satellite go to: landsat.gsfc.nasa.gov/ NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Join us on Facebook

  16. Processing advantages for consonance: A comparison between rats (Rattus norvegicus) and humans (Homo sapiens).

    PubMed

    Crespo-Bojorque, Paola; Toro, Juan M

    2016-05-01

    Consonance is a salient perceptual feature in harmonic music associated with pleasantness. Besides being deeply rooted in how we experience music, research suggests consonant intervals are more easily processed than dissonant intervals. In the present work we explore from a comparative perspective if such processing advantage extends to more complex tasks such as the detection of abstract rules. We ran experiments on rule learning over consonant and dissonant intervals with nonhuman animals and human participants. Results show differences across species regarding the extent to which they benefit from differences in consonance. Animals learn abstract rules with the same ease independently of whether they are implemented over consonant intervals (Experiment 1), dissonant intervals (Experiment 2), or over a combination of them (Experiment 3). Humans, on the contrary, learn an abstract rule better when it is implemented over consonant (Experiment 4) than over dissonant intervals (Experiment 5). Moreover, their performance improves when there is a mapping between abstract categories defining a rule and consonant and dissonant intervals (Experiments 6 and 7). Results suggest that for humans, consonance might be used as a perceptual anchor for other cognitive processes as to facilitate the detection of abstract patterns. Lacking extensive experience with harmonic stimuli, nonhuman animals tested here do not seem to benefit from a processing advantage for consonant intervals. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  17. Discrimination Learning, Reversal, and Set-Shifting in First-Episode Schizophrenia: Stability Over Six Years and Specific Associations with Medication Type and Disorganization Syndrome

    PubMed Central

    Leeson, Verity C.; Robbins, Trevor W.; Matheson, Elizabeth; Hutton, Samuel B.; Ron, María A.; Barnes, Thomas R.E.; Joyce, Eileen M.

    2009-01-01

    Background The intradimensional/extradimensional (IDED) task assesses different forms of learning from feedback. Limited evidence suggests that attentional set-shifting deteriorates over time in schizophrenia. We tested this hypothesis and examined the specificity of learning impairments identified by this task. Method Two hundred sixty-two first-episode patients and 76 healthy control subjects, matched for age and premorbid IQ, were tested; 104 patients and 25 control subjects were reassessed 1 and 3 years later, and 31 patients were reassessed additionally 6 years later. Results Patients showed impaired set-shifting that correlated with current IQ and working memory, but there were no impairments when subgroups were matched on current IQ. In contrast, patients showed marked impairments in rule reversal learning that survived correction for IQ, were present in the context of intact rule abstraction, and correlated with disorganization symptoms. Patients prescribed second-generation antipsychotics were worse on set-shifting compared with first-generation, a finding not explained by demographic data, illness characteristics, or IQ. Patients and control subjects showed stable IDED performance over the first 6 years of illness, although set-shifting was inconsistent over the first year. Those with residual negative symptoms were more likely to fail the set-shifting stage at follow-up. Conclusions First-episode schizophrenia patients can learn and generalize rules but are inflexible when rules change, reflecting reduced responsiveness to negative feedback and difficulty in switching attention. Rule-reversal is a promising target for translational studies, because it is specific, clinically relevant, and might reflect orbitofrontal dysfunction. Set-shifting is related to poor function more generally but might be sensitive to medication effects and valuable for clinical trials. PMID:19576575

  18. Beyond the Abstractions!: Adult Education Research from Idealism to Critical Social Science

    ERIC Educational Resources Information Center

    Olesen, Henning Salling

    2006-01-01

    The anniversary of the "International Journal of Lifelong Education" can participate in a conceptual landslide from lifelong education to lifelong learning. Contemporary discourses of lifelong learning etc. are abstractions behind which new functions and agendas for adult education are set. The ideological discourse of recent policies…

  19. Incidental Learning of Melodic Structure of North Indian Music.

    PubMed

    Rohrmeier, Martin; Widdess, Richard

    2017-07-01

    Musical knowledge is largely implicit. It is acquired without awareness of its complex rules, through interaction with a large number of samples during musical enculturation. Whereas several studies explored implicit learning of mostly abstract and less ecologically valid features of Western music, very little work has been done with respect to ecologically valid stimuli as well as non-Western music. The present study investigated implicit learning of modal melodic features in North Indian classical music in a realistic and ecologically valid way. It employed a cross-grammar design, using melodic materials from two modes (rāgas) that use the same scale. Findings indicated that Western participants unfamiliar with Indian music incidentally learned to identify distinctive features of each mode. Confidence ratings suggest that participants' performance was consistently correlated with confidence, indicating that they became aware of whether they were right in their responses; that is, they possessed explicit judgment knowledge. Altogether our findings show incidental learning in a realistic ecologically valid context during only a very short exposure, they provide evidence that incidental learning constitutes a powerful mechanism that plays a fundamental role in musical acquisition. Copyright © 2016 Cognitive Science Society, Inc.

  20. Simple modification of Oja rule limits L1-norm of weight vector and leads to sparse connectivity.

    PubMed

    Aparin, Vladimir

    2012-03-01

    This letter describes a simple modification of the Oja learning rule, which asymptotically constrains the L1-norm of an input weight vector instead of the L2-norm as in the original rule. This constraining is local as opposed to commonly used instant normalizations, which require the knowledge of all input weights of a neuron to update each one of them individually. The proposed rule converges to a weight vector that is sparser (has more zero weights) than the vector learned by the original Oja rule with or without the zero bound, which could explain the developmental synaptic pruning.

  1. Abstracts of Review Articles and Educational Materials in Physiology

    ERIC Educational Resources Information Center

    Physiology Teacher, 1977

    1977-01-01

    Contained are 99 abstracts of review articles, texts, books, manuals, learning programs, and audiovisual material used in teaching physiology. Specific fields include cell physiology, circulation, comparative physiology, development and aging, endocrinology and metabolism, environmental and exercise physiology, gastrointestinal physiology, muscle…

  2. Content Abstract Classification Using Naive Bayes

    NASA Astrophysics Data System (ADS)

    Latif, Syukriyanto; Suwardoyo, Untung; Aldrin Wihelmus Sanadi, Edwin

    2018-03-01

    This study aims to classify abstract content based on the use of the highest number of words in an abstract content of the English language journals. This research uses a system of text mining technology that extracts text data to search information from a set of documents. Abstract content of 120 data downloaded at www.computer.org. Data grouping consists of three categories: DM (Data Mining), ITS (Intelligent Transport System) and MM (Multimedia). Systems built using naive bayes algorithms to classify abstract journals and feature selection processes using term weighting to give weight to each word. Dimensional reduction techniques to reduce the dimensions of word counts rarely appear in each document based on dimensional reduction test parameters of 10% -90% of 5.344 words. The performance of the classification system is tested by using the Confusion Matrix based on comparative test data and test data. The results showed that the best classification results were obtained during the 75% training data test and 25% test data from the total data. Accuracy rates for categories of DM, ITS and MM were 100%, 100%, 86%. respectively with dimension reduction parameters of 30% and the value of learning rate between 0.1-0.5.

  3. Service Learning in the Middle Grades: Learning by Doing and Caring

    ERIC Educational Resources Information Center

    Farber, Katy; Bishop, Penny

    2018-01-01

    Although service learning has been documented as a promising pedagogy for middle grades learners, it remains the exception rather than the rule in many middle schools. This qualitative study examined fifth grade students' experience of a service-learning class. Using the tenets of service learning and experiential learning theory as the…

  4. Group learning versus local learning: Which is prefer for public cooperation?

    NASA Astrophysics Data System (ADS)

    Yang, Shi-Han; Song, Qi-Qing

    2018-01-01

    We study the evolution of cooperation in public goods games on various graphs, focusing on the effects that are brought by different kinds of strategy donors. This highlights a basic feature of a public good game, for which there exists a remarkable difference between the interactive players and the players who are imitated. A player can learn from all the groups where the player is a member or from the typically local nearest neighbors, and the results show that the group learning rules have better performance in promoting cooperation on many networks than the local learning rules. The heterogeneity of networks' degree may be an effective mechanism for harvesting the cooperation expectation in many cases, however, we find that heterogeneity does not definitely mean the high frequency of cooperators in a population under group learning rules. It was shown that cooperators always hardly evolve whenever the interaction and the replacement do not coincide for evolutionary pairwise dilemmas on graphs, while for PG games we find that breaking the symmetry is conducive to the survival of cooperators.

  5. Optimal Sequential Rules for Computer-Based Instruction.

    ERIC Educational Resources Information Center

    Vos, Hans J.

    1998-01-01

    Formulates sequential rules for adapting the appropriate amount of instruction to learning needs in the context of computer-based instruction. Topics include Bayesian decision theory, threshold and linear-utility structure, psychometric model, optimal sequential number of test questions, and an empirical example of sequential instructional…

  6. Playing by the Rules: Instruction and Acculturation in Role-Playing Games

    ERIC Educational Resources Information Center

    Neuenschwander, Bryn

    2008-01-01

    The open-ended, informal, and socially negotiated nature of role-playing games creates a distinct learning challenge for newcomers to the hobby. The explicit rules of the game provide only an incomplete framework for structuring the actions of players, and the expectations and mores of a given group will add other, unspoken rules that discourage…

  7. Language Learning in Preschool Children: An Embodied Learning Account

    ERIC Educational Resources Information Center

    Ionescu, Thea; Ilie, Adriana

    2018-01-01

    In Romanian preschool settings, there is a tendency to use abstract strategies in language-learning activities. The present study explored if strategies based on an embodied cognition approach facilitate learning more than traditional strategies that progress from concrete to abstract. Twenty-five children between 4 and 5 years of age listened to…

  8. Implicit learning and reading: insights from typical children and children with developmental dyslexia using the artificial grammar learning (AGL) paradigm.

    PubMed

    Pavlidou, Elpis V; Williams, Joanne M

    2014-07-01

    We examined implicit learning in school-aged children with and without developmental dyslexia based on the proposal that implicit learning plays a significant role in mastering fluent reading. We ran two experiments with 16 typically developing children (9 to 11-years-old) and 16 age-matched children with developmental dyslexia using the artificial grammar learning (AGL) paradigm. In Experiment 1 (non-transfer task), children were trained on stimuli that followed patterns (rules) unknown to them. Subsequently, they were asked to decide from a novel set which stimuli follow the same rules (grammaticality judgments). In Experiment 2 (transfer task), training and testing stimuli differed in their superficial characteristics but followed the same rules. Again, children were asked to make grammaticality judgments. Our findings expand upon previous research by showing that children with developmental dyslexia show difficulties in implicit learning that are most likely specific to higher-order rule-like learning. These findings are discussed in relation to current theories of developmental dyslexia and of implicit learning. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Neuromodulation of Prefrontal Cortex in Non-Human Primates by Dopaminergic Receptors during Rule-Guided Flexible Behavior and Cognitive Control

    PubMed Central

    Vijayraghavan, Susheel; Major, Alex J.; Everling, Stefan

    2017-01-01

    The prefrontal cortex (PFC) is indispensable for several higher-order cognitive and executive capacities of primates, including representation of salient stimuli in working memory (WM), maintenance of cognitive task set, inhibition of inappropriate responses and rule-guided flexible behavior. PFC networks are subject to robust neuromodulation from ascending catecholaminergic systems. Disruption of these systems in PFC has been implicated in cognitive deficits associated with several neuropsychiatric disorders. Over the past four decades, a considerable body of work has examined the influence of dopamine on macaque PFC activity representing spatial WM. There has also been burgeoning interest in neuromodulation of PFC circuits involved in other cognitive functions of PFC, including representation of rules to guide flexible behavior. Here, we review recent neuropharmacological investigations conducted in our laboratory and others of the role of PFC dopamine receptors in regulating rule-guided behavior in non-human primates. Employing iontophoresis, we examined the effects of local manipulation of dopaminergic subtypes on neuronal activity during performance of rule-guided pro- and antisaccades, an experimental paradigm sensitive to PFC integrity, wherein deficits in performance are reliably observed in many neuropsychiatric disorders. We found dissociable effects of dopamine receptors on neuronal activity for rule representation and oculomotor responses and discuss these findings in the context of prior studies that have examined the role of dopamine in spatial delayed response tasks, attention, target selection, abstract rules, visuomotor learning and reward. The findings we describe here highlight the common features, as well as heterogeneity and context dependence of dopaminergic neuromodulation in regulating the efficacy of cognitive functions of PFC in health and disease. PMID:29259545

  10. A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.

    PubMed

    Nguyen, Su; Mei, Yi; Xue, Bing; Zhang, Mengjie

    2018-06-04

    Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This paper develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

  11. Identification and description of the momentum effect in studies of learning: An abstract science concept

    NASA Astrophysics Data System (ADS)

    Kwon, Jae-Sool; Mayer, Victor J.

    Several studies of the validity of the intensive time series design have revealed a post-intervention increase in the level of achievement data. This so called momentum effect has not been demonstrated through the application of an appropriate analysis technique. The purpose of this study was to identify and apply a technique that would adequately represent and describe such an effect if indeed it does occur, and to use that technique to study the momentum effect as it is observed in several data sets on the learning of the concept of plate tectonics. Subsequent to trials of several different analyses, a segmented straight line regression analysis was chosen and used on three different data sets. Each set revealed similar patterns of inflection points between lines with similar time intervals between inflections for those data from students with formal cognitive tendencies. These results seem to indicate that this method will indeed be useful in representing and identifying the presence and duration of the momentum effect in time series data on achievement. Since the momentum effect could be described in each of the data sets and since its presence seems a function of similar circumstances, support is given for its presence in the learning of abstract scientific concepts for formal cognitive tendency students. The results indicate that the duration of the momentum effect is related to the level of student understanding tested and the cognitive level of the learners.

  12. Mathematics and Structural Learning. Final Report.

    ERIC Educational Resources Information Center

    Scandura, Joseph M.

    This report contains four papers describing research based on the view of mathematical knowledge as a hierarchy of "rules." The first paper: "The Role of Rules in Behavior" was abstracted in ED 040 036 (October 1970). The second paper: "A Theory of Mathematical Knowledge" defends the thesis that rules are the basic building blocks of mathematical…

  13. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

    PubMed

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

  14. Instructional control of reinforcement learning: A behavioral and neurocomputational investigation

    PubMed Central

    Doll, Bradley B.; Jacobs, W. Jake; Sanfey, Alan G.; Frank, Michael J.

    2011-01-01

    Humans learn how to behave directly through environmental experience and indirectly through rules and instructions. Behavior analytic research has shown that instructions can control behavior, even when such behavior leads to sub-optimal outcomes (Hayes, S. (Ed.). 1989. Rule-governed behavior: cognition, contingencies, and instructional control. Plenum Press.). Here we examine the control of behavior through instructions in a reinforcement learning task known to depend on striatal dopaminergic function. Participants selected between probabilistically reinforced stimuli, and were (incorrectly) told that a specific stimulus had the highest (or lowest) reinforcement probability. Despite experience to the contrary, instructions drove choice behavior. We present neural network simulations that capture the interactions between instruction-driven and reinforcement-driven behavior via two potential neural circuits: one in which the striatum is inaccurately trained by instruction representations coming from prefrontal cortex/hippocampus (PFC/HC), and another in which the striatum learns the environmentally based reinforcement contingencies, but is “overridden” at decision output. Both models capture the core behavioral phenomena but, because they differ fundamentally on what is learned, make distinct predictions for subsequent behavioral and neuroimaging experiments. Finally, we attempt to distinguish between the proposed computational mechanisms governing instructed behavior by fitting a series of abstract “Q-learning” and Bayesian models to subject data. The best-fitting model supports one of the neural models, suggesting the existence of a “confirmation bias” in which the PFC/HC system trains the reinforcement system by amplifying outcomes that are consistent with instructions while diminishing inconsistent outcomes. PMID:19595993

  15. Enrichment Effects of Gestures and Pictures on Abstract Words in a Second Language.

    PubMed

    Repetto, Claudia; Pedroli, Elisa; Macedonia, Manuela

    2017-01-01

    Laboratory research has demonstrated that multisensory enrichment promotes verbal learning in a foreign language (L2). Enrichment can be done in various ways, e.g., by adding a picture that illustrates the L2 word's meaning or by the learner performing a gesture to the word (enactment). Most studies have tested enrichment on concrete but not on abstract words. Unlike concrete words, the representation of abstract words is deprived of sensory-motor features. This has been addressed as one of the reasons why abstract words are difficult to remember. Here, we ask whether a brief enrichment training by means of pictures and by self-performed gestures also enhances the memorability of abstract words in L2. Further, we explore which of these two enrichment strategies is more effective. Twenty young adults learned 30 novel abstract words in L2 according to three encoding conditions: (1) reading, (2) reading and pairing the novel word to a picture, and (3) reading and enacting the word by means of a gesture. We measured memory performance in free and cued recall tests, as well as in a visual recognition task. Words encoded with gestures were better remembered in the free recall in the native language (L1). When recognizing the novel words, participants made less errors for words encoded with gestures compared to words encoded with pictures. The reaction times in the recognition task did not differ across conditions. The present findings support, even if only partially, the idea that enactment promotes learning of abstract words and that it is superior to enrichment by means of pictures even after short training.

  16. Enrichment Effects of Gestures and Pictures on Abstract Words in a Second Language

    PubMed Central

    Repetto, Claudia; Pedroli, Elisa; Macedonia, Manuela

    2017-01-01

    Laboratory research has demonstrated that multisensory enrichment promotes verbal learning in a foreign language (L2). Enrichment can be done in various ways, e.g., by adding a picture that illustrates the L2 word’s meaning or by the learner performing a gesture to the word (enactment). Most studies have tested enrichment on concrete but not on abstract words. Unlike concrete words, the representation of abstract words is deprived of sensory-motor features. This has been addressed as one of the reasons why abstract words are difficult to remember. Here, we ask whether a brief enrichment training by means of pictures and by self-performed gestures also enhances the memorability of abstract words in L2. Further, we explore which of these two enrichment strategies is more effective. Twenty young adults learned 30 novel abstract words in L2 according to three encoding conditions: (1) reading, (2) reading and pairing the novel word to a picture, and (3) reading and enacting the word by means of a gesture. We measured memory performance in free and cued recall tests, as well as in a visual recognition task. Words encoded with gestures were better remembered in the free recall in the native language (L1). When recognizing the novel words, participants made less errors for words encoded with gestures compared to words encoded with pictures. The reaction times in the recognition task did not differ across conditions. The present findings support, even if only partially, the idea that enactment promotes learning of abstract words and that it is superior to enrichment by means of pictures even after short training. PMID:29326617

  17. Music models aberrant rule decoding and reward valuation in dementia

    PubMed Central

    Clark, Camilla N; Golden, Hannah L; McCallion, Oliver; Nicholas, Jennifer M; Cohen, Miriam H; Slattery, Catherine F; Paterson, Ross W; Fletcher, Phillip D; Mummery, Catherine J; Rohrer, Jonathan D; Crutch, Sebastian J; Warren, Jason D

    2018-01-01

    Abstract Aberrant rule- and reward-based processes underpin abnormalities of socio-emotional behaviour in major dementias. However, these processes remain poorly characterized. Here we used music to probe rule decoding and reward valuation in patients with frontotemporal dementia (FTD) syndromes and Alzheimer’s disease (AD) relative to healthy age-matched individuals. We created short melodies that were either harmonically resolved (‘finished’) or unresolved (‘unfinished’); the task was to classify each melody as finished or unfinished (rule processing) and rate its subjective pleasantness (reward valuation). Results were adjusted for elementary pitch and executive processing; neuroanatomical correlates were assessed using voxel-based morphometry. Relative to healthy older controls, patients with behavioural variant FTD showed impairments of both musical rule decoding and reward valuation, while patients with semantic dementia showed impaired reward valuation but intact rule decoding, patients with AD showed impaired rule decoding but intact reward valuation and patients with progressive non-fluent aphasia performed comparably to healthy controls. Grey matter associations with task performance were identified in anterior temporal, medial and lateral orbitofrontal cortices, previously implicated in computing diverse biological and non-biological rules and rewards. The processing of musical rules and reward distils cognitive and neuroanatomical mechanisms relevant to complex socio-emotional dysfunction in major dementias. PMID:29186630

  18. A Machine Learning Approach to Student Modeling.

    DTIC Science & Technology

    1984-05-01

    machine learning , and describe ACN, a student modeling system that incorporates this approach. This system begins with a set of overly general rules, which it uses to search a problem space until it arrives at the same answer as the student. The ACM computer program then uses the solution path it has discovered to determine positive and negative instances of its initial rules, and employs a discrimination learning mechanism to place additional conditions on these rules. The revised rules will reproduce the solution path without search, and constitute a cognitive model of

  19. Neural learning rules for the vestibulo-ocular reflex

    NASA Technical Reports Server (NTRS)

    Raymond, J. L.; Lisberger, S. G.

    1998-01-01

    Mechanisms for the induction of motor learning in the vestibulo-ocular reflex (VOR) were evaluated by recording the patterns of neural activity elicited in the cerebellum by a range of stimuli that induce learning. Patterns of climbing-fiber, vestibular, and Purkinje cell simple-spike signals were examined during sinusoidal head movement paired with visual image movement at stimulus frequencies from 0.5 to 10 Hz. A comparison of simple-spike and vestibular signals contained the information required to guide learning only at low stimulus frequencies, and a comparison of climbing-fiber and simple-spike signals contained the information required to guide learning only at high stimulus frequencies. Learning could be guided by comparison of climbing-fiber and vestibular signals at all stimulus frequencies tested, but only if climbing fiber responses were compared with the vestibular signals present 100 msec earlier. Computational analysis demonstrated that this conclusion is valid even if there is a broad range of vestibular signals at the site of plasticity. Simulations also indicated that the comparison of vestibular and climbing-fiber signals across the 100 msec delay must be implemented by a subcellular "eligibility" trace rather than by neural circuits that delay the vestibular inputs to the site of plasticity. The results suggest two alternative accounts of learning in the VOR. Either there are multiple mechanisms of learning that use different combinations of neural signals to drive plasticity, or there is a single mechanism tuned to climbing-fiber activity that follows activity in vestibular pathways by approximately 100 msec.

  20. Use what you can: storage, abstraction processes, and perceptual adjustments help listeners recognize reduced forms

    PubMed Central

    Poellmann, Katja; Mitterer, Holger; McQueen, James M.

    2014-01-01

    Three eye-tracking experiments tested whether native listeners recognized reduced Dutch words better after having heard the same reduced words, or different reduced words of the same reduction type and whether familiarization with one reduction type helps listeners to deal with another reduction type. In the exposure phase, a segmental reduction group was exposed to /b/-reductions (e.g., minderij instead of binderij, “book binder”) and a syllabic reduction group was exposed to full-vowel deletions (e.g., p'raat instead of paraat, “ready”), while a control group did not hear any reductions. In the test phase, all three groups heard the same speaker producing reduced-/b/ and deleted-vowel words that were either repeated (Experiments 1 and 2) or new (Experiment 3), but that now appeared as targets in semantically neutral sentences. Word-specific learning effects were found for vowel-deletions but not for /b/-reductions. Generalization of learning to new words of the same reduction type occurred only if the exposure words showed a phonologically consistent reduction pattern (/b/-reductions). In contrast, generalization of learning to words of another reduction type occurred only if the exposure words showed a phonologically inconsistent reduction pattern (the vowel deletions; learning about them generalized to recognition of the /b/-reductions). In order to deal with reductions, listeners thus use various means. They store reduced variants (e.g., for the inconsistent vowel-deleted words) and they abstract over incoming information to build up and apply mapping rules (e.g., for the consistent /b/-reductions). Experience with inconsistent pronunciations leads to greater perceptual flexibility in dealing with other forms of reduction uttered by the same speaker than experience with consistent pronunciations. PMID:24910622

  1. Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation.

    PubMed

    Brito, Carlos S N; Gerstner, Wulfram

    2016-09-01

    The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely nonlinear Hebbian learning. When nonlinear Hebbian learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities.

  2. Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation

    PubMed Central

    Gerstner, Wulfram

    2016-01-01

    The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely nonlinear Hebbian learning. When nonlinear Hebbian learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities. PMID:27690349

  3. Development of abstract mathematical reasoning: the case of algebra.

    PubMed

    Susac, Ana; Bubic, Andreja; Vrbanc, Andrija; Planinic, Maja

    2014-01-01

    Algebra typically represents the students' first encounter with abstract mathematical reasoning and it therefore causes significant difficulties for students who still reason concretely. The aim of the present study was to investigate the developmental trajectory of the students' ability to solve simple algebraic equations. 311 participants between the ages of 13 and 17 were given a computerized test of equation rearrangement. Equations consisted of an unknown and two other elements (numbers or letters), and the operations of multiplication/division. The obtained results showed that younger participants are less accurate and slower in solving equations with letters (symbols) than those with numbers. This difference disappeared for older participants (16-17 years), suggesting that they had reached an abstract reasoning level, at least for this simple task. A corresponding conclusion arises from the analysis of their strategies which suggests that younger participants mostly used concrete strategies such as inserting numbers, while older participants typically used more abstract, rule-based strategies. These results indicate that the development of algebraic thinking is a process which unfolds over a long period of time. In agreement with previous research, we can conclude that, on average, children at the age of 15-16 transition from using concrete to abstract strategies while solving the algebra problems addressed within the present study. A better understanding of the timing and speed of students' transition from concrete arithmetic reasoning to abstract algebraic reasoning might help in designing better curricula and teaching materials that would ease that transition.

  4. Abstract feature codes: The building blocks of the implicit learning system.

    PubMed

    Eberhardt, Katharina; Esser, Sarah; Haider, Hilde

    2017-07-01

    According to the Theory of Event Coding (TEC; Hommel, Müsseler, Aschersleben, & Prinz, 2001), action and perception are represented in a shared format in the cognitive system by means of feature codes. In implicit sequence learning research, it is still common to make a conceptual difference between independent motor and perceptual sequences. This supposedly independent learning takes place in encapsulated modules (Keele, Ivry, Mayr, Hazeltine, & Heuer 2003) that process information along single dimensions. These dimensions have remained underspecified so far. It is especially not clear whether stimulus and response characteristics are processed in separate modules. Here, we suggest that feature dimensions as they are described in the TEC should be viewed as the basic content of modules of implicit learning. This means that the modules process all stimulus and response information related to certain feature dimensions of the perceptual environment. In 3 experiments, we investigated by means of a serial reaction time task the nature of the basic units of implicit learning. As a test case, we used stimulus location sequence learning. The results show that a stimulus location sequence and a response location sequence cannot be learned without interference (Experiment 2) unless one of the sequences can be coded via an alternative, nonspatial dimension (Experiment 3). These results support the notion that spatial location is one module of the implicit learning system and, consequently, that there are no separate processing units for stimulus versus response locations. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  5. Invented Rule with English Language Learners

    ERIC Educational Resources Information Center

    Boyer, Valerie E.; Martin, Kathryn Y.

    2012-01-01

    The purpose of this study was to utilize an invented rule with English language learners (ELLs) in a clinical setting to determine differences based on language and age of the children. The performance was correlated with teacher reports of strong and weak language learning. Using a within-participants design, ELLs of age three to five were taught…

  6. Automatic Learning of Fine Operating Rules for Online Power System Security Control.

    PubMed

    Sun, Hongbin; Zhao, Feng; Wang, Hao; Wang, Kang; Jiang, Weiyong; Guo, Qinglai; Zhang, Boming; Wehenkel, Louis

    2016-08-01

    Fine operating rules for security control and an automatic system for their online discovery were developed to adapt to the development of smart grids. The automatic system uses the real-time system state to determine critical flowgates, and then a continuation power flow-based security analysis is used to compute the initial transfer capability of critical flowgates. Next, the system applies the Monte Carlo simulations to expected short-term operating condition changes, feature selection, and a linear least squares fitting of the fine operating rules. The proposed system was validated both on an academic test system and on a provincial power system in China. The results indicated that the derived rules provide accuracy and good interpretability and are suitable for real-time power system security control. The use of high-performance computing systems enables these fine operating rules to be refreshed online every 15 min.

  7. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

    PubMed Central

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule’s error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism. PMID:27532262

  8. Statistical Methods in Ai: Rare Event Learning Using Associative Rules and Higher-Order Statistics

    NASA Astrophysics Data System (ADS)

    Iyer, V.; Shetty, S.; Iyengar, S. S.

    2015-07-01

    Rare event learning has not been actively researched since lately due to the unavailability of algorithms which deal with big samples. The research addresses spatio-temporal streams from multi-resolution sensors to find actionable items from a perspective of real-time algorithms. This computing framework is independent of the number of input samples, application domain, labelled or label-less streams. A sampling overlap algorithm such as Brooks-Iyengar is used for dealing with noisy sensor streams. We extend the existing noise pre-processing algorithms using Data-Cleaning trees. Pre-processing using ensemble of trees using bagging and multi-target regression showed robustness to random noise and missing data. As spatio-temporal streams are highly statistically correlated, we prove that a temporal window based sampling from sensor data streams converges after n samples using Hoeffding bounds. Which can be used for fast prediction of new samples in real-time. The Data-cleaning tree model uses a nonparametric node splitting technique, which can be learned in an iterative way which scales linearly in memory consumption for any size input stream. The improved task based ensemble extraction is compared with non-linear computation models using various SVM kernels for speed and accuracy. We show using empirical datasets the explicit rule learning computation is linear in time and is only dependent on the number of leafs present in the tree ensemble. The use of unpruned trees (t) in our proposed ensemble always yields minimum number (m) of leafs keeping pre-processing computation to n × t log m compared to N2 for Gram Matrix. We also show that the task based feature induction yields higher Qualify of Data (QoD) in the feature space compared to kernel methods using Gram Matrix.

  9. From action to abstraction: Gesture as a mechanism of change

    PubMed Central

    Goldin-Meadow, Susan

    2015-01-01

    Piaget was a master at observing the routine behaviors children produce as they go from knowing less to knowing more about at a task, and making inferences not only about how the children understood the task at each point, but also about how they progressed from one point to the next. In this paper, I examine a routine behavior that Piaget overlooked—the spontaneous gestures speakers produce as they explain their solutions to a problem. These gestures are not mere hand waving. They reflect ideas that the speaker has about the problem, often ideas that are not found in that speaker’s talk. But gesture can do more than reflect ideas—it can also change them. In this sense, gesture behaves like any other action; both gesture and action on objects facilitate learning problems on which training was given. However, only gesture promotes transferring the knowledge gained to problems that require generalization. Gesture is, in fact, a special kind of action in that it represents the world rather than directly manipulating the world (gesture does not move objects around). The mechanisms by which gesture and action promote learning may therefore differ—gesture is able to highlight components of an action that promote abstract learning while leaving out details that could tie learning to a specific context. Because it is both an action and a representation, gesture can serve as a bridge between the two and thus be a powerful tool for learning abstract ideas. PMID:26692629

  10. From action to abstraction: Gesture as a mechanism of change.

    PubMed

    Goldin-Meadow, Susan

    2015-12-01

    Piaget was a master at observing the routine behaviors children produce as they go from knowing less to knowing more about at a task, and making inferences not only about how the children understood the task at each point, but also about how they progressed from one point to the next. In this paper, I examine a routine behavior that Piaget overlooked-the spontaneous gestures speakers produce as they explain their solutions to a problem. These gestures are not mere hand waving. They reflect ideas that the speaker has about the problem, often ideas that are not found in that speaker's talk. But gesture can do more than reflect ideas-it can also change them. In this sense, gesture behaves like any other action; both gesture and action on objects facilitate learning problems on which training was given. However, only gesture promotes transferring the knowledge gained to problems that require generalization. Gesture is, in fact, a special kind of action in that it represents the world rather than directly manipulating the world (gesture does not move objects around). The mechanisms by which gesture and action promote learning may therefore differ-gesture is able to highlight components of an action that promote abstract learning while leaving out details that could tie learning to a specific context. Because it is both an action and a representation, gesture can serve as a bridge between the two and thus be a powerful tool for learning abstract ideas.

  11. Learning Non-Local Dependencies

    ERIC Educational Resources Information Center

    Kuhn, Gustav; Dienes, Zoltan

    2008-01-01

    This paper addresses the nature of the temporary storage buffer used in implicit or statistical learning. Kuhn and Dienes [Kuhn, G., & Dienes, Z. (2005). Implicit learning of nonlocal musical rules: implicitly learning more than chunks. "Journal of Experimental Psychology-Learning Memory and Cognition," 31(6) 1417-1432] showed that people could…

  12. Complexity, Training Paradigm Design, and the Contribution of Memory Subsystems to Grammar Learning

    PubMed Central

    Ettlinger, Marc; Wong, Patrick C. M.

    2016-01-01

    Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative, procedural, and working memory subsystems. Experiment 1 demonstrated that passive, exposure-based training boosted learning of both simple and complex grammatical rules, relative to no training. Additionally, procedural memory correlated with simple rule learning, whereas declarative memory correlated with complex rule learning. Experiment 2 showed that presenting corrective feedback during the test phase did not improve learning. Experiment 3 revealed that structuring the order of training so that subjects are first exposed to the simple rule and then the complex improved learning. The cumulative findings shed light on the contributions of grammatical complexity, training paradigm design, and domain-general memory subsystems in determining grammar learning success. PMID:27391085

  13. The Role of Age and Executive Function in Auditory Category Learning

    PubMed Central

    Reetzke, Rachel; Maddox, W. Todd; Chandrasekaran, Bharath

    2015-01-01

    Auditory categorization is a natural and adaptive process that allows for the organization of high-dimensional, continuous acoustic information into discrete representations. Studies in the visual domain have identified a rule-based learning system that learns and reasons via a hypothesis-testing process that requires working memory and executive attention. The rule-based learning system in vision shows a protracted development, reflecting the influence of maturing prefrontal function on visual categorization. The aim of the current study is two-fold: (a) to examine the developmental trajectory of rule-based auditory category learning from childhood through adolescence, into early adulthood; and (b) to examine the extent to which individual differences in rule-based category learning relate to individual differences in executive function. Sixty participants with normal hearing, 20 children (age range, 7–12), 21 adolescents (age range, 13–19), and 19 young adults (age range, 20–23), learned to categorize novel dynamic ripple sounds using trial-by-trial feedback. The spectrotemporally modulated ripple sounds are considered the auditory equivalent of the well-studied Gabor patches in the visual domain. Results revealed that auditory categorization accuracy improved with age, with young adults outperforming children and adolescents. Computational modeling analyses indicated that the use of the task-optimal strategy (i.e. a conjunctive rule-based learning strategy) improved with age. Notably, individual differences in executive flexibility significantly predicted auditory category learning success. The current findings demonstrate a protracted development of rule-based auditory categorization. The results further suggest that executive flexibility coupled with perceptual processes play important roles in successful rule-based auditory category learning. PMID:26491987

  14. ALT-C 96: Integrating Technology into the Curriculum. Conference Programme and Abstracts of the Association for Learning Technology Conference (3rd, Glasgow, Scotland, September 16-18, 1996).

    ERIC Educational Resources Information Center

    Cameron, Shona, Ed.

    This program for the 1996 Association for Learning Technology Conference summarizes the poster sessions, discussions, workshops, and software demonstrations, and provides abstracts of the 38 papers presented. Topics covered by the papers include: hand-held technology for mathematics; modeling global warming; computer-mediated communications; Java;…

  15. Writing Abstracts for MLIS Research Proposals Using Worked Examples: An Innovative Approach to Teaching the Elements of Research Design

    ERIC Educational Resources Information Center

    Ondrusek, Anita L.; Thiele, Harold E.; Yang, Changwoo

    2014-01-01

    The authors examined abstracts written by graduate students for their research proposals as a requirement for a course in research methods in a distance learning MLIS program. The students learned under three instructional conditions that involved varying levels of access to worked examples created from abstracts representing research in the LIS…

  16. Mining Formative Evaluation Rules Using Web-Based Learning Portfolios for Web-Based Learning Systems

    ERIC Educational Resources Information Center

    Chen, Chih-Ming; Hong, Chin-Ming; Chen, Shyuan-Yi; Liu, Chao-Yu

    2006-01-01

    Learning performance assessment aims to evaluate what knowledge learners have acquired from teaching activities. Objective technical measures of learning performance are difficult to develop, but are extremely important for both teachers and learners. Learning performance assessment using learning portfolios or web server log data is becoming an…

  17. The Effects of Concurrent Verbal and Visual Tasks on Category Learning

    ERIC Educational Resources Information Center

    Miles, Sarah J.; Minda, John Paul

    2011-01-01

    Current theories of category learning posit separate verbal and nonverbal learning systems. Past research suggests that the verbal system relies on verbal working memory and executive functioning and learns rule-defined categories; the nonverbal system does not rely on verbal working memory and learns non-rule-defined categories (E. M. Waldron…

  18. Cognitive control over learning: Creating, clustering and generalizing task-set structure

    PubMed Central

    Collins, Anne G.E.; Frank, Michael J.

    2013-01-01

    Executive functions and learning share common neural substrates essential for their expression, notably in prefrontal cortex and basal ganglia. Understanding how they interact requires studying how cognitive control facilitates learning, but also how learning provides the (potentially hidden) structure, such as abstract rules or task-sets, needed for cognitive control. We investigate this question from three complementary angles. First, we develop a new computational “C-TS” (context-task-set) model inspired by non-parametric Bayesian methods, specifying how the learner might infer hidden structure and decide whether to re-use that structure in new situations, or to create new structure. Second, we develop a neurobiologically explicit model to assess potential mechanisms of such interactive structured learning in multiple circuits linking frontal cortex and basal ganglia. We systematically explore the link betweens these levels of modeling across multiple task demands. We find that the network provides an approximate implementation of high level C-TS computations, where manipulations of specific neural mechanisms are well captured by variations in distinct C-TS parameters. Third, this synergism across models yields strong predictions about the nature of human optimal and suboptimal choices and response times during learning. In particular, the models suggest that participants spontaneously build task-set structure into a learning problem when not cued to do so, which predicts positive and negative transfer in subsequent generalization tests. We provide evidence for these predictions in two experiments and show that the C-TS model provides a good quantitative fit to human sequences of choices in this task. These findings implicate a strong tendency to interactively engage cognitive control and learning, resulting in structured abstract representations that afford generalization opportunities, and thus potentially long-term rather than short-term optimality. PMID

  19. Evolution of cooperation driven by incremental learning

    NASA Astrophysics Data System (ADS)

    Li, Pei; Duan, Haibin

    2015-02-01

    It has been shown that the details of microscopic rules in structured populations can have a crucial impact on the ultimate outcome in evolutionary games. So alternative formulations of strategies and their revision processes exploring how strategies are actually adopted and spread within the interaction network need to be studied. In the present work, we formulate the strategy update rule as an incremental learning process, wherein knowledge is refreshed according to one's own experience learned from the past (self-learning) and that gained from social interaction (social-learning). More precisely, we propose a continuous version of strategy update rules, by introducing the willingness to cooperate W, to better capture the flexibility of decision making behavior. Importantly, the newly gained knowledge including self-learning and social learning is weighted by the parameter ω, establishing a strategy update rule involving innovative element. Moreover, we quantify the macroscopic features of the emerging patterns to inspect the underlying mechanisms of the evolutionary process using six cluster characteristics. In order to further support our results, we examine the time evolution course for these characteristics. Our results might provide insights for understanding cooperative behaviors and have several important implications for understanding how individuals adjust their strategies under real-life conditions.

  20. Learning a theory of causality.

    PubMed

    Goodman, Noah D; Ullman, Tomer D; Tenenbaum, Joshua B

    2011-01-01

    The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned--an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion.

  1. Learning in the Absence of Experience-Dependent Regulation of NMDAR Composition

    ERIC Educational Resources Information Center

    Lebel, David; Sidhu, Nishchal; Barkai, Edi; Quinlan, Elizabeth M.

    2006-01-01

    Olfactory discrimination (OD) learning consists of two phases: an initial N-methyl-d-aspartate (NMDA) receptor--sensitive rule-learning phase, followed by an NMDA receptor (NMDAR)--insensitive pair-learning phase. The rule-learning phase is accompanied by changes in the composition and function of NMDARs at synapses in the piriform cortex,…

  2. Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences

    PubMed Central

    2016-01-01

    Emerging studies indicate that several species such as corvids, apes and children solve ‘The Crow and the Pitcher’ task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause–effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended ‘learning–prediction–abstraction’ loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration. PMID:27466440

  3. Representations of Abstract Grammatical Feature Agreement in Young Children

    ERIC Educational Resources Information Center

    Melançon, Andréane; Shi, Rushen

    2015-01-01

    A fundamental question in language acquisition research is whether young children have abstract grammatical representations. We tested this question experimentally. French-learning 30-month-olds were first taught novel word-object pairs in the context of a gender-marked determiner (e.g., un[subscript MASC]ravole "a ravole"). Test trials…

  4. Instructional Variables in Meaningful Learning of Computer Programming.

    ERIC Educational Resources Information Center

    Mayer, Richard E.

    Some 120 undergraduate students participated in experiments to learn how novice computer programers learn to interact with the computer. Two instructional booklets were used: A "rule" booklet consisted of definitions and examples of seven modified FORTRAN statements and appropriate grammar rules; the "model" booklet was…

  5. Knowledge discovery with classification rules in a cardiovascular dataset.

    PubMed

    Podgorelec, Vili; Kokol, Peter; Stiglic, Milojka Molan; Hericko, Marjan; Rozman, Ivan

    2005-12-01

    In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

  6. Analysis of Secondary School Students’ Algebraic Thinking and Math-Talk Learning Community to Help Students Learn

    NASA Astrophysics Data System (ADS)

    Nurhayati, D. M.; Herman, T.; Suhendra, S.

    2017-09-01

    This study aims to determine the difficulties of algebraic thinking ability of students in one of secondary school on quadrilateral subject and to describe Math-Talk Learning Community as the alternative way that can be done to overcome the difficulties of the students’ algebraic thinking ability. Research conducted by using quantitative approach with descriptive method. The population in this research was all students of that school and twenty three students as the sample that was chosen by purposive sampling technique. Data of algebraic thinking were collected through essay test. The results showed the percentage of achievement of students’ algebraic thinking’s indicators on three aspects: a) algebra as generalized arithmetic with the indicators (conceptually based computational strategies and estimation); b) algebra as the language of mathematics (meaning of variables, variable expressions and meaning of solution); c) algebra as a tool for functions and mathematical modelling (representing mathematical ideas using equations, tables, or words and generalizing patterns and rules in real-world contexts) is still low. It is predicted that because the secondary school students was not familiar with the abstract problem and they are still at a semi-concrete stage where the stage of cognitive development is between concrete and abstract. Based on the percentage achievement of each indicators, it can be concluded that the level of achievement of student’s mathematical communication using conventional learning is still low, so students’ algebraic thinking ability need to be improved.

  7. Assessing predation risk: optimal behaviour and rules of thumb.

    PubMed

    Welton, Nicky J; McNamara, John M; Houston, Alasdair I

    2003-12-01

    We look at a simple model in which an animal makes behavioural decisions over time in an environment in which all parameters are known to the animal except predation risk. In the model there is a trade-off between gaining information about predation risk and anti-predator behaviour. All predator attacks lead to death for the prey, so that the prey learns about predation risk by virtue of the fact that it is still alive. We show that it is not usually optimal to behave as if the current unbiased estimate of the predation risk is its true value. We consider two different ways to model reproduction; in the first scenario the animal reproduces throughout its life until it dies, and in the second scenario expected reproductive success depends on the level of energy reserves the animal has gained by some point in time. For both of these scenarios we find results on the form of the optimal strategy and give numerical examples which compare optimal behaviour with behaviour under simple rules of thumb. The numerical examples suggest that the value of the optimal strategy over the rules of thumb is greatest when there is little current information about predation risk, learning is not too costly in terms of predation, and it is energetically advantageous to learn about predation. We find that for the model and parameters investigated, a very simple rule of thumb such as 'use the best constant control' performs well.

  8. Supreme Court's Patent Ruling Could Spell Trouble For Blackboard and Others

    ERIC Educational Resources Information Center

    Carnevale, Dan

    2007-01-01

    Many college officials have criticized Blackboard Inc. for its patent on its course-management system, arguing that the patent is overly broad and seems to cover the entire concept of online learning. Critics of Blackboard and other companies that have patents on learning technology are welcoming a recent Supreme Court ruling that they hope may…

  9. Promoter Sequences Prediction Using Relational Association Rule Mining

    PubMed Central

    Czibula, Gabriela; Bocicor, Maria-Iuliana; Czibula, Istvan Gergely

    2012-01-01

    In this paper we are approaching, from a computational perspective, the problem of promoter sequences prediction, an important problem within the field of bioinformatics. As the conditions for a DNA sequence to function as a promoter are not known, machine learning based classification models are still developed to approach the problem of promoter identification in the DNA. We are proposing a classification model based on relational association rules mining. Relational association rules are a particular type of association rules and describe numerical orderings between attributes that commonly occur over a data set. Our classifier is based on the discovery of relational association rules for predicting if a DNA sequence contains or not a promoter region. An experimental evaluation of the proposed model and comparison with similar existing approaches is provided. The obtained results show that our classifier overperforms the existing techniques for identifying promoter sequences, confirming the potential of our proposal. PMID:22563233

  10. On-line Gibbs learning. II. Application to perceptron and multilayer networks

    NASA Astrophysics Data System (ADS)

    Kim, J. W.; Sompolinsky, H.

    1998-08-01

    In the preceding paper (``On-line Gibbs Learning. I. General Theory'') we have presented the on-line Gibbs algorithm (OLGA) and studied analytically its asymptotic convergence. In this paper we apply OLGA to on-line supervised learning in several network architectures: a single-layer perceptron, two-layer committee machine, and a winner-takes-all (WTA) classifier. The behavior of OLGA for a single-layer perceptron is studied both analytically and numerically for a variety of rules: a realizable perceptron rule, a perceptron rule corrupted by output and input noise, and a rule generated by a committee machine. The two-layer committee machine is studied numerically for the cases of learning a realizable rule as well as a rule that is corrupted by output noise. The WTA network is studied numerically for the case of a realizable rule. The asymptotic results reported in this paper agree with the predictions of the general theory of OLGA presented in paper I. In all the studied cases, OLGA converges to a set of weights that minimizes the generalization error. When the learning rate is chosen as a power law with an optimal power, OLGA converges with a power law that is the same as that of batch learning.

  11. Development of abstract mathematical reasoning: the case of algebra

    PubMed Central

    Susac, Ana; Bubic, Andreja; Vrbanc, Andrija; Planinic, Maja

    2014-01-01

    Algebra typically represents the students’ first encounter with abstract mathematical reasoning and it therefore causes significant difficulties for students who still reason concretely. The aim of the present study was to investigate the developmental trajectory of the students’ ability to solve simple algebraic equations. 311 participants between the ages of 13 and 17 were given a computerized test of equation rearrangement. Equations consisted of an unknown and two other elements (numbers or letters), and the operations of multiplication/division. The obtained results showed that younger participants are less accurate and slower in solving equations with letters (symbols) than those with numbers. This difference disappeared for older participants (16–17 years), suggesting that they had reached an abstract reasoning level, at least for this simple task. A corresponding conclusion arises from the analysis of their strategies which suggests that younger participants mostly used concrete strategies such as inserting numbers, while older participants typically used more abstract, rule-based strategies. These results indicate that the development of algebraic thinking is a process which unfolds over a long period of time. In agreement with previous research, we can conclude that, on average, children at the age of 15–16 transition from using concrete to abstract strategies while solving the algebra problems addressed within the present study. A better understanding of the timing and speed of students’ transition from concrete arithmetic reasoning to abstract algebraic reasoning might help in designing better curricula and teaching materials that would ease that transition. PMID:25228874

  12. Brain Dynamics Sustaining Rapid Rule Extraction from Speech

    ERIC Educational Resources Information Center

    de Diego-Balaguer, Ruth; Fuentemilla, Lluis; Rodriguez-Fornells, Antoni

    2011-01-01

    Language acquisition is a complex process that requires the synergic involvement of different cognitive functions, which include extracting and storing the words of the language and their embedded rules for progressive acquisition of grammatical information. As has been shown in other fields that study learning processes, synchronization…

  13. Learning CAD at University through Summaries of the Rules of Design Intent

    ERIC Educational Resources Information Center

    Barbero, Basilio Ramos; Pedrosa, Carlos Melgosa; Samperio, Raúl Zamora

    2017-01-01

    The ease with which 3D CAD models may be modified and reused are two key aspects that improve the design-intent variable and that can significantly shorten the development timelines of a product. A set of rules are gathered from various authors that take different 3D modelling strategies into account. These rules are then applied to CAD…

  14. A Collaborative Educational Association Rule Mining Tool

    ERIC Educational Resources Information Center

    Garcia, Enrique; Romero, Cristobal; Ventura, Sebastian; de Castro, Carlos

    2011-01-01

    This paper describes a collaborative educational data mining tool based on association rule mining for the ongoing improvement of e-learning courses and allowing teachers with similar course profiles to share and score the discovered information. The mining tool is oriented to be used by non-expert instructors in data mining so its internal…

  15. Anisotropic interaction rules in circular motions of pigeon flocks: An empirical study based on sparse Bayesian learning

    NASA Astrophysics Data System (ADS)

    Chen, Duxin; Xu, Bowen; Zhu, Tao; Zhou, Tao; Zhang, Hai-Tao

    2017-08-01

    Coordination shall be deemed to the result of interindividual interaction among natural gregarious animal groups. However, revealing the underlying interaction rules and decision-making strategies governing highly coordinated motion in bird flocks is still a long-standing challenge. Based on analysis of high spatial-temporal resolution GPS data of three pigeon flocks, we extract the hidden interaction principle by using a newly emerging machine learning method, namely the sparse Bayesian learning. It is observed that the interaction probability has an inflection point at pairwise distance of 3-4 m closer than the average maximum interindividual distance, after which it decays strictly with rising pairwise metric distances. Significantly, the density of spatial neighbor distribution is strongly anisotropic, with an evident lack of interactions along individual velocity. Thus, it is found that in small-sized bird flocks, individuals reciprocally cooperate with a variational number of neighbors in metric space and tend to interact with closer time-varying neighbors, rather than interacting with a fixed number of topological ones. Finally, extensive numerical investigation is conducted to verify both the revealed interaction and decision-making principle during circular flights of pigeon flocks.

  16. IM and Q-D Rules: An Analysis by French Club MURAT

    DTIC Science & Technology

    1996-08-01

    and Q-D Rules: An Analysis by French Club Murat 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER...IM(1) AND Q-D RULES: AN ANALYSIS BY FRENCH CLUB MURAT by Jean ISLER (2) - Jean G. GOLIGER (3) - Daniel BOCHAND (4) Georges QUEROL (5) - Louis PICARD...6) and Joël FERRON (7) CLUB MURAT - BP 129 78 148 VELIZY CEDEX - FRANCE Tel : (33) - (1) 39.46.15.50 Fax : (33) - (1) 39.46.15.38 ABSTRACT The

  17. Breaking the Rules: Do Infants Have a True Understanding of False Belief?

    ERIC Educational Resources Information Center

    Yott, Jessica; Poulin-Dubois, Diane

    2012-01-01

    It has been suggested that infants' performance on the false belief task can be explained by the use of behavioural rules. To test this hypothesis, 18-month-old infants were trained to learn the new rule that an object that disappeared in location A could be found in location B. Infants were then administered a false belief task based on the…

  18. Reading, Comprehension, and Memory Processes: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," July through September 1977 (Vol. 38 Nos. 1 through 3).

    ERIC Educational Resources Information Center

    ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

    This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 14 titles deal with the following topics: children's ability to read their own dictated oral language; adjunct structure and reading comprehension; learning and comprehension of simultaneously presented stimuli in children of…

  19. Learning of Abstract Concepts through Full-Body Interaction: A Systematic Review

    ERIC Educational Resources Information Center

    Malinverni, Laura; Pares, Narcis

    2014-01-01

    Over the past ten years several learning environments based on novel interaction modalities have been developed. Within this field, Full-body Interaction Learning Environments open promising possibilities given their capacity to involve the users at different levels, such as sensorimotor experience, cognitive aspects and affective factors.…

  20. Representations of abstract grammatical feature agreement in young children.

    PubMed

    Melançon, Andréane; Shi, Rushen

    2015-11-01

    A fundamental question in language acquisition research is whether young children have abstract grammatical representations. We tested this question experimentally. French-learning 30-month-olds were first taught novel word-object pairs in the context of a gender-marked determiner (e.g., un MASC ravole 'a ravole'). Test trials presented the objects side-by-side while one of them was named in new phrases containing other determiners and an adjective (e.g., le MASC joli ravole MASC 'the pretty ravole'). The gender agreement between the new determiner and the non-adjacent noun was manipulated in different test trials (e.g., le MASC __ravole MASC; *la FEM __ravole MASC). We found that online comprehension of the named target was facilitated in gender-matched trials but impeded in gender-mismatched trials. That is, children assigned the determiner genders to the novel nouns during word learning. They then processed the non-adjacent gender agreement between the two categories (Det, Noun) during test. The results demonstrate abstract featural representation and grammatical productivity in young children.

  1. Learning classification trees

    NASA Technical Reports Server (NTRS)

    Buntine, Wray

    1991-01-01

    Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.

  2. Learning Non-Adjacent Regularities at Age 0 ; 7

    ERIC Educational Resources Information Center

    Gervain, Judit; Werker, Janet F.

    2013-01-01

    One important mechanism suggested to underlie the acquisition of grammar is rule learning. Indeed, infants aged 0 ; 7 are able to learn rules based on simple identity relations (adjacent repetitions, ABB: "wo fe fe" and non-adjacent repetitions, ABA: "wo fe wo", respectively; Marcus et al., 1999). One unexplored issue is…

  3. Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

    PubMed

    Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

    2013-01-01

    A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

  4. Precise-Spike-Driven Synaptic Plasticity: Learning Hetero-Association of Spatiotemporal Spike Patterns

    PubMed Central

    Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

    2013-01-01

    A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe. PMID:24223789

  5. The Role of Feedback Contingency in Perceptual Category Learning

    PubMed Central

    Ashby, F. Gregory; Vucovich, Lauren E.

    2016-01-01

    Feedback is highly contingent on behavior if it eventually becomes easy to predict, and weakly contingent on behavior if it remains difficult or impossible to predict even after learning is complete. Many studies have demonstrated that humans and nonhuman animals are highly sensitive to feedback contingency, but no known studies have examined how feedback contingency affects category learning, and current theories assign little or no importance to this variable. Two experiments examined the effects of contingency degradation on rule-based and information-integration category learning. In rule-based tasks, optimal accuracy is possible with a simple explicit rule, whereas optimal accuracy in information-integration tasks requires integrating information from two or more incommensurable perceptual dimensions. In both experiments, participants each learned rule-based or information-integration categories under either high or low levels of feedback contingency. The exact same stimuli were used in all four conditions and optimal accuracy was identical in every condition. Learning was good in both high-contingency conditions, but most participants showed little or no evidence of learning in either low-contingency condition. Possible causes of these effects are discussed, as well as their theoretical implications. PMID:27149393

  6. Rule-based modeling with Virtual Cell

    PubMed Central

    Schaff, James C.; Vasilescu, Dan; Moraru, Ion I.; Loew, Leslie M.; Blinov, Michael L.

    2016-01-01

    Summary: Rule-based modeling is invaluable when the number of possible species and reactions in a model become too large to allow convenient manual specification. The popular rule-based software tools BioNetGen and NFSim provide powerful modeling and simulation capabilities at the cost of learning a complex scripting language which is used to specify these models. Here, we introduce a modeling tool that combines new graphical rule-based model specification with existing simulation engines in a seamless way within the familiar Virtual Cell (VCell) modeling environment. A mathematical model can be built integrating explicit reaction networks with reaction rules. In addition to offering a large choice of ODE and stochastic solvers, a model can be simulated using a network free approach through the NFSim simulation engine. Availability and implementation: Available as VCell (versions 6.0 and later) at the Virtual Cell web site (http://vcell.org/). The application installs and runs on all major platforms and does not require registration for use on the user’s computer. Tutorials are available at the Virtual Cell website and Help is provided within the software. Source code is available at Sourceforge. Contact: vcell_support@uchc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27497444

  7. Reading, Comprehension, and Memory Processes: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," April through June 1978 (Vol. 38, Nos. 10 through 12).

    ERIC Educational Resources Information Center

    ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

    This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 17 titles deal with the following topics: the effects of preinstructional activities on the learning and retention of prose; the interaction of picture and print in reading instruction; the perception of structure in scrambled…

  8. Reading Instruction: Preschool and Elementary: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," July through September 1978 (Vol. 39 Nos. 1 through 3).

    ERIC Educational Resources Information Center

    ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

    This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 25 titles deal with a variety of topics, including the following: learning outcomes produced by single- and multi-grade primary classrooms; the effect of sex-role stereotyped picture books on children's behavior; word recognition…

  9. Rules for Optical Testing

    NASA Technical Reports Server (NTRS)

    Stahl, H. Philip

    2014-01-01

    Based on 30 years of optical testing experience, a lot of mistakes, a lot of learning and a lot of experience, I have defined seven guiding principles for optical testing - regardless of how small or how large the optical testing or metrology task: Fully Understand the Task, Develop an Error Budget, Continuous Metrology Coverage, Know where you are, Test like you fly, Independent Cross-Checks, Understand All Anomalies. These rules have been applied with great success to the inprocess optical testing and final specification compliance testing of the JWST mirrors.

  10. Experience with abstract notation one

    NASA Technical Reports Server (NTRS)

    Harvey, James D.; Weaver, Alfred C.

    1990-01-01

    The development of computer science has produced a vast number of machine architectures, programming languages, and compiler technologies. The cross product of these three characteristics defines the spectrum of previous and present data representation methodologies. With regard to computer networks, the uniqueness of these methodologies presents an obstacle when disparate host environments are to be interconnected. Interoperability within a heterogeneous network relies upon the establishment of data representation commonality. The International Standards Organization (ISO) is currently developing the abstract syntax notation one standard (ASN.1) and the basic encoding rules standard (BER) that collectively address this problem. When used within the presentation layer of the open systems interconnection reference model, these two standards provide the data representation commonality required to facilitate interoperability. The details of a compiler that was built to automate the use of ASN.1 and BER are described. From this experience, insights into both standards are given and potential problems relating to this development effort are discussed.

  11. Aesthetic concepts, perceptual learning, and linguistic enculturation: considerations from Wittgenstein, language, and music.

    PubMed

    Croom, Adam M

    2012-03-01

    Aesthetic non-cognitivists deny that aesthetic statements express genuinely aesthetic beliefs and instead hold that they work primarily to express something non-cognitive, such as attitudes of approval or disapproval, or desire. Non-cognitivists deny that aesthetic statements express aesthetic beliefs because they deny that there are aesthetic features in the world for aesthetic beliefs to represent. Their assumption, shared by scientists and theorists of mind alike, was that language-users possess cognitive mechanisms with which to objectively grasp abstract rules fixed independently of human responses, and that cognizers are thereby capable of grasping rules for the correct application of aesthetic concepts without relying on evaluation or enculturation. However, in this article I use Wittgenstein's rule-following considerations to argue that psychological theories grounded upon this so-called objective model of rule-following fail to adequately account for concept acquisition and mastery. I argue that this is because linguistic enculturation, and the perceptual learning that's often involved, influences and enables the mastery of aesthetic concepts. I argue that part of what's involved in speaking aesthetically is to belong to a cultural practice of making sense of things aesthetically, and that it's within a socio-linguistic community, and that community's practices, that such aesthetic sense can be made intelligible.

  12. Natural concepts in a juvenile gorilla (gorilla gorilla gorilla) at three levels of abstraction.

    PubMed Central

    Vonk, Jennifer; MacDonald, Suzanne E

    2002-01-01

    The extent to which nonhumans are able to form conceptual versus perceptual discriminations remains a matter of debate. Among the great apes, only chimpanzees have been tested for conceptual understanding, defined as the ability to form discriminations not based solely on simple perceptual features of stimuli, and to transfer this learning to novel stimuli. In the present investigation, a young captive female gorilla was trained at three levels of abstraction (concrete, intermediate, and abstract) involving sets of photographs representing natural categories (e.g., orangutans vs. humans, primates vs. nonprimate animals, animals vs. foods). Within each level of abstraction, when the gorilla had learned to discriminate positive from negative exemplars in one set of photographs, a novel set was introduced. Transfer was defined in terms of high accuracy during the first two sessions with the new stimuli. The gorilla acquired discriminations at all three levels of abstraction but showed unambiguous transfer only with the concrete and abstract stimulus sets. Detailed analyses of response patterns revealed little evidence of control by simple stimulus features. Acquisition and transfer involving abstract stimulus sets suggest a conceptual basis for gorilla categorization. The gorilla's relatively poor performance with intermediate-level discriminations parallels findings with pigeons, and suggests a need to reconsider the role of perceptual information in discriminations thought to indicate conceptual behavior in nonhumans. PMID:12507006

  13. Spontaneous generalization of abstract multimodal patterns in young domestic chicks.

    PubMed

    Versace, Elisabetta; Spierings, Michelle J; Caffini, Matteo; Ten Cate, Carel; Vallortigara, Giorgio

    2017-05-01

    From the early stages of life, learning the regularities associated with specific objects is crucial for making sense of experiences. Through filial imprinting, young precocial birds quickly learn the features of their social partners by mere exposure. It is not clear though to what extent chicks can extract abstract patterns of the visual and acoustic stimuli present in the imprinting object, and how they combine them. To investigate this issue, we exposed chicks (Gallus gallus) to three days of visual and acoustic imprinting, using either patterns with two identical items or patterns with two different items, presented visually, acoustically or in both modalities. Next, chicks were given a choice between the familiar and the unfamiliar pattern, present in either the multimodal, visual or acoustic modality. The responses to the novel stimuli were affected by their imprinting experience, and the effect was stronger for chicks imprinted with multimodal patterns than for the other groups. Interestingly, males and females adopted a different strategy, with males more attracted by unfamiliar patterns and females more attracted by familiar patterns. Our data show that chicks can generalize abstract patterns by mere exposure through filial imprinting and that multimodal stimulation is more effective than unimodal stimulation for pattern learning.

  14. Remedial and Compensatory Reading Instruction: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," January through June 1985 (Vol. 45 Nos. 7 through 12).

    ERIC Educational Resources Information Center

    ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

    This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 18 titles deal with a variety of topics, including the following: (1) the effects of phonemic segmentation and blending training on the word recognition performance of learning disabled readers; (2) a comparison of two remedial…

  15. A "Sweet 16" of Rules About Teamwork

    NASA Technical Reports Server (NTRS)

    Laufer, Alexander (Editor)

    2002-01-01

    The following "Sweet 16" rules included in this paper derive from a longer paper by APPL Director Dr. Edward Hoffman and myself entitled " 99 Rules for Managing Faster, Better, Cheaper Projects." Our sources consisted mainly of "war stories" told by master project managers in my book Simultaneous Management: Managing Projects in a Dynamic Environment (AMACOM, The American Management Association, 1996). The Simultaneous Management model was a result of 10 years of intensive research and testing conducted with the active participation of master project managers from leading private organizations such as AT&T, DuPont, Exxon, General Motors, IBM, Motorola and Procter & Gamble. In a more recent study, led by Dr. Hoffman, we learned that master project managers in leading public organizations employ most of these rules as well. Both studies, in private and public organizations, found that a dynamic environment calls for dynamic management, and that is especially clear in how successful project managers think about their teams.

  16. Evolving optimised decision rules for intrusion detection using particle swarm paradigm

    NASA Astrophysics Data System (ADS)

    Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

    2012-12-01

    The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

  17. Foraging Ecology Predicts Learning Performance in Insectivorous Bats

    PubMed Central

    Clarin, Theresa M. A.; Ruczyński, Ireneusz; Page, Rachel A.

    2013-01-01

    Bats are unusual among mammals in showing great ecological diversity even among closely related species and are thus well suited for studies of adaptation to the ecological background. Here we investigate whether behavioral flexibility and simple- and complex-rule learning performance can be predicted by foraging ecology. We predict faster learning and higher flexibility in animals hunting in more complex, variable environments than in animals hunting in more simple, stable environments. To test this hypothesis, we studied three closely related insectivorous European bat species of the genus Myotis that belong to three different functional groups based on foraging habitats: M. capaccinii, an open water forager, M. myotis, a passive listening gleaner, and M. emarginatus, a clutter specialist. We predicted that M. capaccinii would show the least flexibility and slowest learning reflecting its relatively unstructured foraging habitat and the stereotypy of its natural foraging behavior, while the other two species would show greater flexibility and more rapid learning reflecting the complexity of their natural foraging tasks. We used a purposefully unnatural and thus species-fair crawling maze to test simple- and complex-rule learning, flexibility and re-learning performance. We found that M. capaccinii learned a simple rule as fast as the other species, but was slower in complex rule learning and was less flexible in response to changes in reward location. We found no differences in re-learning ability among species. Our results corroborate the hypothesis that animals’ cognitive skills reflect the demands of their ecological niche. PMID:23755146

  18. A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models.

    PubMed

    Hanuschkin, A; Ganguli, S; Hahnloser, R H R

    2013-01-01

    Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.

  19. A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

    PubMed Central

    Hanuschkin, A.; Ganguli, S.; Hahnloser, R. H. R.

    2013-01-01

    Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli. PMID

  20. A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

    PubMed

    Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

    2017-05-01

    This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

  1. Comparison of Two Mnemonic Encoding Strategies on Children's Recognition and Recall of Abstract Prose Information.

    ERIC Educational Resources Information Center

    Shriberg, Linda K.

    As an extension of an earlier investigation that examined the effects of mnemonic strategy application on children's memory for abstract prose passages, a study compared the benefits accrued by students taught two different variations of the mnemonic keyword method for learning abstract prose information, via tasks of associative recognition and…

  2. Infant Learning Is Influenced by Local Spurious Generalizations

    ERIC Educational Resources Information Center

    Gerken, LouAnn; Quam, Carolyn

    2017-01-01

    In previous work, 11-month-old infants were able to learn rules about the relation of the consonants in CVCV words from just four examples. The rules involved phonetic feature relations (same voicing or same place of articulation), and infants' learning was impeded when pairs of words allowed alternative possible generalizations (e.g. two words…

  3. Multimedia Football Viewing: Embedded Rules, Practice, and Video Context in IVD Procedural Learning.

    ERIC Educational Resources Information Center

    Kim, Eunsoon; Young, Michael F.

    This study investigated the effects of interactive video (IVD) instruction with embedded rules (production system rules) and practice with feedback on learners' academic achievement and perceived self efficacy in the domain of procedural knowledge for watching professional football. Subjects were 71 female volunteers from undergraduate education…

  4. An Examination of Strategy Implementation During Abstract Nonlinguistic Category Learning in Aphasia

    PubMed Central

    Kiran, Swathi

    2015-01-01

    Purpose Our purpose was to study strategy use during nonlinguistic category learning in aphasia. Method Twelve control participants without aphasia and 53 participants with aphasia (PWA) completed a computerized feedback-based category learning task consisting of training and testing phases. Accuracy rates of categorization in testing phases were calculated. To evaluate strategy use, strategy analyses were conducted over training and testing phases. Participant data were compared with model data that simulated complex multi-cue, single feature, and random pattern strategies. Learning success and strategy use were evaluated within the context of standardized cognitive–linguistic assessments. Results Categorization accuracy was higher among control participants than among PWA. The majority of control participants implemented suboptimal or optimal multi-cue and single-feature strategies by testing phases of the experiment. In contrast, a large subgroup of PWA implemented random patterns, or no strategy, during both training and testing phases of the experiment. Conclusions Person-to-person variability arises not only in category learning ability but also in the strategies implemented to complete category learning tasks. PWA less frequently developed effective strategies during category learning tasks than control participants. Certain PWA may have impairments of strategy development or feedback processing not captured by language and currently probed cognitive abilities. PMID:25908438

  5. An Examination of Strategy Implementation During Abstract Nonlinguistic Category Learning in Aphasia.

    PubMed

    Vallila-Rohter, Sofia; Kiran, Swathi

    2015-08-01

    Our purpose was to study strategy use during nonlinguistic category learning in aphasia. Twelve control participants without aphasia and 53 participants with aphasia (PWA) completed a computerized feedback-based category learning task consisting of training and testing phases. Accuracy rates of categorization in testing phases were calculated. To evaluate strategy use, strategy analyses were conducted over training and testing phases. Participant data were compared with model data that simulated complex multi-cue, single feature, and random pattern strategies. Learning success and strategy use were evaluated within the context of standardized cognitive-linguistic assessments. Categorization accuracy was higher among control participants than among PWA. The majority of control participants implemented suboptimal or optimal multi-cue and single-feature strategies by testing phases of the experiment. In contrast, a large subgroup of PWA implemented random patterns, or no strategy, during both training and testing phases of the experiment. Person-to-person variability arises not only in category learning ability but also in the strategies implemented to complete category learning tasks. PWA less frequently developed effective strategies during category learning tasks than control participants. Certain PWA may have impairments of strategy development or feedback processing not captured by language and currently probed cognitive abilities.

  6. Learning and tuning fuzzy logic controllers through reinforcements.

    PubMed

    Berenji, H R; Khedkar, P

    1992-01-01

    A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  7. Remedial and Compensatory Reading Instruction: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," July through December 1981 (Vol. 42 Nos. 1 through 6).

    ERIC Educational Resources Information Center

    ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

    This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 35 titles deal with a variety of topics, including the following: (1) the oral, written, and reading syntax of 46 learning disabled children; (2) the effects of oral reading rate and reinforcement on reading comprehension; (3)…

  8. 12 Math Rules That Expire in the Middle Grades

    ERIC Educational Resources Information Center

    Karp, Karen S.; Bush, Sarah B.; Dougherty, Barbara J.

    2015-01-01

    Many rules taught in mathematics classrooms "expire" when students develop knowledge that is more sophisticated, such as using new number systems. For example, in elementary grades, students are sometimes taught that "addition makes bigger" or "subtraction makes smaller" when learning to compute with whole numbers,…

  9. Collaboration rules.

    PubMed

    Evans, Philip; Wolf, Bob

    2005-01-01

    Corporate leaders seeking to boost growth, learning, and innovation may find the answer in a surprising place: the Linux open-source software community. Linux is developed by an essentially volunteer, self-organizing community of thousands of programmers. Most leaders would sell their grandmothers for workforces that collaborate as efficiently, frictionlessly, and creatively as the self-styled Linux hackers. But Linux is software, and software is hardly a model for mainstream business. The authors have, nonetheless, found surprising parallels between the anarchistic, caffeinated, hirsute world of Linux hackers and the disciplined, tea-sipping, clean-cut world of Toyota engineering. Specifically, Toyota and Linux operate by rules that blend the self-organizing advantages of markets with the low transaction costs of hierarchies. In place of markets' cash and contracts and hierarchies' authority are rules about how individuals and groups work together (with rigorous discipline); how they communicate (widely and with granularity); and how leaders guide them toward a common goal (through example). Those rules, augmented by simple communication technologies and a lack of legal barriers to sharing information, create rich common knowledge, the ability to organize teams modularly, extraordinary motivation, and high levels of trust, which radically lowers transaction costs. Low transaction costs, in turn, make it profitable for organizations to perform more and smaller transactions--and so increase the pace and flexibility typical of high-performance organizations. Once the system achieves critical mass, it feeds on itself. The larger the system, the more broadly shared the knowledge, language, and work style. The greater individuals' reputational capital, the louder the applause and the stronger the motivation. The success of Linux is evidence of the power of that virtuous circle. Toyota's success is evidence that it is also powerful in conventional companies.

  10. From Sailing Ships to Subtraction Symbols: Multiple Representations to Support Abstraction

    ERIC Educational Resources Information Center

    Jao, Limin

    2013-01-01

    Teachers are tasked with supporting students' learning of abstract mathematical concepts. Students can represent their mathematical understanding in a variety of modes, for example: manipulatives, pictures, diagrams, spoken languages, and written symbols. Although most students easily pick up rudimentary knowledge through the use of concrete…

  11. Mental Transformation Skill in Young Children: The Role of Concrete and Abstract Motor Training.

    PubMed

    Levine, Susan C; Goldin-Meadow, Susan; Carlson, Matthew T; Hemani-Lopez, Naureen

    2018-05-01

    We examined the effects of three different training conditions, all of which involve the motor system, on kindergarteners' mental transformation skill. We focused on three main questions. First, we asked whether training that involves making a motor movement that is relevant to the mental transformation-either concretely through action (action training) or more abstractly through gestural movements that represent the action (move-gesture training)-resulted in greater gains than training using motor movements irrelevant to the mental transformation (point-gesture training). We tested children prior to training, immediately after training (posttest), and 1 week after training (retest), and we found greater improvement in mental transformation skill in both the action and move-gesture training conditions than in the point-gesture condition, at both posttest and retest. Second, we asked whether the total gain made by retest differed depending on the abstractness of the movement-relevant training (action vs. move-gesture), and we found that it did not. Finally, we asked whether the time course of improvement differed for the two movement-relevant conditions, and we found that it did-gains in the action condition were realized immediately at posttest, with no further gains at retest; gains in the move-gesture condition were realized throughout, with comparable gains from pretest-to-posttest and from posttest-to-retest. Training that involves movement, whether concrete or abstract, can thus benefit children's mental transformation skill. However, the benefits unfold differently over time-the benefits of concrete training unfold immediately after training (online learning); the benefits of more abstract training unfold in equal steps immediately after training (online learning) and during the intervening week with no additional training (offline learning). These findings have implications for the kinds of instruction that can best support spatial learning. Copyright

  12. The role of feedback contingency in perceptual category learning.

    PubMed

    Ashby, F Gregory; Vucovich, Lauren E

    2016-11-01

    Feedback is highly contingent on behavior if it eventually becomes easy to predict, and weakly contingent on behavior if it remains difficult or impossible to predict even after learning is complete. Many studies have demonstrated that humans and nonhuman animals are highly sensitive to feedback contingency, but no known studies have examined how feedback contingency affects category learning, and current theories assign little or no importance to this variable. Two experiments examined the effects of contingency degradation on rule-based and information-integration category learning. In rule-based tasks, optimal accuracy is possible with a simple explicit rule, whereas optimal accuracy in information-integration tasks requires integrating information from 2 or more incommensurable perceptual dimensions. In both experiments, participants each learned rule-based or information-integration categories under either high or low levels of feedback contingency. The exact same stimuli were used in all 4 conditions, and optimal accuracy was identical in every condition. Learning was good in both high-contingency conditions, but most participants showed little or no evidence of learning in either low-contingency condition. Possible causes of these effects, as well as their theoretical implications, are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  13. Theories of Artificial Grammar Learning

    ERIC Educational Resources Information Center

    Pothos, Emmanuel M.

    2007-01-01

    Artificial grammar learning (AGL) is one of the most commonly used paradigms for the study of implicit learning and the contrast between rules, similarity, and associative learning. Despite five decades of extensive research, however, a satisfactory theoretical consensus has not been forthcoming. Theoretical accounts of AGL are reviewed, together…

  14. How Can Students Generalize the Chain Rule? The Roles of Abduction in Mathematical Modeling

    ERIC Educational Resources Information Center

    Park, Jin Hyeong; Lee, Kyeong-Hwa

    2016-01-01

    The purpose of this study is to design a modeling task to facilitate students' inquiries into the chain rule in calculus and to analyze the results after implementation of the task. In this study, we take a modeling approach to the teaching and learning of the chain rule by facilitating the generalization of students' models and modeling…

  15. Adaptive WTA with an analog VLSI neuromorphic learning chip.

    PubMed

    Häfliger, Philipp

    2007-03-01

    In this paper, we demonstrate how a particular spike-based learning rule (where exact temporal relations between input and output spikes of a spiking model neuron determine the changes of the synaptic weights) can be tuned to express rate-based classical Hebbian learning behavior (where the average input and output spike rates are sufficient to describe the synaptic changes). This shift in behavior is controlled by the input statistic and by a single time constant. The learning rule has been implemented in a neuromorphic very large scale integration (VLSI) chip as part of a neurally inspired spike signal image processing system. The latter is the result of the European Union research project Convolution AER Vision Architecture for Real-Time (CAVIAR). Since it is implemented as a spike-based learning rule (which is most convenient in the overall spike-based system), even if it is tuned to show rate behavior, no explicit long-term average signals are computed on the chip. We show the rule's rate-based Hebbian learning ability in a classification task in both simulation and chip experiment, first with artificial stimuli and then with sensor input from the CAVIAR system.

  16. Learning non-local dependencies.

    PubMed

    Kuhn, Gustav; Dienes, Zoltán

    2008-01-01

    This paper addresses the nature of the temporary storage buffer used in implicit or statistical learning. Kuhn and Dienes [Kuhn, G., and Dienes, Z. (2005). Implicit learning of nonlocal musical rules: implicitly learning more than chunks. Journal of Experimental Psychology-Learning Memory and Cognition, 31(6) 1417-1432] showed that people could implicitly learn a musical rule that was solely based on non-local dependencies. These results seriously challenge models of implicit learning that assume knowledge merely takes the form of linking adjacent elements (chunking). We compare two models that use a buffer to allow learning of long distance dependencies, the Simple Recurrent Network (SRN) and the memory buffer model. We argue that these models - as models of the mind - should not be evaluated simply by fitting them to human data but by determining the characteristic behaviour of each model. Simulations showed for the first time that the SRN could rapidly learn non-local dependencies. However, the characteristic performance of the memory buffer model rather than SRN more closely matched how people came to like different musical structures. We conclude that the SRN is more powerful than previous demonstrations have shown, but it's flexible learned buffer does not explain people's implicit learning (at least, the affective learning of musical structures) as well as fixed memory buffer models do.

  17. Selection of Learning Media Mathematics for Junior School Students

    ERIC Educational Resources Information Center

    Widodo, Sri Adi; Wahyudin

    2018-01-01

    One of the factors that determine the success of mathematics learning is the learning media used. Learning media can help students to create mathematical abstract mathematics that is abstract. In addition to media, meaningful learning is a learning that is adapted to the students' cognitive development. According to Piaget, junior high school…

  18. Analysis, Simulation, and Verification of Knowledge-Based, Rule-Based, and Expert Systems

    NASA Technical Reports Server (NTRS)

    Hinchey, Mike; Rash, James; Erickson, John; Gracanin, Denis; Rouff, Chris

    2010-01-01

    Mathematically sound techniques are used to view a knowledge-based system (KBS) as a set of processes executing in parallel and being enabled in response to specific rules being fired. The set of processes can be manipulated, examined, analyzed, and used in a simulation. The tool that embodies this technology may warn developers of errors in their rules, but may also highlight rules (or sets of rules) in the system that are underspecified (or overspecified) and need to be corrected for the KBS to operate as intended. The rules embodied in a KBS specify the allowed situations, events, and/or results of the system they describe. In that sense, they provide a very abstract specification of a system. The system is implemented through the combination of the system specification together with an appropriate inference engine, independent of the algorithm used in that inference engine. Viewing the rule base as a major component of the specification, and choosing an appropriate specification notation to represent it, reveals how additional power can be derived from an approach to the knowledge-base system that involves analysis, simulation, and verification. This innovative approach requires no special knowledge of the rules, and allows a general approach where standardized analysis, verification, simulation, and model checking techniques can be applied to the KBS.

  19. Struggling To Understand Abstract Science Topics: A Roundhouse Diagram-Based Study.

    ERIC Educational Resources Information Center

    Ward, Robin E.; Wandersee, James H.

    2002-01-01

    Explores the effects of Roundhouse diagram construction on a previously low-performing middle school science student's struggles to understand abstract science concepts and principles. Based on a metacognition-based visual learning model, aims to elucidate the process by which Roundhouse diagramming helps learners bootstrap their current…

  20. Functional Neuroanatomy of Contextual Acquisition of Concrete and Abstract Words

    ERIC Educational Resources Information Center

    Mestres-Misse, Anna; Munte, Thomas F.; Rodriguez-Fornells, Antoni

    2009-01-01

    The meaning of a novel word can be acquired by extracting it from linguistic context. Here we simulated word learning of new words associated to concrete and abstract concepts in a variant of the human simulation paradigm that provided linguistic context information in order to characterize the brain systems involved. Native speakers of Spanish…

  1. A case for restricted-domain relational learning.

    PubMed

    Wright, Anthony A; Katz, Jeffrey S

    2009-10-01

    Monkeys and pigeons learned a same/different task with pairs that were selected from a training set of eight picture stimuli. They showed no novel-stimulus transfer and hence no abstract-concept learning. They were also tested with novel pairs of the eight training pictures (i.e., combinations that had not been used in training) and with inverted pictures of the training pairs. If the subjects had learned the task item-specifically (e.g., if-then or configural learning), they should have failed these tests, but they performed well with novel combinations of training pictures and inverted pictures, suggesting that they learned the task relationally (i.e., on the basis of the relationship between the two pictures that were presented in each trial). This somewhat paradoxical conclusion of relational learning in the absence of abstract-concept learning is contrary to most theories of abstract-concept learning. The implications of this conclusion are discussed in the context of restricted-domain relational learning.

  2. Dopamine neurons modulate pheromone responses in Drosophila courtship learning.

    PubMed

    Keleman, Krystyna; Vrontou, Eleftheria; Krüttner, Sebastian; Yu, Jai Y; Kurtovic-Kozaric, Amina; Dickson, Barry J

    2012-09-06

    Learning through trial-and-error interactions allows animals to adapt innate behavioural ‘rules of thumb’ to the local environment, improving their prospects for survival and reproduction. Naive Drosophila melanogaster males, for example, court both virgin and mated females, but learn through experience to selectively suppress futile courtship towards females that have already mated. Here we show that courtship learning reflects an enhanced response to the male pheromone cis-vaccenyl acetate (cVA), which is deposited on females during mating and thus distinguishes mated females from virgins. Dissociation experiments suggest a simple learning rule in which unsuccessful courtship enhances sensitivity to cVA. The learning experience can be mimicked by artificial activation of dopaminergic neurons, and we identify a specific class of dopaminergic neuron that is critical for courtship learning. These neurons provide input to the mushroom body (MB) γ lobe, and the DopR1 dopamine receptor is required in MBγ neurons for both natural and artificial courtship learning. Our work thus reveals critical behavioural, cellular and molecular components of the learning rule by which Drosophila adjusts its innate mating strategy according to experience.

  3. Input and Age-Dependent Variation in Second Language Learning: A Connectionist Account.

    PubMed

    Janciauskas, Marius; Chang, Franklin

    2017-07-26

    Language learning requires linguistic input, but several studies have found that knowledge of second language (L2) rules does not seem to improve with more language exposure (e.g., Johnson & Newport, 1989). One reason for this is that previous studies did not factor out variation due to the different rules tested. To examine this issue, we reanalyzed grammaticality judgment scores in Flege, Yeni-Komshian, and Liu's (1999) study of L2 learners using rule-related predictors and found that, in addition to the overall drop in performance due to a sensitive period, L2 knowledge increased with years of input. Knowledge of different grammar rules was negatively associated with input frequency of those rules. To better understand these effects, we modeled the results using a connectionist model that was trained using Korean as a first language (L1) and then English as an L2. To explain the sensitive period in L2 learning, the model's learning rate was reduced in an age-related manner. By assigning different learning rates for syntax and lexical learning, we were able to model the difference between early and late L2 learners in input sensitivity. The model's learning mechanism allowed transfer between the L1 and L2, and this helped to explain the differences between different rules in the grammaticality judgment task. This work demonstrates that an L1 model of learning and processing can be adapted to provide an explicit account of how the input and the sensitive period interact in L2 learning. © 2017 The Authors. Cognitive Science - A Multidisciplinary Journal published by Wiley Periodicals, Inc.

  4. Bring It On, Complexity! Present and Future of Self-Organising Middle-Out Abstraction

    NASA Astrophysics Data System (ADS)

    Mammen, Sebastian Von; Steghöfer, Jan-Philipp

    The following sections are included: * The Great Complexity Challenge * Self-Organising Middle-Out Abstraction * Optimising Graphics, Physics and Artificial Intelligence * Emergence and Hierarchies in a Natural System * The Technical Concept of SOMO * Observation of interactions * Interaction pattern recognition and behavioural abstraction * Creating and adjusting hierarchies * Confidence measures * Execution model * Learning SOMO: parameters, knowledge propagation, and procreation * Current Implementations * Awareness Beyond Virtuality * Integration and emergence * Model inference * SOMO net * SOMO after me * The Future of SOMO

  5. Machine learning with quantum relative entropy

    NASA Astrophysics Data System (ADS)

    Tsuda, Koji

    2009-12-01

    Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

  6. Accurate crop classification using hierarchical genetic fuzzy rule-based systems

    NASA Astrophysics Data System (ADS)

    Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

    2014-10-01

    This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

  7. Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit.

    PubMed

    Quintián, Héctor; Corchado, Emilio

    2017-09-01

    In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data's internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback. The derived learning rules can be linked to an adaptive form of Exploratory Projection Pursuit and with artificial distributions, the networks perform as the theory suggests they should: the use of different learning rules derived from different PDFs allows the identification of "interesting" dimensions (as far from the Gaussian distribution as possible) in high-dimensional datasets. This novel algorithm, BHL, has been tested over seven artificial datasets to study the behavior of BHL parameters, and was later applied successfully over four real datasets, comparing its results, in terms of performance, with other well-known Exploratory and projection models such as Maximum Likelihood Hebbian Learning (MLHL), Locally-Linear Embedding (LLE), Curvilinear Component Analysis (CCA), Isomap and Neural Principal Component Analysis (Neural PCA).

  8. Abstract representations of associated emotions in the human brain.

    PubMed

    Kim, Junsuk; Schultz, Johannes; Rohe, Tim; Wallraven, Christian; Lee, Seong-Whan; Bülthoff, Heinrich H

    2015-04-08

    Emotions can be aroused by various kinds of stimulus modalities. Recent neuroimaging studies indicate that several brain regions represent emotions at an abstract level, i.e., independently from the sensory cues from which they are perceived (e.g., face, body, or voice stimuli). If emotions are indeed represented at such an abstract level, then these abstract representations should also be activated by the memory of an emotional event. We tested this hypothesis by asking human participants to learn associations between emotional stimuli (videos of faces or bodies) and non-emotional stimuli (fractals). After successful learning, fMRI signals were recorded during the presentations of emotional stimuli and emotion-associated fractals. We tested whether emotions could be decoded from fMRI signals evoked by the fractal stimuli using a classifier trained on the responses to the emotional stimuli (and vice versa). This was implemented as a whole-brain searchlight, multivoxel activation pattern analysis, which revealed successful emotion decoding in four brain regions: posterior cingulate cortex (PCC), precuneus, MPFC, and angular gyrus. The same analysis run only on responses to emotional stimuli revealed clusters in PCC, precuneus, and MPFC. Multidimensional scaling analysis of the activation patterns revealed clear clustering of responses by emotion across stimulus types. Our results suggest that PCC, precuneus, and MPFC contain representations of emotions that can be evoked by stimuli that carry emotional information themselves or by stimuli that evoke memories of emotional stimuli, while angular gyrus is more likely to take part in emotional memory retrieval. Copyright © 2015 the authors 0270-6474/15/355655-09$15.00/0.

  9. Effective Bayesian Transfer Learning

    DTIC Science & Technology

    2010-03-01

    reasonable value of k , defined by the task B training set size. Transfer Regret 1 Regret = 100 * G AB B No Transfer With Transfer AB...a. REPORT U b . ABSTRACT U c. THIS PAGE U 19b. TELEPHONE NUMBER (Include area code) N/A Standard Form 298 (Rev. 8-98) Prescribed...rule set given the prior and developed staged approximate inference strategy, in which data from observed tasks 1 to k are used to infer general rule

  10. Inductive learning of thyroid functional states using the ID3 algorithm. The effect of poor examples on the learning result.

    PubMed

    Forsström, J

    1992-01-01

    The ID3 algorithm for inductive learning was tested using preclassified material for patients suspected to have a thyroid illness. Classification followed a rule-based expert system for the diagnosis of thyroid function. Thus, the knowledge to be learned was limited to the rules existing in the knowledge base of that expert system. The learning capability of the ID3 algorithm was tested with an unselected learning material (with some inherent missing data) and with a selected learning material (no missing data). The selected learning material was a subgroup which formed a part of the unselected learning material. When the number of learning cases was increased, the accuracy of the program improved. When the learning material was large enough, an increase in the learning material did not improve the results further. A better learning result was achieved with the selected learning material not including missing data as compared to unselected learning material. With this material we demonstrate a weakness in the ID3 algorithm: it can not find available information from good example cases if we add poor examples to the data.

  11. Adding dynamic rules to self-organizing fuzzy systems

    NASA Technical Reports Server (NTRS)

    Buhusi, Catalin V.

    1992-01-01

    This paper develops a Dynamic Self-Organizing Fuzzy System (DSOFS) capable of adding, removing, and/or adapting the fuzzy rules and the fuzzy reference sets. The DSOFS background consists of a self-organizing neural structure with neuron relocation features which will develop a map of the input-output behavior. The relocation algorithm extends the topological ordering concept. Fuzzy rules (neurons) are dynamically added or released while the neural structure learns the pattern. The DSOFS advantages are the automatic synthesis and the possibility of parallel implementation. A high adaptation speed and a reduced number of neurons is needed in order to keep errors under some limits. The computer simulation results are presented in a nonlinear systems modelling application.

  12. An Examination of Strategy Implementation during Abstract Nonlinguistic Category Learning in Aphasia

    ERIC Educational Resources Information Center

    Vallila-Rohter, Sofia; Kiran, Swathi

    2015-01-01

    Purpose: Our purpose was to study strategy use during nonlinguistic category learning in aphasia. Method: Twelve control participants without aphasia and 53 participants with aphasia (PWA) completed a computerized feedback-based category learning task consisting of training and testing phases. Accuracy rates of categorization in testing phases…

  13. NASA Patent Abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 21) Abstracts

    NASA Technical Reports Server (NTRS)

    1982-01-01

    Abstracts are cited for 87 patents and applications introduced into the NASA scientific and technical information system during the period of January 1982 through June 1982. Each entry consists of a citation, an abstract, and in mose cases, a key illustration selected from the patent or patent application.

  14. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  15. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  16. Do Training Institutions Learn from Experience?

    ERIC Educational Resources Information Center

    Castro, Claudio de Moura

    2011-01-01

    This essay is concerned with organisational learning and, in particular, policy learning. Sometimes, organisations learn; sometimes they do not. Sometimes they use their acquired knowledge, but this is, not always the case. Inside organisations, particular values, rules and incentive systems are developed. In addition, organisations need to…

  17. Experiential Learning Theory as One of the Foundations of Adult Learning Practice Worldwide

    ERIC Educational Resources Information Center

    Dernova, Maiya

    2015-01-01

    The paper presents the analysis of existing theory, assumptions, and models of adult experiential learning. The experiential learning is a learning based on a learning cycle guided by the dual dialectics of action-reflection and experience-abstraction. It defines learning as a process of knowledge creation through experience transformation, so…

  18. Abstract concepts, language and sociality: from acquisition to inner speech.

    PubMed

    Borghi, Anna M; Barca, Laura; Binkofski, Ferdinand; Tummolini, Luca

    2018-08-05

    The problem of representation of abstract concepts, such as 'freedom' and 'justice', has become particularly crucial in recent years, owing to the increased success of embodied and grounded views of cognition. We will present a novel view on abstract concepts and abstract words. Since abstract concepts do not have single objects as referents, children and adults might rely more on input from others to learn them; we, therefore, suggest that linguistic and social experience play an important role for abstract concepts. We will discuss evidence obtained in our and other laboratories showing that processing of abstract concepts evokes linguistic interaction and social experiences, leading to the activation of the mouth motor system. We will discuss the possible mechanisms that underlie this activation. Mouth motor system activation can be due to re-enactment of the experience of conceptual acquisition, which occurred through the mediation of language. Alternatively, it could be due to the re-explanation of the word meaning, possibly through inner speech. Finally, it can be due to a metacognitive process revealing low confidence in the meaning of our concepts. This process induces in us the need to rely on others to ask/negotiate conceptual meaning. We conclude that with abstract concepts language works as a social tool: it extends our thinking abilities and pushes us to rely on others to integrate our knowledge.This article is part of the theme issue 'Varieties of abstract concepts: development, use, and representation in the brain'. © 2018 The Author(s).

  19. Grounding Abstractness: Abstract Concepts and the Activation of the Mouth

    PubMed Central

    Borghi, Anna M.; Zarcone, Edoardo

    2016-01-01

    One key issue for theories of cognition is how abstract concepts, such as freedom, are represented. According to the WAT (Words As social Tools) proposal, abstract concepts activate both sensorimotor and linguistic/social information, and their acquisition modality involves the linguistic experience more than the acquisition of concrete concepts. We report an experiment in which participants were presented with abstract and concrete definitions followed by concrete and abstract target-words. When the definition and the word matched, participants were required to press a key, either with the hand or with the mouth. Response times and accuracy were recorded. As predicted, we found that abstract definitions and abstract words yielded slower responses and more errors compared to concrete definitions and concrete words. More crucially, there was an interaction between the target-words and the effector used to respond (hand, mouth). While responses with the mouth were overall slower, the advantage of the hand over the mouth responses was more marked with concrete than with abstract concepts. The results are in keeping with grounded and embodied theories of cognition and support the WAT proposal, according to which abstract concepts evoke linguistic-social information, hence activate the mouth. The mechanisms underlying the mouth activation with abstract concepts (re-enactment of acquisition experience, or re-explanation of the word meaning, possibly through inner talk) are discussed. To our knowledge this is the first behavioral study demonstrating with real words that the advantage of the hand over the mouth is more marked with concrete than with abstract concepts, likely because of the activation of linguistic information with abstract concepts. PMID:27777563

  20. 26 CFR 1.1445-5 - Special rules concerning distributions and other transactions by corporations, partnerships...

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... of the transfer a partnership or fiduciary learns that a partner's or beneficiary's certification of... transfer a partnership or fiduciary learns that a corporation's statement (that an interest in the... a transfer of property in accordance with the rules of this section, then no additional tax is...

  1. 26 CFR 1.1445-5 - Special rules concerning distributions and other transactions by corporations, partnerships...

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... of the transfer a partnership or fiduciary learns that a partner's or beneficiary's certification of... transfer a partnership or fiduciary learns that a corporation's statement (that an interest in the... a transfer of property in accordance with the rules of this section, then no additional tax is...

  2. 26 CFR 1.1445-5 - Special rules concerning distributions and other transactions by corporations, partnerships...

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... of the transfer a partnership or fiduciary learns that a partner's or beneficiary's certification of... transfer a partnership or fiduciary learns that a corporation's statement (that an interest in the... a transfer of property in accordance with the rules of this section, then no additional tax is...

  3. 26 CFR 1.1445-5 - Special rules concerning distributions and other transactions by corporations, partnerships...

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... of the transfer a partnership or fiduciary learns that a partner's or beneficiary's certification of... transfer a partnership or fiduciary learns that a corporation's statement (that an interest in the... a transfer of property in accordance with the rules of this section, then no additional tax is...

  4. The development of display rule knowledge: linkages with family expressiveness and social competence.

    PubMed

    Jones, D C; Abbey, B B; Cumberland, A

    1998-08-01

    The development of display rule knowledge and its associations with family expressiveness (Study 1) and peer competence (Study 2) were investigated among elementary school children. In Study 1, the display rule knowledge of 121 kindergartners and third graders was assessed using validated hypothetical scenarios. There were significant grade differences in display rule knowledge such that third graders compared to kindergartners more frequently combined expression regulation with prosocial reasoning, norm-maintenance, and self-protective motives. Maternal reports of family emotional climates indicated that aspects of negative expressiveness were related positively to self-protective display rules and negatively to prosocial display rules. Study 2 included 93 third and fifth graders who reported on their display rule knowledge and on their emotional reactions and strategies to resolve peer conflict. Classmates and teachers provided ratings on social competence. Age differences for display rule knowledge were not documented, but prosocial display rules were most consistently related to hypothetical peer conflict responses and social competence. The findings confirm that display rule knowledge is related in consistent and systematic ways to what children learn within the family emotional context, how they propose to resolve peer conflict, and how they are perceived by peers and teachers.

  5. Learning Object-Level and Meta-Level Knowledge in Expert Systems.

    DTIC Science & Technology

    1985-11-01

    usually a misdiagnosed one). 1.2.2. Efficiency Consideration Learning becomes a complicated issue in a complex domain like medicine where there may... misdiagnosed cases are often due to missing rules. Therefore, we would rather view this problem as a learning problem. A strategy called "retrospective...inspection after learning" is described in Chapter 5. With this strategy, rules that can make the misdiagnosed case diagnosed correctly are first found; then

  6. Quest for the Golden Rule: An Effective Social Skills Promotion and Bullying Prevention Program

    ERIC Educational Resources Information Center

    Rubin-Vaughan, Alice; Pepler, Debra; Brown, Steven; Craig, Wendy

    2011-01-01

    Everyday many students face bullying situations that they are ill equipped to manage. E-learning has recently emerged as a potentially effective tool in teaching children social skills, in addition to academic subject matter. Quest for the Golden Rule is one of the first bullying prevention e-learning programs available, designed by the…

  7. Rule-Based Event Processing and Reaction Rules

    NASA Astrophysics Data System (ADS)

    Paschke, Adrian; Kozlenkov, Alexander

    Reaction rules and event processing technologies play a key role in making business and IT / Internet infrastructures more agile and active. While event processing is concerned with detecting events from large event clouds or streams in almost real-time, reaction rules are concerned with the invocation of actions in response to events and actionable situations. They state the conditions under which actions must be taken. In the last decades various reaction rule and event processing approaches have been developed, which for the most part have been advanced separately. In this paper we survey reaction rule approaches and rule-based event processing systems and languages.

  8. The Learning Factory.

    ERIC Educational Resources Information Center

    Alef, Edward R.; Berg, Daniel

    This book looks at the administration of an academic institution as a learning factory which needs to be run like a business, competing with other businesses offering the same service. The book begins with a discussion of the concepts of the continuing education learning market, the playing fields and rules of the games, as well as the idea of…

  9. Reading Achievement: Characteristics Associated with Success and Failure: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," July through September 1978 (Vol. 39 Nos. 1 through 3).

    ERIC Educational Resources Information Center

    ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

    This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 25 titles deal with a variety of topics, including the following: reading achievement as it relates to child dependency, the development of phonological coding, short-term memory and associative learning, variables available in…

  10. 77 FR 52977 - Regulatory Capital Rules: Advanced Approaches Risk-Based Capital Rule; Market Risk Capital Rule

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-08-30

    ... Corporation 12 CFR Parts 324, 325 Regulatory Capital Rules: Advanced Approaches Risk-Based Capital Rule... 325 RIN 3064-AD97 Regulatory Capital Rules: Advanced Approaches Risk-Based Capital Rule; Market Risk... the agencies' current capital rules. In this NPR (Advanced Approaches and Market Risk NPR) the...

  11. Learning in the machine: The symmetries of the deep learning channel.

    PubMed

    Baldi, Pierre; Sadowski, Peter; Lu, Zhiqin

    2017-11-01

    In a physical neural system, learning rules must be local both in space and time. In order for learning to occur, non-local information must be communicated to the deep synapses through a communication channel, the deep learning channel. We identify several possible architectures for this learning channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges: (1) symmetry of architectures; (2) symmetry of weights; (3) symmetry of neurons; (4) symmetry of derivatives; (5) symmetry of processing; and (6) symmetry of learning rules. Random backpropagation (RBP) addresses the second and third symmetry, and some of its variations, such as skipped RBP (SRBP) address the first and the fourth symmetry. Here we address the last two desirable symmetries showing through simulations that they can be achieved and that the learning channel is particularly robust to symmetry variations. Specifically, random backpropagation and its variations can be performed with the same non-linear neurons used in the main input-output forward channel, and the connections in the learning channel can be adapted using the same algorithm used in the forward channel, removing the need for any specialized hardware in the learning channel. Finally, we provide mathematical results in simple cases showing that the learning equations in the forward and backward channels converge to fixed points, for almost any initial conditions. In symmetric architectures, if the weights in both channels are small at initialization, adaptation in both channels leads to weights that are essentially symmetric during and after learning. Biological connections are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. When More Is Less: Feedback Effects in Perceptual Category Learning

    ERIC Educational Resources Information Center

    Maddox, W. Todd; Love, Bradley C.; Glass, Brian D.; Filoteo, J. Vincent

    2008-01-01

    Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether…

  13. Debate as Encapsulated Conflict: Ruled Controversy as an Approach to Learning Conflict Management Skills.

    ERIC Educational Resources Information Center

    Lee, David G.; Hensley, Carl Wayne

    Debate can provide a format for the development of communication skills to aid students in managing conflicts, because an understanding of rule-governed communication in conflict situations is invaluable in constructive conflict management. Since in debate procedural rules restrict discussion primarily to substantive and procedural topics, debate…

  14. Neuronal avalanches and learning

    NASA Astrophysics Data System (ADS)

    de Arcangelis, Lucilla

    2011-05-01

    Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

  15. Pushing the rules: effects and aftereffects of deliberate rule violations.

    PubMed

    Wirth, Robert; Pfister, Roland; Foerster, Anna; Huestegge, Lynn; Kunde, Wilfried

    2016-09-01

    Most of our daily life is organized around rules and social norms. But what makes rules so special? And what if one were to break a rule intentionally? Can we simply free us from the present set of rules or do we automatically adhere to them? How do rule violations influence subsequent behavior? To investigate the effects and aftereffects of violating simple S-R rule, we conducted three experiments that investigated continuous finger-tracking responses on an iPad. Our experiments show that rule violations are distinct from rule-based actions in both response times and movement trajectories, they take longer to initiate and execute, and their movement trajectory is heavily contorted. Data not only show differences between the two types of response (rule-based vs. violation), but also yielded a characteristic pattern of aftereffects in case of rule violations: rule violations do not trigger adaptation effects that render further rule violations less difficult, but every rule violation poses repeated effort on the agent. The study represents a first step towards understanding the signature and underlying mechanisms of deliberate rule violations, they cannot be acted out by themselves, but require the activation of the original rule first. Consequently, they are best understood as reformulations of existing rules that are not accessible on their own, but need to be constantly derived from the original rule, with an add-on that might entail an active tendency to steer away from mental representations that reflect (socially) unwanted behavior.

  16. Mechanisms of rule acquisition and rule following in inductive reasoning.

    PubMed

    Crescentini, Cristiano; Seyed-Allaei, Shima; De Pisapia, Nicola; Jovicich, Jorge; Amati, Daniele; Shallice, Tim

    2011-05-25

    Despite the recent interest in the neuroanatomy of inductive reasoning processes, the regional specificity within prefrontal cortex (PFC) for the different mechanisms involved in induction tasks remains to be determined. In this study, we used fMRI to investigate the contribution of PFC regions to rule acquisition (rule search and rule discovery) and rule following. Twenty-six healthy young adult participants were presented with a series of images of cards, each consisting of a set of circles numbered in sequence with one colored blue. Participants had to predict the position of the blue circle on the next card. The rules that had to be acquired pertained to the relationship among succeeding stimuli. Responses given by subjects were categorized in a series of phases either tapping rule acquisition (responses given up to and including rule discovery) or rule following (correct responses after rule acquisition). Mid-dorsolateral PFC (mid-DLPFC) was active during rule search and remained active until successful rule acquisition. By contrast, rule following was associated with activation in temporal, motor, and medial/anterior prefrontal cortex. Moreover, frontopolar cortex (FPC) was active throughout the rule acquisition and rule following phases before a rule became familiar. We attributed activation in mid-DLPFC to hypothesis generation and in FPC to integration of multiple separate inferences. The present study provides evidence that brain activation during inductive reasoning involves a complex network of frontal processes and that different subregions respond during rule acquisition and rule following phases.

  17. Cognitive Diffusion Model: Facilitating EFL Learning in an Authentic Environment

    ERIC Educational Resources Information Center

    Shadiev, Rustam; Hwang, Wu-Yuin; Huang, Yueh-Min; Liu, Tzu-Yu

    2017-01-01

    For this study, we designed learning activities in which students applied newly acquired knowledge to solve meaningful daily life problems in their local community--a real, familiar, and relevant environment for students. For example, students learned about signs and rules in class and then applied this new knowledge to create their own rules for…

  18. 18 CFR 385.104 - Rule of construction (Rule 104).

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 18 Conservation of Power and Water Resources 1 2011-04-01 2011-04-01 false Rule of construction (Rule 104). 385.104 Section 385.104 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... Definitions § 385.104 Rule of construction (Rule 104). To the extent that the text of a rule is inconsistent...

  19. 18 CFR 385.104 - Rule of construction (Rule 104).

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... 18 Conservation of Power and Water Resources 1 2010-04-01 2010-04-01 false Rule of construction (Rule 104). 385.104 Section 385.104 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... Definitions § 385.104 Rule of construction (Rule 104). To the extent that the text of a rule is inconsistent...

  20. Learning Display Rules: The Socialization of Emotion Expression in Infancy.

    ERIC Educational Resources Information Center

    Malatesta, Carol Zander; Haviland, Jeannette M.

    1982-01-01

    Develops a methodology for studying emotion socialization and examines the synchrony of mother and infant expressions to determine whether "instruction" in display rules is underway in early infancy and what the short-term effects of such instruction on infant expression might be. Sixty dyads were videotaped during play and reunion after brief…

  1. Phonological reduplication in sign language: Rules rule

    PubMed Central

    Berent, Iris; Dupuis, Amanda; Brentari, Diane

    2014-01-01

    Productivity—the hallmark of linguistic competence—is typically attributed to algebraic rules that support broad generalizations. Past research on spoken language has documented such generalizations in both adults and infants. But whether algebraic rules form part of the linguistic competence of signers remains unknown. To address this question, here we gauge the generalization afforded by American Sign Language (ASL). As a case study, we examine reduplication (X→XX)—a rule that, inter alia, generates ASL nouns from verbs. If signers encode this rule, then they should freely extend it to novel syllables, including ones with features that are unattested in ASL. And since reduplicated disyllables are preferred in ASL, such a rule should favor novel reduplicated signs. Novel reduplicated signs should thus be preferred to nonreduplicative controls (in rating), and consequently, such stimuli should also be harder to classify as nonsigns (in the lexical decision task). The results of four experiments support this prediction. These findings suggest that the phonological knowledge of signers includes powerful algebraic rules. The convergence between these conclusions and previous evidence for phonological rules in spoken language suggests that the architecture of the phonological mind is partly amodal. PMID:24959158

  2. Leadership for Service Learning

    ERIC Educational Resources Information Center

    Billig, Shelley H.

    2010-01-01

    Service learning is a promising practice, but like other instructional practices, it must be done well to produce positive outcomes. This author has found that following five "rules" of service learning will likely result in improved academic engagement and positive academic, civic, and social outcomes among students. Energy and collaboration…

  3. Reading Achievement: Characteristics Associated with Success and Failure: Abstracts of Doctoral Dissertations Published in "Dissertation Abstracts International," January through June 1982 (Vol. 42 Nos. 7 through 12).

    ERIC Educational Resources Information Center

    ERIC Clearinghouse on Reading and Communication Skills, Urbana, IL.

    This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 16 titles deal with a variety of topics, including the following: (1) attitudes and reading achievement in an innovative high school; (2) the interaction of learning styles and word type as they affect word recognition among…

  4. Adaptive structured dictionary learning for image fusion based on group-sparse-representation

    NASA Astrophysics Data System (ADS)

    Yang, Jiajie; Sun, Bin; Luo, Chengwei; Wu, Yuzhong; Xu, Limei

    2018-04-01

    Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a l1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.

  5. Level of Abstraction and Feelings of Presence in Virtual Space: Business English Negotiation in Open Wonderland

    ERIC Educational Resources Information Center

    Chen, Judy F.; Warden, Clyde A.; Tai, David Wen-Shung; Chen, Farn-Shing; Chao, Chich-Yang

    2011-01-01

    Virtual spaces allow abstract representations of reality that not only encourage student self-directed learning but also reinforce core content of the learning objective through visual metaphors not reproducible in the physical world. One of the advantages of such a space is the ability to escape the restrictions of the physical classroom, yet…

  6. The Feedback-related Negativity Codes Components of Abstract Inference during Reward-based Decision-making.

    PubMed

    Reiter, Andrea M F; Koch, Stefan P; Schröger, Erich; Hinrichs, Hermann; Heinze, Hans-Jochen; Deserno, Lorenz; Schlagenhauf, Florian

    2016-08-01

    Behavioral control is influenced not only by learning from the choices made and the rewards obtained but also by "what might have happened," that is, inference about unchosen options and their fictive outcomes. Substantial progress has been made in understanding the neural signatures of direct learning from choices that are actually made and their associated rewards via reward prediction errors (RPEs). However, electrophysiological correlates of abstract inference in decision-making are less clear. One seminal theory suggests that the so-called feedback-related negativity (FRN), an ERP peaking 200-300 msec after a feedback stimulus at frontocentral sites of the scalp, codes RPEs. Hitherto, the FRN has been predominantly related to a so-called "model-free" RPE: The difference between the observed outcome and what had been expected. Here, by means of computational modeling of choice behavior, we show that individuals employ abstract, "double-update" inference on the task structure by concurrently tracking values of chosen stimuli (associated with observed outcomes) and unchosen stimuli (linked to fictive outcomes). In a parametric analysis, model-free RPEs as well as their modification because of abstract inference were regressed against single-trial FRN amplitudes. We demonstrate that components related to abstract inference uniquely explain variance in the FRN beyond model-free RPEs. These findings advance our understanding of the FRN and its role in behavioral adaptation. This might further the investigation of disturbed abstract inference, as proposed, for example, for psychiatric disorders, and its underlying neural correlates.

  7. Non-formal learning and tacit knowledge in professional work.

    PubMed

    Eraut, M

    2000-03-01

    This paper explores the conceptual and methodological problems arising from several empirical investigations of professional education and learning in the workplace. 1. To clarify the multiple meanings accorded to terms such as 'non-formal learning', 'implicit learning' and 'tacit knowledge', their theoretical assumptions and the range of phenomena to which they refer. 2. To discuss their implications for professional practice. A largely theoretical analysis of issues and phenomena arising from empirical investigations. The author's typology of non-formal learning distinguishes between implicit learning, reactive on-the-spot learning and deliberative learning. The significance of the last is commonly overemphasized. The problematic nature of tacit knowledge is discussed with respect to both detecting it and representing it. Three types of tacit knowledge are discussed: tacit understanding of people and situations, routinized actions and the tacit rules that underpin intuitive decision-making. They come together when professional performance involves sequences of routinized action punctuated by rapid intuitive decisions based on tacit understanding of the situation. Four types of process are involved--reading the situation, making decisions, overt activity and metacognition--and three modes of cognition--intuitive, analytic and deliberative. The balance between these modes depends on time, experience and complexity. Where rapid action dominates, periods of deliberation are needed to maintain critical control. Finally the role of both formal and informal social knowledge is discussed; and it is argued that situated learning often leads not to local conformity but to greater individual variation as people's careers take them through a series of different contexts. This abstract necessarily simplifies a more complex analysis in the paper itself.

  8. Competitive STDP Learning of Overlapping Spatial Patterns.

    PubMed

    Krunglevicius, Dalius

    2015-08-01

    Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron's responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron's forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.

  9. Transfer between local and global processing levels by pigeons (Columba livia) and humans (Homo sapiens) in exemplar- and rule-based categorization tasks.

    PubMed

    Aust, Ulrike; Braunöder, Elisabeth

    2015-02-01

    The present experiment investigated pigeons' and humans' processing styles-local or global-in an exemplar-based visual categorization task in which category membership of every stimulus had to be learned individually, and in a rule-based task in which category membership was defined by a perceptual rule. Group Intact was trained with the original pictures (providing both intact local and global information), Group Scrambled was trained with scrambled versions of the same pictures (impairing global information), and Group Blurred was trained with blurred versions (impairing local information). Subsequently, all subjects were tested for transfer to the 2 untrained presentation modes. Humans outperformed pigeons regarding learning speed and accuracy as well as transfer performance and showed good learning irrespective of group assignment, whereas the pigeons of Group Blurred needed longer to learn the training tasks than the pigeons of Groups Intact and Scrambled. Also, whereas humans generalized equally well to any novel presentation mode, pigeons' transfer from and to blurred stimuli was impaired. Both species showed faster learning and, for the most part, better transfer in the rule-based than in the exemplar-based task, but there was no evidence of the used processing mode depending on the type of task (exemplar- or rule-based). Whereas pigeons relied on local information throughout, humans did not show a preference for either processing level. Additional tests with grayscale versions of the training stimuli, with versions that were both blurred and scrambled, and with novel instances of the rule-based task confirmed and further extended these findings. PsycINFO Database Record (c) 2015 APA, all rights reserved.

  10. Applying Suffix Rules to Organization Name Recognition

    NASA Astrophysics Data System (ADS)

    Inui, Takashi; Murakami, Koji; Hashimoto, Taiichi; Utsumi, Kazuo; Ishikawa, Masamichi

    This paper presents a method for boosting the performance of the organization name recognition, which is a part of named entity recognition (NER). Although gazetteers (lists of the NEs) have been known as one of the effective features for supervised machine learning approaches on the NER task, the previous methods which have applied the gazetteers to the NER were very simple. The gazetteers have been used just for searching the exact matches between input text and NEs included in them. The proposed method generates regular expression rules from gazetteers, and, with these rules, it can realize a high-coverage searches based on looser matches between input text and NEs. To generate these rules, we focus on the two well-known characteristics of NE expressions; 1) most of NE expressions can be divided into two parts, class-reference part and instance-reference part, 2) for most of NE expressions the class-reference parts are located at the suffix position of them. A pattern mining algorithm runs on the set of NEs in the gazetteers, and some frequent word sequences from which NEs are constructed are found. Then, we employ only word sequences which have the class-reference part at the suffix position as suffix rules. Experimental results showed that our proposed method improved the performance of the organization name recognition, and achieved the 84.58 F-value for evaluation data.

  11. Abstract analogical reasoning in high-functioning children with autism spectrum disorders.

    PubMed

    Green, Adam E; Kenworthy, Lauren; Mosner, Maya G; Gallagher, Natalie M; Fearon, Edward W; Balhana, Carlos D; Yerys, Benjamin E

    2014-12-01

    Children with autism spectrum disorders (ASD) exhibit a deficit in spontaneously recognizing abstract similarities that are crucial for generalizing learning to new situations. This may contribute to deficits in the development of appropriate schemas for navigating novel situations, including social interactions. Analogical reasoning is the central cognitive mechanism that enables typically developing children to understand abstract similarities between different situations. Intriguingly, studies of high-functioning children with ASD point to a relative cognitive strength in basic, nonabstract forms of analogical reasoning. If this analogical reasoning ability extends to abstract analogical reasoning (i.e., between superficially dissimilar situations), it may provide a bridge between a cognitive capability and core ASD deficits in areas such as generalization and categorization. This study tested whether preserved analogical reasoning abilities in ASD can be extended to abstract analogical reasoning, using photographs of real-world items and situations. Abstractness of the analogies was determined via a quantitative measure of semantic distance derived from latent semantic analysis. Children with ASD performed as well as typically developing children at identifying abstract analogical similarities when explicitly instructed to apply analogical reasoning. Individual differences in abstract analogical reasoning ability predicted individual differences in a measure of social function in the ASD group. Preliminary analyses indicated that children with ASD, but not typically developing children, showed an effect of age on abstract analogical reasoning. These results provide new evidence that children with ASD are capable of identifying abstract similarities through analogical reasoning, pointing to abstract analogical reasoning as a potential lever for improving generalization skills and social function in ASD. © 2014 International Society for Autism Research, Wiley

  12. A high-level language for rule-based modelling.

    PubMed

    Pedersen, Michael; Phillips, Andrew; Plotkin, Gordon D

    2015-01-01

    Rule-based languages such as Kappa excel in their support for handling the combinatorial complexities prevalent in many biological systems, including signalling pathways. But Kappa provides little structure for organising rules, and large models can therefore be hard to read and maintain. This paper introduces a high-level, modular extension of Kappa called LBS-κ. We demonstrate the constructs of the language through examples and three case studies: a chemotaxis switch ring, a MAPK cascade, and an insulin signalling pathway. We then provide a formal definition of LBS-κ through an abstract syntax and a translation to plain Kappa. The translation is implemented in a compiler tool which is available as a web application. We finally demonstrate how to increase the expressivity of LBS-κ through embedded scripts in a general-purpose programming language, a technique which we view as generally applicable to other domain specific languages.

  13. A High-Level Language for Rule-Based Modelling

    PubMed Central

    Pedersen, Michael; Phillips, Andrew; Plotkin, Gordon D.

    2015-01-01

    Rule-based languages such as Kappa excel in their support for handling the combinatorial complexities prevalent in many biological systems, including signalling pathways. But Kappa provides little structure for organising rules, and large models can therefore be hard to read and maintain. This paper introduces a high-level, modular extension of Kappa called LBS-κ. We demonstrate the constructs of the language through examples and three case studies: a chemotaxis switch ring, a MAPK cascade, and an insulin signalling pathway. We then provide a formal definition of LBS-κ through an abstract syntax and a translation to plain Kappa. The translation is implemented in a compiler tool which is available as a web application. We finally demonstrate how to increase the expressivity of LBS-κ through embedded scripts in a general-purpose programming language, a technique which we view as generally applicable to other domain specific languages. PMID:26043208

  14. Does job complexity mitigate the negative effect of emotion-rule dissonance on employee burnout?

    PubMed Central

    Kubicek, Bettina; Korunka, Christian

    2015-01-01

    ABSTRACT In interactions with clients or patients, human service workers are at risk of experiencing discrepancies between felt and organizationally mandated emotions (i.e. emotion-rule dissonance). Given the documented detrimental effects of such discrepancies on employee strain, the present study investigated whether job complexity mitigates the relation between emotion-rule dissonance and employee burnout using data from a two-wave panel study of eldercare workers (N = 583, 16-month time lag). Structural equation modelling revealed that emotion-rule dissonance at Time 1 preceded emotional exhaustion and depersonalization at Time 2. Beyond that, employees whose work offered job complexity were found to suffer less from emotional exhaustion and depersonalization when encountering discrepancies between felt and stipulated emotions compared to employees who conducted noncomplex work. Thus, designing complex tasks appears to be a crucial starting point for alleviating employee burnout in jobs that provoke emotion-rule dissonance. PMID:26681818

  15. RuleMonkey: software for stochastic simulation of rule-based models

    PubMed Central

    2010-01-01

    Background The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems. Results Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods. Conclusions RuleMonkey enables the simulation of

  16. A requirement for memory retrieval during and after long-term extinction learning

    PubMed Central

    Ouyang, Ming; Thomas, Steven A.

    2005-01-01

    Current learning theories are based on the idea that learning is driven by the difference between expectations and experience (the delta rule). In extinction, one learns that certain expectations no longer apply. Here, we test the potential validity of the delta rule by manipulating memory retrieval (and thus expectations) during extinction learning. Adrenergic signaling is critical for the time-limited retrieval (but not acquisition or consolidation) of contextual fear. Using genetic and pharmacologic approaches to manipulate adrenergic signaling, we find that long-term extinction requires memory retrieval but not conditioned responding. Identical manipulations of the adrenergic system that do not affect memory retrieval do not alter extinction. The results provide substantial support for the delta rule of learning theory. In addition, the timing over which extinction is sensitive to adrenergic manipulation suggests a model whereby memory retrieval occurs during, and several hours after, extinction learning to consolidate long-term extinction memory. PMID:15947076

  17. 4 CFR 22.1 - Applicability of Rules [Rule 1].

    Code of Federal Regulations, 2010 CFR

    2010-01-01

    ... 4 Accounts 1 2010-01-01 2010-01-01 false Applicability of Rules [Rule 1]. 22.1 Section 22.1... ACCOUNTABILITY OFFICE CONTRACT APPEALS BOARD § 22.1 Applicability of Rules [Rule 1]. The Government... all appeals filed with the Board on or after October 1, 2007. ...

  18. Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

    PubMed Central

    Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

    2017-01-01

    An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning. PMID:28680387

  19. Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

    PubMed

    Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

    2017-01-01

    An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

  20. Physiotherapy clinical educators' perceptions and experiences of clinical prediction rules.

    PubMed

    Knox, Grahame M; Snodgrass, Suzanne J; Rivett, Darren A

    2015-12-01

    Clinical prediction rules (CPRs) are widely used in medicine, but their application to physiotherapy practice is more recent and less widespread, and their implementation in physiotherapy clinical education has not been investigated. This study aimed to determine the experiences and perceptions of physiotherapy clinical educators regarding CPRs, and whether they are teaching CPRs to students on clinical placement. Cross-sectional observational survey using a modified Dillman method. Clinical educators (n=211, response rate 81%) supervising physiotherapy students from 10 universities across 5 states and territories in Australia. Half (48%) of respondents had never heard of CPRs, and a further 25% had never used CPRs. Only 27% reported using CPRs, and of these half (51%) were rarely if ever teaching CPRs to students in the clinical setting. However most respondents (81%) believed CPRs assisted in the development of clinical reasoning skills and few (9%) were opposed to teaching CPRs to students. Users of CPRs were more likely to be male (p<0.001), have post-professional qualifications (p=0.020), work in private practice (p<0.001), and work in the area of musculoskeletal physiotherapy (p<0.001) compared with non-users. The CPRs most commonly known, used and taught were the Ottawa Ankle Rule, the Ottawa Knee Rule, and Wells' Rule for Deep Vein Thrombosis. Students are unlikely to be learning about CPRs on clinical placement, as few clinical educators use them. Clinical educators will require training in CPRs and assistance in teaching them if students are to better learn about implementing CPRs in physiotherapy clinical practice. Copyright © 2015 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.