Listening through Voices: Infant Statistical Word Segmentation across Multiple Speakers

ERIC Educational Resources Information Center

Graf Estes, Katharine; Lew-Williams, Casey

2015-01-01

To learn from their environments, infants must detect structure behind pervasive variation. This presents substantial and largely untested learning challenges in early language acquisition. The current experiments address whether infants can use statistical learning mechanisms to segment words when the speech signal contains acoustic variation…

Observational Word Learning: Beyond Propose-But-Verify and Associative Bean Counting.

PubMed

Roembke, Tanja; McMurray, Bob

2016-04-01

Learning new words is difficult. In any naming situation, there are multiple possible interpretations of a novel word. Recent approaches suggest that learners may solve this problem by tracking co-occurrence statistics between words and referents across multiple naming situations (e.g. Yu & Smith, 2007), overcoming the ambiguity in any one situation. Yet, there remains debate around the underlying mechanisms. We conducted two experiments in which learners acquired eight word-object mappings using cross-situational statistics while eye-movements were tracked. These addressed four unresolved questions regarding the learning mechanism. First, eye-movements during learning showed evidence that listeners maintain multiple hypotheses for a given word and bring them all to bear in the moment of naming. Second, trial-by-trial analyses of accuracy suggested that listeners accumulate continuous statistics about word/object mappings, over and above prior hypotheses they have about a word. Third, consistent, probabilistic context can impede learning, as false associations between words and highly co-occurring referents are formed. Finally, a number of factors not previously considered in prior analysis impact observational word learning: knowledge of the foils, spatial consistency of the target object, and the number of trials between presentations of the same word. This evidence suggests that observational word learning may derive from a combination of gradual statistical or associative learning mechanisms and more rapid real-time processes such as competition, mutual exclusivity and even inference or hypothesis testing.

Infants Segment Continuous Events Using Transitional Probabilities

ERIC Educational Resources Information Center

Stahl, Aimee E.; Romberg, Alexa R.; Roseberry, Sarah; Golinkoff, Roberta Michnick; Hirsh-Pasek, Kathryn

2014-01-01

Throughout their 1st year, infants adeptly detect statistical structure in their environment. However, little is known about whether statistical learning is a primary mechanism for event segmentation. This study directly tests whether statistical learning alone is sufficient to segment continuous events. Twenty-eight 7- to 9-month-old infants…

Statistical Mechanics of Node-perturbation Learning with Noisy Baseline

NASA Astrophysics Data System (ADS)

Hara, Kazuyuki; Katahira, Kentaro; Okada, Masato

2017-02-01

Node-perturbation learning is a type of statistical gradient descent algorithm that can be applied to problems where the objective function is not explicitly formulated, including reinforcement learning. It estimates the gradient of an objective function by using the change in the object function in response to the perturbation. The value of the objective function for an unperturbed output is called a baseline. Cho et al. proposed node-perturbation learning with a noisy baseline. In this paper, we report on building the statistical mechanics of Cho's model and on deriving coupled differential equations of order parameters that depict learning dynamics. We also show how to derive the generalization error by solving the differential equations of order parameters. On the basis of the results, we show that Cho's results are also apply in general cases and show some general performances of Cho's model.

Statistical learning and language acquisition

PubMed Central

Romberg, Alexa R.; Saffran, Jenny R.

2011-01-01

Human learners, including infants, are highly sensitive to structure in their environment. Statistical learning refers to the process of extracting this structure. A major question in language acquisition in the past few decades has been the extent to which infants use statistical learning mechanisms to acquire their native language. There have been many demonstrations showing infants’ ability to extract structures in linguistic input, such as the transitional probability between adjacent elements. This paper reviews current research on how statistical learning contributes to language acquisition. Current research is extending the initial findings of infants’ sensitivity to basic statistical information in many different directions, including investigating how infants represent regularities, learn about different levels of language, and integrate information across situations. These current directions emphasize studying statistical language learning in context: within language, within the infant learner, and within the environment as a whole. PMID:21666883

Statistical learning: a powerful mechanism that operates by mere exposure.

PubMed

Aslin, Richard N

2017-01-01

How do infants learn so rapidly and with little apparent effort? In 1996, Saffran, Aslin, and Newport reported that 8-month-old human infants could learn the underlying temporal structure of a stream of speech syllables after only 2 min of passive listening. This demonstration of what was called statistical learning, involving no instruction, reinforcement, or feedback, led to dozens of confirmations of this powerful mechanism of implicit learning in a variety of modalities, domains, and species. These findings reveal that infants are not nearly as dependent on explicit forms of instruction as we might have assumed from studies of learning in which children or adults are taught facts such as math or problem solving skills. Instead, at least in some domains, infants soak up the information around them by mere exposure. Learning and development in these domains thus appear to occur automatically and with little active involvement by an instructor (parent or teacher). The details of this statistical learning mechanism are discussed, including how exposure to specific types of information can, under some circumstances, generalize to never-before-observed information, thereby enabling transfer of learning. WIREs Cogn Sci 2017, 8:e1373. doi: 10.1002/wcs.1373 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

Statistical and Cooperative Learning in Reading: An Artificial Orthography Learning Study

ERIC Educational Resources Information Center

Zhao, Jingjing; Li, Tong; Elliott, Mark A.; Rueckl, Jay G.

2018-01-01

This article reports two experiments in which the artificial orthography paradigm was used to investigate the mechanisms underlying learning to read. In each experiment, participants were taught the meanings and pronunications of words written in an unfamiliar orthography, and the statistical structure of the mapping between written and spoken…

Statistical Learning in a Natural Language by 8-Month-Old Infants

PubMed Central

Pelucchi, Bruna; Hay, Jessica F.; Saffran, Jenny R.

2013-01-01

Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants’ ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition. PMID:19489896

Statistical learning in a natural language by 8-month-old infants.

PubMed

Pelucchi, Bruna; Hay, Jessica F; Saffran, Jenny R

2009-01-01

Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants' ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition.

Online incidental statistical learning of audiovisual word sequences in adults: a registered report.

PubMed

Kuppuraj, Sengottuvel; Duta, Mihaela; Thompson, Paul; Bishop, Dorothy

2018-02-01

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory-picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test-retest reliability ( r  = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process.

Online incidental statistical learning of audiovisual word sequences in adults: a registered report

PubMed Central

Duta, Mihaela; Thompson, Paul

2018-01-01

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory–picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test–retest reliability (r = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process. PMID:29515876

Cross-situational statistical word learning in young children.

PubMed

Suanda, Sumarga H; Mugwanya, Nassali; Namy, Laura L

2014-10-01

Recent empirical work has highlighted the potential role of cross-situational statistical word learning in children's early vocabulary development. In the current study, we tested 5- to 7-year-old children's cross-situational learning by presenting children with a series of ambiguous naming events containing multiple words and multiple referents. Children rapidly learned word-to-object mappings by attending to the co-occurrence regularities across these ambiguous naming events. The current study begins to address the mechanisms underlying children's learning by demonstrating that the diversity of learning contexts affects performance. The implications of the current findings for the role of cross-situational word learning at different points in development are discussed along with the methodological implications of employing school-aged children to test hypotheses regarding the mechanisms supporting early word learning. Copyright © 2014 Elsevier Inc. All rights reserved.

The Statistical Interpretation of Classical Thermodynamic Heating and Expansion Processes

ERIC Educational Resources Information Center

Cartier, Stephen F.

2011-01-01

A statistical model has been developed and applied to interpret thermodynamic processes typically presented from the macroscopic, classical perspective. Through this model, students learn and apply the concepts of statistical mechanics, quantum mechanics, and classical thermodynamics in the analysis of the (i) constant volume heating, (ii)…

Mere exposure alters category learning of novel objects.

PubMed

Folstein, Jonathan R; Gauthier, Isabel; Palmeri, Thomas J

2010-01-01

We investigated how mere exposure to complex objects with correlated or uncorrelated object features affects later category learning of new objects not seen during exposure. Correlations among pre-exposed object dimensions influenced later category learning. Unlike other published studies, the collection of pre-exposed objects provided no information regarding the categories to be learned, ruling out unsupervised or incidental category learning during pre-exposure. Instead, results are interpreted with respect to statistical learning mechanisms, providing one of the first demonstrations of how statistical learning can influence visual object learning.

Mere Exposure Alters Category Learning of Novel Objects

PubMed Central

Folstein, Jonathan R.; Gauthier, Isabel; Palmeri, Thomas J.

2010-01-01

We investigated how mere exposure to complex objects with correlated or uncorrelated object features affects later category learning of new objects not seen during exposure. Correlations among pre-exposed object dimensions influenced later category learning. Unlike other published studies, the collection of pre-exposed objects provided no information regarding the categories to be learned, ruling out unsupervised or incidental category learning during pre-exposure. Instead, results are interpreted with respect to statistical learning mechanisms, providing one of the first demonstrations of how statistical learning can influence visual object learning. PMID:21833209

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation.

PubMed

Pearce, Marcus T

2018-05-11

Music perception depends on internal psychological models derived through exposure to a musical culture. It is hypothesized that this musical enculturation depends on two cognitive processes: (1) statistical learning, in which listeners acquire internal cognitive models of statistical regularities present in the music to which they are exposed; and (2) probabilistic prediction based on these learned models that enables listeners to organize and process their mental representations of music. To corroborate these hypotheses, I review research that uses a computational model of probabilistic prediction based on statistical learning (the information dynamics of music (IDyOM) model) to simulate data from empirical studies of human listeners. The results show that a broad range of psychological processes involved in music perception-expectation, emotion, memory, similarity, segmentation, and meter-can be understood in terms of a single, underlying process of probabilistic prediction using learned statistical models. Furthermore, IDyOM simulations of listeners from different musical cultures demonstrate that statistical learning can plausibly predict causal effects of differential cultural exposure to musical styles, providing a quantitative model of cultural distance. Understanding the neural basis of musical enculturation will benefit from close coordination between empirical neuroimaging and computational modeling of underlying mechanisms, as outlined here. © 2018 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

Student Understanding of Taylor Series Expansions in Statistical Mechanics

ERIC Educational Resources Information Center

Smith, Trevor I.; Thompson, John R.; Mountcastle, Donald B.

2013-01-01

One goal of physics instruction is to have students learn to make physical meaning of specific mathematical expressions, concepts, and procedures in different physical settings. As part of research investigating student learning in statistical physics, we are developing curriculum materials that guide students through a derivation of the Boltzmann…

Statistical word learning in children with autism spectrum disorder and specific language impairment.

PubMed

Haebig, Eileen; Saffran, Jenny R; Ellis Weismer, Susan

2017-11-01

Word learning is an important component of language development that influences child outcomes across multiple domains. Despite the importance of word knowledge, word-learning mechanisms are poorly understood in children with specific language impairment (SLI) and children with autism spectrum disorder (ASD). This study examined underlying mechanisms of word learning, specifically, statistical learning and fast-mapping, in school-aged children with typical and atypical development. Statistical learning was assessed through a word segmentation task and fast-mapping was examined in an object-label association task. We also examined children's ability to map meaning onto newly segmented words in a third task that combined exposure to an artificial language and a fast-mapping task. Children with SLI had poorer performance on the word segmentation and fast-mapping tasks relative to the typically developing and ASD groups, who did not differ from one another. However, when children with SLI were exposed to an artificial language with phonemes used in the subsequent fast-mapping task, they successfully learned more words than in the isolated fast-mapping task. There was some evidence that word segmentation abilities are associated with word learning in school-aged children with typical development and ASD, but not SLI. Follow-up analyses also examined performance in children with ASD who did and did not have a language impairment. Children with ASD with language impairment evidenced intact statistical learning abilities, but subtle weaknesses in fast-mapping abilities. As the Procedural Deficit Hypothesis (PDH) predicts, children with SLI have impairments in statistical learning. However, children with SLI also have impairments in fast-mapping. Nonetheless, they are able to take advantage of additional phonological exposure to boost subsequent word-learning performance. In contrast to the PDH, children with ASD appear to have intact statistical learning, regardless of language status; however, fast-mapping abilities differ according to broader language skills. © 2017 Association for Child and Adolescent Mental Health.

Statistical Mechanics of the Delayed Reward-Based Learning with Node Perturbation

NASA Astrophysics Data System (ADS)

Hiroshi Saito,; Kentaro Katahira,; Kazuo Okanoya,; Masato Okada,

2010-06-01

In reward-based learning, reward is typically given with some delay after a behavior that causes the reward. In machine learning literature, the framework of the eligibility trace has been used as one of the solutions to handle the delayed reward in reinforcement learning. In recent studies, the eligibility trace is implied to be important for difficult neuroscience problem known as the “distal reward problem”. Node perturbation is one of the stochastic gradient methods from among many kinds of reinforcement learning implementations, and it searches the approximate gradient by introducing perturbation to a network. Since the stochastic gradient method does not require a objective function differential, it is expected to be able to account for the learning mechanism of a complex system, like a brain. We study the node perturbation with the eligibility trace as a specific example of delayed reward-based learning, and analyzed it using a statistical mechanics approach. As a result, we show the optimal time constant of the eligibility trace respect to the reward delay and the existence of unlearnable parameter configurations.

From inverse problems to learning: a Statistical Mechanics approach

NASA Astrophysics Data System (ADS)

Baldassi, Carlo; Gerace, Federica; Saglietti, Luca; Zecchina, Riccardo

2018-01-01

We present a brief introduction to the statistical mechanics approaches for the study of inverse problems in data science. We then provide concrete new results on inferring couplings from sampled configurations in systems characterized by an extensive number of stable attractors in the low temperature regime. We also show how these result are connected to the problem of learning with realistic weak signals in computational neuroscience. Our techniques and algorithms rely on advanced mean-field methods developed in the context of disordered systems.

Quantum Mechanics From the Cradle?

ERIC Educational Resources Information Center

Martin, John L.

1974-01-01

States that the major problem in learning quantum mechanics is often the student's ignorance of classical mechanics and that one conceptual hurdle in quantum mechanics is its statistical nature, in contrast to the determinism of classical mechanics. (MLH)

Modeling Cross-Situational Word–Referent Learning: Prior Questions

PubMed Central

Yu, Chen; Smith, Linda B.

2013-01-01

Both adults and young children possess powerful statistical computation capabilities—they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning. This article describes a series of simulation studies and analyses designed to understand the different learning mechanisms posited by the 2 classes of models and their relation to each other. Variants of a hypothesis-testing model and a simple or dumb associative mechanism were examined under different specifications of information selection, computation, and decision. Critically, these 3 components of the models interact in complex ways. The models illustrate a fundamental tradeoff between amount of data input and powerful computations: With the selection of more information, dumb associative models can mimic the powerful learning that is accomplished by hypothesis-testing models with fewer data. However, because of the interactions among the component parts of the models, the associative model can mimic various hypothesis-testing models, producing the same learning patterns but through different internal components. The simulations argue for the importance of a compositional approach to human statistical learning: the experimental decomposition of the processes that contribute to statistical learning in human learners and models with the internal components that can be evaluated independently and together. PMID:22229490

Investigating implicit statistical learning mechanisms through contextual cueing.

PubMed

Goujon, Annabelle; Didierjean, André; Thorpe, Simon

2015-09-01

Since its inception, the contextual cueing (CC) paradigm has generated considerable interest in various fields of cognitive sciences because it constitutes an elegant approach to understanding how statistical learning (SL) mechanisms can detect contextual regularities during a visual search. In this article we review and discuss five aspects of CC: (i) the implicit nature of learning, (ii) the mechanisms involved in CC, (iii) the mediating factors affecting CC, (iv) the generalization of CC phenomena, and (v) the dissociation between implicit and explicit CC phenomena. The findings suggest that implicit SL is an inherent component of ongoing processing which operates through clustering, associative, and reinforcement processes at various levels of sensory-motor processing, and might result from simple spike-timing-dependent plasticity. Copyright © 2015 Elsevier Ltd. All rights reserved.

Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.

PubMed

Gopnik, Alison; Wellman, Henry M

2012-11-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.

Reward-Guided Learning with and without Causal Attribution

PubMed Central

Jocham, Gerhard; Brodersen, Kay H.; Constantinescu, Alexandra O.; Kahn, Martin C.; Ianni, Angela M.; Walton, Mark E.; Rushworth, Matthew F.S.; Behrens, Timothy E.J.

2016-01-01

Summary When an organism receives a reward, it is crucial to know which of many candidate actions caused this reward. However, recent work suggests that learning is possible even when this most fundamental assumption is not met. We used novel reward-guided learning paradigms in two fMRI studies to show that humans deploy separable learning mechanisms that operate in parallel. While behavior was dominated by precise contingent learning, it also revealed hallmarks of noncontingent learning strategies. These learning mechanisms were separable behaviorally and neurally. Lateral orbitofrontal cortex supported contingent learning and reflected contingencies between outcomes and their causal choices. Amygdala responses around reward times related to statistical patterns of learning. Time-based heuristic mechanisms were related to activity in sensorimotor corticostriatal circuitry. Our data point to the existence of several learning mechanisms in the human brain, of which only one relies on applying known rules about the causal structure of the task. PMID:26971947

TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning.

PubMed

Mareschal, Denis; French, Robert M

2017-01-05

Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning

PubMed Central

French, Robert M.

2017-01-01

Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872375

Co-occurrence statistics as a language-dependent cue for speech segmentation.

PubMed

Saksida, Amanda; Langus, Alan; Nespor, Marina

2017-05-01

To what extent can language acquisition be explained in terms of different associative learning mechanisms? It has been hypothesized that distributional regularities in spoken languages are strong enough to elicit statistical learning about dependencies among speech units. Distributional regularities could be a useful cue for word learning even without rich language-specific knowledge. However, it is not clear how strong and reliable the distributional cues are that humans might use to segment speech. We investigate cross-linguistic viability of different statistical learning strategies by analyzing child-directed speech corpora from nine languages and by modeling possible statistics-based speech segmentations. We show that languages vary as to which statistical segmentation strategies are most successful. The variability of the results can be partially explained by systematic differences between languages, such as rhythmical differences. The results confirm previous findings that different statistical learning strategies are successful in different languages and suggest that infants may have to primarily rely on non-statistical cues when they begin their process of speech segmentation. © 2016 John Wiley & Sons Ltd.

An Experimental Approach to Teaching and Learning Elementary Statistical Mechanics

ERIC Educational Resources Information Center

Ellis, Frank B.; Ellis, David C.

2008-01-01

Introductory statistical mechanics is studied for a simple two-state system using an inexpensive and easily built apparatus. A large variety of demonstrations, suitable for students in high school and introductory university chemistry courses, are possible. This article details demonstrations for exothermic and endothermic reactions, the dynamic…

Brain Responses Reveal That Infants' Face Discrimination Is Guided by Statistical Learning from Distributional Information

ERIC Educational Resources Information Center

Altvater-Mackensen, Nicole; Jessen, Sarah; Grossmann, Tobias

2017-01-01

Infants' perception of faces becomes attuned to the environment during the first year of life. However, the mechanisms that underpin perceptual narrowing for faces are only poorly understood. Considering the developmental similarities seen in perceptual narrowing for faces and speech and the role that statistical learning has been shown to play…

Selective social learning in infancy: looking for mechanisms.

PubMed

Crivello, Cristina; Phillips, Sara; Poulin-Dubois, Diane

2018-05-01

Although there is mounting evidence that selective social learning begins in infancy, the psychological mechanisms underlying this ability are currently a controversial issue. The purpose of this study is to investigate whether theory of mind abilities and statistical learning skills are related to infants' selective social learning. Seventy-seven 18-month-olds were first exposed to a reliable or an unreliable speaker and then completed a word learning task, two theory of mind tasks, and a statistical learning task. If domain-general abilities are linked to selective social learning, then infants who demonstrate superior performance on the statistical learning task should perform better on the selective learning task, that is, should be less likely to learn words from an unreliable speaker. Alternatively, if domain-specific abilities are involved, then superior performance on theory of mind tasks should be related to selective learning performance. Findings revealed that, as expected, infants were more likely to learn a novel word from a reliable speaker. Importantly, infants who passed a theory of mind task assessing knowledge attribution were significantly less likely to learn a novel word from an unreliable speaker compared to infants who failed this task. No such effect was observed for the other tasks. These results suggest that infants who possess superior social-cognitive abilities are more apt to reject an unreliable speaker as informant. A video abstract of this article can be viewed at: https://youtu.be/zuuCniHYzqo. © 2017 John Wiley & Sons Ltd.

Statistical learning of novel graphotactic constraints in children and adults.

PubMed

Samara, Anna; Caravolas, Markéta

2014-05-01

The current study explored statistical learning processes in the acquisition of orthographic knowledge in school-aged children and skilled adults. Learning of novel graphotactic constraints on the position and context of letter distributions was induced by means of a two-phase learning task adapted from Onishi, Chambers, and Fisher (Cognition, 83 (2002) B13-B23). Following incidental exposure to pattern-embedding stimuli in Phase 1, participants' learning generalization was tested in Phase 2 with legality judgments about novel conforming/nonconforming word-like strings. Test phase performance was above chance, suggesting that both types of constraints were reliably learned even after relatively brief exposure. As hypothesized, signal detection theory d' analyses confirmed that learning permissible letter positions (d'=0.97) was easier than permissible neighboring letter contexts (d'=0.19). Adults were more accurate than children in all but a strict analysis of the contextual constraints condition. Consistent with the statistical learning perspective in literacy, our results suggest that statistical learning mechanisms contribute to children's and adults' acquisition of knowledge about graphotactic constraints similar to those existing in their orthography. Copyright © 2013 Elsevier Inc. All rights reserved.

Establishing a learning foundation in a dynamically changing world: Insights from artificial language work

NASA Astrophysics Data System (ADS)

Gonzales, Kalim

It is argued that infants build a foundation for learning about the world through their incidental acquisition of the spatial and temporal regularities surrounding them. A challenge is that learning occurs across multiple contexts whose statistics can greatly differ. Two artificial language studies with 12-month-olds demonstrate that infants come prepared to parse statistics across contexts using the temporal and perceptual features that distinguish one context from another. These results suggest that infants can organize their statistical input with a wider range of features that typically considered. Possible attention, decision making, and memory mechanisms are discussed.

Statistical learning of action: the role of conditional probability.

PubMed

Meyer, Meredith; Baldwin, Dare

2011-12-01

Identification of distinct units within a continuous flow of human action is fundamental to action processing. Such segmentation may rest in part on statistical learning. In a series of four experiments, we examined what types of statistics people can use to segment a continuous stream involving many brief, goal-directed action elements. The results of Experiment 1 showed no evidence for sensitivity to conditional probability, whereas Experiment 2 displayed learning based on joint probability. In Experiment 3, we demonstrated that additional exposure to the input failed to engender sensitivity to conditional probability. However, the results of Experiment 4 showed that a subset of adults-namely, those more successful at identifying actions that had been seen more frequently than comparison sequences-were also successful at learning conditional-probability statistics. These experiments help to clarify the mechanisms subserving processing of intentional action, and they highlight important differences from, as well as similarities to, prior studies of statistical learning in other domains, including language.

Modeling the Development of Audiovisual Cue Integration in Speech Perception

PubMed Central

Getz, Laura M.; Nordeen, Elke R.; Vrabic, Sarah C.; Toscano, Joseph C.

2017-01-01

Adult speech perception is generally enhanced when information is provided from multiple modalities. In contrast, infants do not appear to benefit from combining auditory and visual speech information early in development. This is true despite the fact that both modalities are important to speech comprehension even at early stages of language acquisition. How then do listeners learn how to process auditory and visual information as part of a unified signal? In the auditory domain, statistical learning processes provide an excellent mechanism for acquiring phonological categories. Is this also true for the more complex problem of acquiring audiovisual correspondences, which require the learner to integrate information from multiple modalities? In this paper, we present simulations using Gaussian mixture models (GMMs) that learn cue weights and combine cues on the basis of their distributional statistics. First, we simulate the developmental process of acquiring phonological categories from auditory and visual cues, asking whether simple statistical learning approaches are sufficient for learning multi-modal representations. Second, we use this time course information to explain audiovisual speech perception in adult perceivers, including cases where auditory and visual input are mismatched. Overall, we find that domain-general statistical learning techniques allow us to model the developmental trajectory of audiovisual cue integration in speech, and in turn, allow us to better understand the mechanisms that give rise to unified percepts based on multiple cues. PMID:28335558

Modeling the Development of Audiovisual Cue Integration in Speech Perception.

PubMed

Getz, Laura M; Nordeen, Elke R; Vrabic, Sarah C; Toscano, Joseph C

2017-03-21

Adult speech perception is generally enhanced when information is provided from multiple modalities. In contrast, infants do not appear to benefit from combining auditory and visual speech information early in development. This is true despite the fact that both modalities are important to speech comprehension even at early stages of language acquisition. How then do listeners learn how to process auditory and visual information as part of a unified signal? In the auditory domain, statistical learning processes provide an excellent mechanism for acquiring phonological categories. Is this also true for the more complex problem of acquiring audiovisual correspondences, which require the learner to integrate information from multiple modalities? In this paper, we present simulations using Gaussian mixture models (GMMs) that learn cue weights and combine cues on the basis of their distributional statistics. First, we simulate the developmental process of acquiring phonological categories from auditory and visual cues, asking whether simple statistical learning approaches are sufficient for learning multi-modal representations. Second, we use this time course information to explain audiovisual speech perception in adult perceivers, including cases where auditory and visual input are mismatched. Overall, we find that domain-general statistical learning techniques allow us to model the developmental trajectory of audiovisual cue integration in speech, and in turn, allow us to better understand the mechanisms that give rise to unified percepts based on multiple cues.

Multi-fidelity machine learning models for accurate bandgap predictions of solids

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

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

Multi-fidelity machine learning models for accurate bandgap predictions of solids

DOE PAGES

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

2016-12-28

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

Turking Statistics: Student-Generated Surveys Increase Student Engagement and Performance

ERIC Educational Resources Information Center

Whitley, Cameron T.; Dietz, Thomas

2018-01-01

Thirty years ago, Hubert M. Blalock Jr. published an article in "Teaching Sociology" about the importance of teaching statistics. We honor Blalock's legacy by assessing how using Amazon Mechanical Turk (MTurk) in statistics classes can enhance student learning and increase statistical literacy among social science gradaute students. In…

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

PubMed Central

2017-01-01

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274, 1926–1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105, 2745–2750; Thiessen & Yee 2010 Child Development 81, 1287–1303; Saffran 2002 Journal of Memory and Language 47, 172–196; Misyak & Christiansen 2012 Language Learning 62, 302–331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39, 246–263; Thiessen et al. 2013 Psychological Bulletin 139, 792–814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37, 310–343). This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences'. PMID:27872374

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes.

PubMed

Thiessen, Erik D

2017-01-05

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37: , 310-343).This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

When Mommy Comes to the Rescue of Statistics: Infants Combine Top-Down and Bottom-Up Cues to Segment Speech

ERIC Educational Resources Information Center

Mersad, Karima; Nazzi, Thierry

2012-01-01

Transitional Probability (TP) computations are regarded as a powerful learning mechanism that is functional early in development and has been proposed as an initial bootstrapping device for speech segmentation. However, a recent study casts doubt on the robustness of early statistical word-learning. Johnson and Tyler (2010) showed that when…

eLearning course may shorten the duration of mechanical restraint among psychiatric inpatients: a cluster-randomized trial.

PubMed

Kontio, Raija; Pitkänen, Anneli; Joffe, Grigori; Katajisto, Jouko; Välimäki, Maritta

2014-10-01

The management of psychiatric inpatients exhibiting severely disturbed and aggressive behaviour is an important educational topic. Well structured, IT-based educational programmes (eLearning) often ensure quality and may make training more affordable and accessible. The aim of this study was to explore the impact of an eLearning course for personnel on the rates and duration of seclusion and mechanical restraint among psychiatric inpatients. In a cluster-randomized intervention trial, the nursing personnel on 10 wards were randomly assigned to eLearning (intervention) or training-as-usual (control) groups. The eLearning course comprised six modules with specific topics (legal and ethical issues, behaviour-related factors, therapeutic relationship and self-awareness, teamwork and integrating knowledge with practice) and specific learning methods. The rates (incidents per 1000 occupied bed days) and durations of the coercion incidents were examined before and after the course. A total of 1283 coercion incidents (1143 seclusions [89%] and 140 incidents involving the use of mechanical restraints [11%]) were recorded on the study wards during the data collection period. On the intervention wards, there were no statistically significant changes in the rates of seclusion and mechanical restraint. However, the duration of incidents involving mechanical restraints shortened from 36.0 to 4.0 h (median) (P < 0.001). No statistically significant changes occurred on the control wards. After our eLearning course, the duration of incidents involving the use of mechanical restraints decreased. However, more studies are needed to ensure that the content of the course focuses on the most important factors associated with the seclusion-related elements. The eLearning course deserves further development and further studies. The duration of coercion incidents merits attention in future research.

Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

PubMed Central

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists. PMID:22582739

Evolving Learning Paradigms: Re-Setting Baselines and Collection Methods of Information and Communication Technology in Education Statistics

ERIC Educational Resources Information Center

Gibson, David; Broadley, Tania; Downie, Jill; Wallet, Peter

2018-01-01

The UNESCO Institute for Statistics (UIS) has been measuring ICT in education since 2009, but with such rapid change in technology and its use in education, it is important now to revise the collection mechanisms to focus on how technology is being used to enhance learning and teaching. Sustainable development goal (SDG) 4, for example, moves…

Moral foundations in an interacting neural networks society: A statistical mechanics analysis

NASA Astrophysics Data System (ADS)

Vicente, R.; Susemihl, A.; Jericó, J. P.; Caticha, N.

2014-04-01

The moral foundations theory supports that people, across cultures, tend to consider a small number of dimensions when classifying issues on a moral basis. The data also show that the statistics of weights attributed to each moral dimension is related to self-declared political affiliation, which in turn has been connected to cognitive learning styles by the recent literature in neuroscience and psychology. Inspired by these data, we propose a simple statistical mechanics model with interacting neural networks classifying vectors and learning from members of their social neighbourhood about their average opinion on a large set of issues. The purpose of learning is to reduce dissension among agents when disagreeing. We consider a family of learning algorithms parametrized by δ, that represents the importance given to corroborating (same sign) opinions. We define an order parameter that quantifies the diversity of opinions in a group with homogeneous learning style. Using Monte Carlo simulations and a mean field approximation we find the relation between the order parameter and the learning parameter δ at a temperature we associate with the importance of social influence in a given group. In concordance with data, groups that rely more strongly on corroborating evidence sustain less opinion diversity. We discuss predictions of the model and propose possible experimental tests.

Statistical Mechanical Analysis of Online Learning with Weight Normalization in Single Layer Perceptron

NASA Astrophysics Data System (ADS)

Yoshida, Yuki; Karakida, Ryo; Okada, Masato; Amari, Shun-ichi

2017-04-01

Weight normalization, a newly proposed optimization method for neural networks by Salimans and Kingma (2016), decomposes the weight vector of a neural network into a radial length and a direction vector, and the decomposed parameters follow their steepest descent update. They reported that learning with the weight normalization achieves better converging speed in several tasks including image recognition and reinforcement learning than learning with the conventional parameterization. However, it remains theoretically uncovered how the weight normalization improves the converging speed. In this study, we applied a statistical mechanical technique to analyze on-line learning in single layer linear and nonlinear perceptrons with weight normalization. By deriving order parameters of the learning dynamics, we confirmed quantitatively that weight normalization realizes fast converging speed by automatically tuning the effective learning rate, regardless of the nonlinearity of the neural network. This property is realized when the initial value of the radial length is near the global minimum; therefore, our theory suggests that it is important to choose the initial value of the radial length appropriately when using weight normalization.

Statistical Word Learning in Children with Autism Spectrum Disorder and Specific Language Impairment

ERIC Educational Resources Information Center

Haebig, Eileen; Saffran, Jenny R.; Ellis Weismer, Susan

2017-01-01

Background: Word learning is an important component of language development that influences child outcomes across multiple domains. Despite the importance of word knowledge, word-learning mechanisms are poorly understood in children with specific language impairment (SLI) and children with autism spectrum disorder (ASD). This study examined…

Learning the Association between a Context and a Target Location in Infancy

ERIC Educational Resources Information Center

Bertels, Julie; San Anton, Estibaliz; Gebuis, Titia; Destrebecqz, Arnaud

2017-01-01

Extracting the statistical regularities present in the environment is a central learning mechanism in infancy. For instance, infants are able to learn the associations between simultaneously or successively presented visual objects (Fiser & Aslin, 2002; Kirkham, Slemmer & Johnson, 2002). The present study extends these results by…

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

PubMed

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-06-17

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

PubMed Central

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-01-01

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

Words, rules, and mechanisms of language acquisition.

PubMed

Endress, Ansgar D; Bonatti, Luca L

2016-01-01

We review recent artificial language learning studies, especially those following Endress and Bonatti (Endress AD, Bonatti LL. Rapid learning of syllable classes from a perceptually continuous speech stream. Cognition 2007, 105:247-299), suggesting that humans can deploy a variety of learning mechanisms to acquire artificial languages. Several experiments provide evidence for multiple learning mechanisms that can be deployed in fluent speech: one mechanism encodes the positions of syllables within words and can be used to extract generalization, while the other registers co-occurrence statistics of syllables and can be used to break a continuum into its components. We review dissociations between these mechanisms and their potential role in language acquisition. We then turn to recent criticisms of the multiple mechanisms hypothesis and show that they are inconsistent with the available data. Our results suggest that artificial and natural language learning is best understood by dissecting the underlying specialized learning abilities, and that these data provide a rare opportunity to link important language phenomena to basic psychological mechanisms. For further resources related to this article, please visit the WIREs website. © 2015 Wiley Periodicals, Inc.

The influence of social information on children's statistical and causal inferences.

PubMed

Sobel, David M; Kirkham, Natasha Z

2012-01-01

Constructivist accounts of learning posit that causal inference is a child-driven process. Recent interpretations of such accounts also suggest that the process children use for causal learning is rational: Children interpret and learn from new evidence in light of their existing beliefs. We argue that such mechanisms are also driven by informative social cues and suggest ways in which such information influences both preschoolers' and infants' inferences. In doing so, we argue that a rational constructivist account should not only focus on describing the child's internal cognitive mechanisms for learning but also on how social information affects the process of learning.

Statistical Analysis Tools for Learning in Engineering Laboratories.

ERIC Educational Resources Information Center

Maher, Carolyn A.

1990-01-01

Described are engineering programs that have used automated data acquisition systems to implement data collection and analyze experiments. Applications include a biochemical engineering laboratory, heat transfer performance, engineering materials testing, mechanical system reliability, statistical control laboratory, thermo-fluid laboratory, and a…

Learning-dependent plasticity with and without training in the human brain.

PubMed

Zhang, Jiaxiang; Kourtzi, Zoe

2010-07-27

Long-term experience through development and evolution and shorter-term training in adulthood have both been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of objects in cluttered scenes remain largely unknown. Here, we combine behavioral and functional MRI (fMRI) measurements to investigate the human-brain mechanisms that mediate our ability to learn statistical regularities and detect targets in clutter. We show two different routes to visual learning in clutter with discrete brain plasticity signatures. Specifically, opportunistic learning of regularities typical in natural contours (i.e., collinearity) can occur simply through frequent exposure, generalize across untrained stimulus features, and shape processing in occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training (bootstrap-based learning), is stimulus-dependent, and enhances processing in intraparietal regions implicated in attention-gated learning. We propose that long-term experience with statistical regularities may facilitate opportunistic learning of collinear contours, whereas learning to integrate discontinuities entails bootstrap-based training for the detection of contours in clutter. These findings provide insights in understanding how long-term experience and short-term training interact to shape the optimization of visual recognition processes.

The Impact of Phonological Neighborhood Density on Typical and Atypical Emerging Lexicons

ERIC Educational Resources Information Center

Stokes, Stephanie F.

2014-01-01

According to the Extended Statistical Learning account (ExSL; Stokes, Kern & dos Santos, 2012) late talkers (LTs) continue to use neighborhood density (ND) as a cue for word learning when their peers no longer use a density learning mechanism. In the current article, LTs expressive ("active") lexicon ND values differed from those of…

Songs as an aid for language acquisition.

PubMed

Schön, Daniele; Boyer, Maud; Moreno, Sylvain; Besson, Mireille; Peretz, Isabelle; Kolinsky, Régine

2008-02-01

In previous research, Saffran and colleagues [Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274, 1926-1928; Saffran, J. R., Newport, E. L., & Aslin, R. N. (1996). Word segmentation: The role of distributional cues. Journal of Memory and Language, 35, 606-621.] have shown that adults and infants can use the statistical properties of syllable sequences to extract words from continuous speech. They also showed that a similar learning mechanism operates with musical stimuli [Saffran, J. R., Johnson, R. E. K., Aslin, N., & Newport, E. L. (1999). Abstract Statistical learning of tone sequences by human infants and adults. Cognition, 70, 27-52.]. In this work we combined linguistic and musical information and we compared language learning based on speech sequences to language learning based on sung sequences. We hypothesized that, compared to speech sequences, a consistent mapping of linguistic and musical information would enhance learning. Results confirmed the hypothesis showing a strong learning facilitation of song compared to speech. Most importantly, the present results show that learning a new language, especially in the first learning phase wherein one needs to segment new words, may largely benefit of the motivational and structuring properties of music in song.

Interactions between statistical and semantic information in infant language development

PubMed Central

Lany, Jill; Saffran, Jenny R.

2013-01-01

Infants can use statistical regularities to form rudimentary word categories (e.g. noun, verb), and to learn the meanings common to words from those categories. Using an artificial language methodology, we probed the mechanisms by which two types of statistical cues (distributional and phonological regularities) affect word learning. Because linking distributional cues vs. phonological information to semantics make different computational demands on learners, we also tested whether their use is related to language proficiency. We found that 22-month-old infants with smaller vocabularies generalized using phonological cues; however, infants with larger vocabularies showed the opposite pattern of results, generalizing based on distributional cues. These findings suggest that both phonological and distributional cues marking word categories promote early word learning. Moreover, while correlations between these cues are important to forming word categories, we found infants’ weighting of these cues in subsequent word-learning tasks changes over the course of early language development. PMID:21884336

Visual statistical learning is related to natural language ability in adults: An ERP study.

PubMed

Daltrozzo, Jerome; Emerson, Samantha N; Deocampo, Joanne; Singh, Sonia; Freggens, Marjorie; Branum-Martin, Lee; Conway, Christopher M

2017-03-01

Statistical learning (SL) is believed to enable language acquisition by allowing individuals to learn regularities within linguistic input. However, neural evidence supporting a direct relationship between SL and language ability is scarce. We investigated whether there are associations between event-related potential (ERP) correlates of SL and language abilities while controlling for the general level of selective attention. Seventeen adults completed tests of visual SL, receptive vocabulary, grammatical ability, and sentence completion. Response times and ERPs showed that SL is related to receptive vocabulary and grammatical ability. ERPs indicated that the relationship between SL and grammatical ability was independent of attention while the association between SL and receptive vocabulary depended on attention. The implications of these dissociative relationships in terms of underlying mechanisms of SL and language are discussed. These results further elucidate the cognitive nature of the links between SL mechanisms and language abilities. Copyright © 2017 Elsevier Inc. All rights reserved.

Visual statistical learning is related to natural language ability in adults: An ERP Study

PubMed Central

Daltrozzo, Jerome; Emerson, Samantha N.; Deocampo, Joanne; Singh, Sonia; Freggens, Marjorie; Branum-Martin, Lee; Conway, Christopher M.

2017-01-01

Statistical learning (SL) is believed to enable language acquisition by allowing individuals to learn regularities within linguistic input. However, neural evidence supporting a direct relationship between SL and language ability is scarce. We investigated whether there are associations between event-related potential (ERP) correlates of SL and language abilities while controlling for the general level of selective attention. Seventeen adults completed tests of visual SL, receptive vocabulary, grammatical ability, and sentence completion. Response times and ERPs showed that SL is related to receptive vocabulary and grammatical ability. ERPs indicated that the relationship between SL and grammatical ability was independent of attention while the association between SL and receptive vocabulary depended on attention. The implications of these dissociative relationships in terms of underlying mechanisms of SL and language are discussed. These results further elucidate the cognitive nature of the links between SL mechanisms and language abilities. PMID:28086142

Proceedings for the Annual Symposium and Exhibition on Situational Awareness in the Tactical Air Environment, (2nd), Held at Patuxent River, Maryland, on 3-4 June 1997

DTIC Science & Technology

1997-06-01

made based on a learning mechanism. Traditional statistical regression and neural network approaches offer some utility, but suffer from practical...Columbus, OH. Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive, skill- based , and affective theories of learning outcomes to new...and Feature Effects 151 Enhanced Spatial State Feedback for Night Vision Goggle Displays 159 Statistical Network Applications of Decision Aiding for

The penumbra of learning: a statistical theory of synaptic tagging and capture.

PubMed

Gershman, Samuel J

2014-01-01

Learning in humans and animals is accompanied by a penumbra: Learning one task benefits from learning an unrelated task shortly before or after. At the cellular level, the penumbra of learning appears when weak potentiation of one synapse is amplified by strong potentiation of another synapse on the same neuron during a critical time window. Weak potentiation sets a molecular tag that enables the synapse to capture plasticity-related proteins synthesized in response to strong potentiation at another synapse. This paper describes a computational model which formalizes synaptic tagging and capture in terms of statistical learning mechanisms. According to this model, synaptic strength encodes a probabilistic inference about the dynamically changing association between pre- and post-synaptic firing rates. The rate of change is itself inferred, coupling together different synapses on the same neuron. When the inputs to one synapse change rapidly, the inferred rate of change increases, amplifying learning at other synapses.

Domain general learning: Infants use social and non-social cues when learning object statistics

PubMed Central

Barry, Ryan A.; Graf Estes, Katharine; Rivera, Susan M.

2015-01-01

Previous research has shown that infants can learn from social cues. But is a social cue more effective at directing learning than a non-social cue? This study investigated whether 9-month-old infants (N = 55) could learn a visual statistical regularity in the presence of a distracting visual sequence when attention was directed by either a social cue (a person) or a non-social cue (a rectangle). The results show that both social and non-social cues can guide infants’ attention to a visual shape sequence (and away from a distracting sequence). The social cue more effectively directed attention than the non-social cue during the familiarization phase, but the social cue did not result in significantly stronger learning than the non-social cue. The findings suggest that domain general attention mechanisms allow for the comparable learning seen in both conditions. PMID:25999879

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

Evolution of social versus individual learning in a subdivided population revisited: comparative analysis of three coexistence mechanisms using the inclusive-fitness method.

PubMed

Kobayashi, Yutaka; Ohtsuki, Hisashi

2014-03-01

Learning abilities are categorized into social (learning from others) and individual learning (learning on one's own). Despite the typically higher cost of individual learning, there are mechanisms that allow stable coexistence of both learning modes in a single population. In this paper, we investigate by means of mathematical modeling how the effect of spatial structure on evolutionary outcomes of pure social and individual learning strategies depends on the mechanisms for coexistence. We model a spatially structured population based on the infinite-island framework and consider three scenarios that differ in coexistence mechanisms. Using the inclusive-fitness method, we derive the equilibrium frequency of social learners and the genetic load of social learning (defined as average fecundity reduction caused by the presence of social learning) in terms of some summary statistics, such as relatedness, for each of the three scenarios and compare the results. This comparative analysis not only reconciles previous models that made contradictory predictions as to the effect of spatial structure on the equilibrium frequency of social learners but also derives a simple mathematical rule that determines the sign of the genetic load (i.e. whether or not social learning contributes to the mean fecundity of the population). Copyright © 2013 Elsevier Inc. All rights reserved.

Neurophysiological Markers of Statistical Learning in Music and Language: Hierarchy, Entropy, and Uncertainty.

PubMed

Daikoku, Tatsuya

2018-06-19

Statistical learning (SL) is a method of learning based on the transitional probabilities embedded in sequential phenomena such as music and language. It has been considered an implicit and domain-general mechanism that is innate in the human brain and that functions independently of intention to learn and awareness of what has been learned. SL is an interdisciplinary notion that incorporates information technology, artificial intelligence, musicology, and linguistics, as well as psychology and neuroscience. A body of recent study has suggested that SL can be reflected in neurophysiological responses based on the framework of information theory. This paper reviews a range of work on SL in adults and children that suggests overlapping and independent neural correlations in music and language, and that indicates disability of SL. Furthermore, this article discusses the relationships between the order of transitional probabilities (TPs) (i.e., hierarchy of local statistics) and entropy (i.e., global statistics) regarding SL strategies in human's brains; claims importance of information-theoretical approaches to understand domain-general, higher-order, and global SL covering both real-world music and language; and proposes promising approaches for the application of therapy and pedagogy from various perspectives of psychology, neuroscience, computational studies, musicology, and linguistics.

Context-Aware Generative Adversarial Privacy

NASA Astrophysics Data System (ADS)

Huang, Chong; Kairouz, Peter; Chen, Xiao; Sankar, Lalitha; Rajagopal, Ram

2017-12-01

Preserving the utility of published datasets while simultaneously providing provable privacy guarantees is a well-known challenge. On the one hand, context-free privacy solutions, such as differential privacy, provide strong privacy guarantees, but often lead to a significant reduction in utility. On the other hand, context-aware privacy solutions, such as information theoretic privacy, achieve an improved privacy-utility tradeoff, but assume that the data holder has access to dataset statistics. We circumvent these limitations by introducing a novel context-aware privacy framework called generative adversarial privacy (GAP). GAP leverages recent advancements in generative adversarial networks (GANs) to allow the data holder to learn privatization schemes from the dataset itself. Under GAP, learning the privacy mechanism is formulated as a constrained minimax game between two players: a privatizer that sanitizes the dataset in a way that limits the risk of inference attacks on the individuals' private variables, and an adversary that tries to infer the private variables from the sanitized dataset. To evaluate GAP's performance, we investigate two simple (yet canonical) statistical dataset models: (a) the binary data model, and (b) the binary Gaussian mixture model. For both models, we derive game-theoretically optimal minimax privacy mechanisms, and show that the privacy mechanisms learned from data (in a generative adversarial fashion) match the theoretically optimal ones. This demonstrates that our framework can be easily applied in practice, even in the absence of dataset statistics.

Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering.

PubMed

Daikoku, Tatsuya; Yatomi, Yutaka; Yumoto, Masato

2017-01-27

Previous neural studies have supported the hypothesis that statistical learning mechanisms are used broadly across different domains such as language and music. However, these studies have only investigated a single aspect of statistical learning at a time, such as recognizing word boundaries or learning word order patterns. In this study, we neutrally investigated how the two levels of statistical learning for recognizing word boundaries and word ordering could be reflected in neuromagnetic responses and how acquired statistical knowledge is reorganised when the syntactic rules are revised. Neuromagnetic responses to the Japanese-vowel sequence (a, e, i, o, and u), presented every .45s, were recorded from 14 right-handed Japanese participants. The vowel order was constrained by a Markov stochastic model such that five nonsense words (aue, eao, iea, oiu, and uoi) were chained with an either-or rule: the probability of the forthcoming word was statistically defined (80% for one word; 20% for the other word) by the most recent two words. All of the word transition probabilities (80% and 20%) were switched in the middle of the sequence. In the first and second quarters of the sequence, the neuromagnetic responses to the words that appeared with higher transitional probability were significantly reduced compared with those that appeared with a lower transitional probability. After switching the word transition probabilities, the response reduction was replicated in the last quarter of the sequence. The responses to the final vowels in the words were significantly reduced compared with those to the initial vowels in the last quarter of the sequence. The results suggest that both within-word and between-word statistical learning are reflected in neural responses. The present study supports the hypothesis that listeners learn larger structures such as phrases first, and they subsequently extract smaller structures, such as words, from the learned phrases. The present study provides the first neurophysiological evidence that the correction of statistical knowledge requires more time than the acquisition of new statistical knowledge. Copyright © 2016 Elsevier Ltd. All rights reserved.

An argument for mechanism-based statistical inference in cancer

PubMed Central

Ochs, Michael; Price, Nathan D.; Tomasetti, Cristian; Younes, Laurent

2015-01-01

Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning bio-markers, metabolism, cell signaling, network inference and tumorigenesis. PMID:25381197

Bayesian learning of visual chunks by human observers

PubMed Central

Orbán, Gergő; Fiser, József; Aslin, Richard N.; Lengyel, Máté

2008-01-01

Efficient and versatile processing of any hierarchically structured information requires a learning mechanism that combines lower-level features into higher-level chunks. We investigated this chunking mechanism in humans with a visual pattern-learning paradigm. We developed an ideal learner based on Bayesian model comparison that extracts and stores only those chunks of information that are minimally sufficient to encode a set of visual scenes. Our ideal Bayesian chunk learner not only reproduced the results of a large set of previous empirical findings in the domain of human pattern learning but also made a key prediction that we confirmed experimentally. In accordance with Bayesian learning but contrary to associative learning, human performance was well above chance when pair-wise statistics in the exemplars contained no relevant information. Thus, humans extract chunks from complex visual patterns by generating accurate yet economical representations and not by encoding the full correlational structure of the input. PMID:18268353

Cognitive Mechanisms Underlying Action Prediction in Children and Adults with Autism Spectrum Condition

ERIC Educational Resources Information Center

Schuwerk, Tobias; Sodian, Beate; Paulus, Markus

2016-01-01

Recent research suggests that impaired action prediction is at the core of social interaction deficits in autism spectrum condition (ASC). Here, we targeted two cognitive mechanisms that are thought to underlie the prediction of others' actions: statistical learning and efficiency considerations. We measured proactive eye movements of 10-year-old…

Infants with Williams syndrome detect statistical regularities in continuous speech.

PubMed

Cashon, Cara H; Ha, Oh-Ryeong; Graf Estes, Katharine; Saffran, Jenny R; Mervis, Carolyn B

2016-09-01

Williams syndrome (WS) is a rare genetic disorder associated with delays in language and cognitive development. The reasons for the language delay are unknown. Statistical learning is a domain-general mechanism recruited for early language acquisition. In the present study, we investigated whether infants with WS were able to detect the statistical structure in continuous speech. Eighteen 8- to 20-month-olds with WS were familiarized with 2min of a continuous stream of synthesized nonsense words; the statistical structure of the speech was the only cue to word boundaries. They were tested on their ability to discriminate statistically-defined "words" and "part-words" (which crossed word boundaries) in the artificial language. Despite significant cognitive and language delays, infants with WS were able to detect the statistical regularities in the speech stream. These findings suggest that an inability to track the statistical properties of speech is unlikely to be the primary basis for the delays in the onset of language observed in infants with WS. These results provide the first evidence of statistical learning by infants with developmental delays. Copyright © 2016 Elsevier B.V. All rights reserved.

A Revelation: Quantum-Statistics and Classical-Statistics are Analytic-Geometry Conic-Sections and Numbers/Functions: Euler, Riemann, Bernoulli Generating-Functions: Conics to Numbers/Functions Deep Subtle Connections

NASA Astrophysics Data System (ADS)

Descartes, R.; Rota, G.-C.; Euler, L.; Bernoulli, J. D.; Siegel, Edward Carl-Ludwig

2011-03-01

Quantum-statistics Dichotomy: Fermi-Dirac(FDQS) Versus Bose-Einstein(BEQS), respectively with contact-repulsion/non-condensation(FDCR) versus attraction/ condensationBEC are manifestly-demonstrated by Taylor-expansion ONLY of their denominator exponential, identified BOTH as Descartes analytic-geometry conic-sections, FDQS as Elllipse (homotopy to rectangle FDQS distribution-function), VIA Maxwell-Boltzmann classical-statistics(MBCS) to Parabola MORPHISM, VS. BEQS to Hyperbola, Archimedes' HYPERBOLICITY INEVITABILITY, and as well generating-functions[Abramowitz-Stegun, Handbook Math.-Functions--p. 804!!!], respectively of Euler-numbers/functions, (via Riemann zeta-function(domination of quantum-statistics: [Pathria, Statistical-Mechanics; Huang, Statistical-Mechanics]) VS. Bernoulli-numbers/ functions. Much can be learned about statistical-physics from Euler-numbers/functions via Riemann zeta-function(s) VS. Bernoulli-numbers/functions [Conway-Guy, Book of Numbers] and about Euler-numbers/functions, via Riemann zeta-function(s) MORPHISM, VS. Bernoulli-numbers/ functions, visa versa!!! Ex.: Riemann-hypothesis PHYSICS proof PARTLY as BEQS BEC/BEA!!!

The evolution of continuous learning of the structure of the environment

PubMed Central

Kolodny, Oren; Edelman, Shimon; Lotem, Arnon

2014-01-01

Continuous, ‘always on’, learning of structure from a stream of data is studied mainly in the fields of machine learning or language acquisition, but its evolutionary roots may go back to the first organisms that were internally motivated to learn and represent their environment. Here, we study under what conditions such continuous learning (CL) may be more adaptive than simple reinforcement learning and examine how it could have evolved from the same basic associative elements. We use agent-based computer simulations to compare three learning strategies: simple reinforcement learning; reinforcement learning with chaining (RL-chain) and CL that applies the same associative mechanisms used by the other strategies, but also seeks statistical regularities in the relations among all items in the environment, regardless of the initial association with food. We show that a sufficiently structured environment favours the evolution of both RL-chain and CL and that CL outperforms the other strategies when food is relatively rare and the time for learning is limited. This advantage of internally motivated CL stems from its ability to capture statistical patterns in the environment even before they are associated with food, at which point they immediately become useful for planning. PMID:24402920

Infants are superior in implicit crossmodal learning and use other learning mechanisms than adults

PubMed Central

von Frieling, Marco; Röder, Brigitte

2017-01-01

During development internal models of the sensory world must be acquired which have to be continuously adapted later. We used event-related potentials (ERP) to test the hypothesis that infants extract crossmodal statistics implicitly while adults learn them when task relevant. Participants were passively exposed to frequent standard audio-visual combinations (A1V1, A2V2, p=0.35 each), rare recombinations of these standard stimuli (A1V2, A2V1, p=0.10 each), and a rare audio-visual deviant with infrequent auditory and visual elements (A3V3, p=0.10). While both six-month-old infants and adults differentiated between rare deviants and standards involving early neural processing stages only infants were sensitive to crossmodal statistics as indicated by a late ERP difference between standard and recombined stimuli. A second experiment revealed that adults differentiated recombined and standard combinations when crossmodal combinations were task relevant. These results demonstrate a heightened sensitivity for crossmodal statistics in infants and a change in learning mode from infancy to adulthood. PMID:28949291

Mutual interference between statistical summary perception and statistical learning.

PubMed

Zhao, Jiaying; Ngo, Nhi; McKendrick, Ryan; Turk-Browne, Nicholas B

2011-09-01

The visual system is an efficient statistician, extracting statistical summaries over sets of objects (statistical summary perception) and statistical regularities among individual objects (statistical learning). Although these two kinds of statistical processing have been studied extensively in isolation, their relationship is not yet understood. We first examined how statistical summary perception influences statistical learning by manipulating the task that participants performed over sets of objects containing statistical regularities (Experiment 1). Participants who performed a summary task showed no statistical learning of the regularities, whereas those who performed control tasks showed robust learning. We then examined how statistical learning influences statistical summary perception by manipulating whether the sets being summarized contained regularities (Experiment 2) and whether such regularities had already been learned (Experiment 3). The accuracy of summary judgments improved when regularities were removed and when learning had occurred in advance. In sum, calculating summary statistics impeded statistical learning, and extracting statistical regularities impeded statistical summary perception. This mutual interference suggests that statistical summary perception and statistical learning are fundamentally related.

Emergence of Joint Attention through Bootstrap Learning based on the Mechanisms of Visual Attention and Learning with Self-evaluation

NASA Astrophysics Data System (ADS)

Nagai, Yukie; Hosoda, Koh; Morita, Akio; Asada, Minoru

This study argues how human infants acquire the ability of joint attention through interactions with their caregivers from a viewpoint of cognitive developmental robotics. In this paper, a mechanism by which a robot acquires sensorimotor coordination for joint attention through bootstrap learning is described. Bootstrap learning is a process by which a learner acquires higher capabilities through interactions with its environment based on embedded lower capabilities even if the learner does not receive any external evaluation nor the environment is controlled. The proposed mechanism for bootstrap learning of joint attention consists of the robot's embedded mechanisms: visual attention and learning with self-evaluation. The former is to find and attend to a salient object in the field of the robot's view, and the latter is to evaluate the success of visual attention, not joint attention, and then to learn the sensorimotor coordination. Since the object which the robot looks at based on visual attention does not always correspond to the object which the caregiver is looking at in an environment including multiple objects, the robot may have incorrect learning situations for joint attention as well as correct ones. However, the robot is expected to statistically lose the learning data of the incorrect ones as outliers because of its weaker correlation between the sensor input and the motor output than that of the correct ones, and consequently to acquire appropriate sensorimotor coordination for joint attention even if the caregiver does not provide any task evaluation to the robot. The experimental results show the validity of the proposed mechanism. It is suggested that the proposed mechanism could explain the developmental mechanism of infants' joint attention because the learning process of the robot's joint attention can be regarded as equivalent to the developmental process of infants' one.

Sentence-Based Attentional Mechanisms in Word Learning: Evidence from a Computational Model

PubMed Central

Alishahi, Afra; Fazly, Afsaneh; Koehne, Judith; Crocker, Matthew W.

2012-01-01

When looking for the referents of novel nouns, adults and young children are sensitive to cross-situational statistics (Yu and Smith, 2007; Smith and Yu, 2008). In addition, the linguistic context that a word appears in has been shown to act as a powerful attention mechanism for guiding sentence processing and word learning (Landau and Gleitman, 1985; Altmann and Kamide, 1999; Kako and Trueswell, 2000). Koehne and Crocker (2010, 2011) investigate the interaction between cross-situational evidence and guidance from the sentential context in an adult language learning scenario. Their studies reveal that these learning mechanisms interact in a complex manner: they can be used in a complementary way when context helps reduce referential uncertainty; they influence word learning about equally strongly when cross-situational and contextual evidence are in conflict; and contextual cues block aspects of cross-situational learning when both mechanisms are independently applicable. To address this complex pattern of findings, we present a probabilistic computational model of word learning which extends a previous cross-situational model (Fazly et al., 2010) with an attention mechanism based on sentential cues. Our model uses a framework that seamlessly combines the two sources of evidence in order to study their emerging pattern of interaction during the process of word learning. Simulations of the experiments of (Koehne and Crocker, 2010, 2011) reveal an overall pattern of results that are in line with their findings. Importantly, we demonstrate that our model does not need to explicitly assign priority to either source of evidence in order to produce these results: learning patterns emerge as a result of a probabilistic interaction between the two clue types. Moreover, using a computational model allows us to examine the developmental trajectory of the differential roles of cross-situational and sentential cues in word learning. PMID:22783211

Language experience changes subsequent learning

PubMed Central

Onnis, Luca; Thiessen, Erik

2013-01-01

What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non-linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses, and are driven by probabilistic preferences. Furthermore, although the Korean individuals had received extensive formal explicit training in English and lived in an English-speaking environment, they exhibited statistical learning biases congruent with their native language. Our findings suggest that mechanisms of statistical sequential learning are implicated in language across the lifespan, and experience with language may affect cognitive processes and later learning. PMID:23200510

Propose but verify: Fast mapping meets cross-situational word learning

PubMed Central

Trueswell, John C.; Medina, Tamara Nicol; Hafri, Alon; Gleitman, Lila R.

2012-01-01

We report three eyetracking experiments that examine the learning procedure used by adults as they pair novel words and visually presented referents over a sequence of referentially ambiguous trials. Successful learning under such conditions has been argued to be the product of a learning procedure in which participants provisionally pair each novel word with several possible referents and use a statistical-associative learning mechanism to gradually converge on a single mapping across learning instances. We argue here that successful learning in this setting is instead the product of a one-trial procedure in which a single hypothesized word-referent pairing is retained across learning instances, abandoned only if the subsequent instance fails to confirm the pairing – more a ‘fast mapping’ procedure than a gradual statistical one. We provide experimental evidence for this Propose-but-Verify learning procedure via three experiments in which adult participants attempted to learn the meanings of nonce words cross-situationally under varying degrees of referential uncertainty. The findings, using both explicit (referent selection) and implicit (eye movement) measures, show that even in these artificial learning contexts, which are far simpler than those encountered by a language learner in a natural environment, participants do not retain multiple meaning hypotheses across learning instances. As we discuss, these findings challenge ‘gradualist’ accounts of word learning and are consistent with the known rapid course of vocabulary learning in a first language. PMID:23142693

Quantitative Skills, Critical Thinking, and Writing Mechanics in Blended versus Face-to-Face Versions of a Research Methods and Statistics Course

ERIC Educational Resources Information Center

Goode, Christopher T.; Lamoreaux, Marika; Atchison, Kristin J.; Jeffress, Elizabeth C.; Lynch, Heather L.; Sheehan, Elizabeth

2018-01-01

Hybrid or blended learning (BL) has been shown to be equivalent to or better than face-to-face (FTF) instruction in a broad variety of contexts. We randomly assigned students to either 50/50 BL or 100% FTF versions of a research methods and statistics in psychology course. Students who took the BL version of the course scored significantly lower…

Neurocognitive mechanisms of statistical-sequential learning: what do event-related potentials tell us?

PubMed Central

Daltrozzo, Jerome; Conway, Christopher M.

2014-01-01

Statistical-sequential learning (SL) is the ability to process patterns of environmental stimuli, such as spoken language, music, or one’s motor actions, that unfold in time. The underlying neurocognitive mechanisms of SL and the associated cognitive representations are still not well understood as reflected by the heterogeneity of the reviewed cognitive models. The purpose of this review is: (1) to provide a general overview of the primary models and theories of SL, (2) to describe the empirical research – with a focus on the event-related potential (ERP) literature – in support of these models while also highlighting the current limitations of this research, and (3) to present a set of new lines of ERP research to overcome these limitations. The review is articulated around three descriptive dimensions in relation to SL: the level of abstractness of the representations learned through SL, the effect of the level of attention and consciousness on SL, and the developmental trajectory of SL across the life-span. We conclude with a new tentative model that takes into account these three dimensions and also point to several promising new lines of SL research. PMID:24994975

More than words: Adults learn probabilities over categories and relationships between them.

PubMed

Hudson Kam, Carla L

2009-04-01

This study examines whether human learners can acquire statistics over abstract categories and their relationships to each other. Adult learners were exposed to miniature artificial languages containing variation in the ordering of the Subject, Object, and Verb constituents. Different orders (e.g. SOV, VSO) occurred in the input with different frequencies, but the occurrence of one order versus another was not predictable. Importantly, the language was constructed such that participants could only match the overall input probabilities if they were tracking statistics over abstract categories, not over individual words. At test, participants reproduced the probabilities present in the input with a high degree of accuracy. Closer examination revealed that learner's were matching the probabilities associated with individual verbs rather than the category as a whole. However, individual nouns had no impact on word orders produced. Thus, participants learned the probabilities of a particular ordering of the abstract grammatical categories Subject and Object associated with each verb. Results suggest that statistical learning mechanisms are capable of tracking relationships between abstract linguistic categories in addition to individual items.

Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials.

PubMed

Potter, Christine E; Wang, Tianlin; Saffran, Jenny R

2017-04-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, 6 months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, whereas both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli. Copyright © 2016 Cognitive Science Society, Inc.

Second language experience facilitates statistical learning of novel linguistic materials

PubMed Central

Potter, Christine E.; Wang, Tianlin; Saffran, Jenny R.

2016-01-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In the present research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, six months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, while both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli. PMID:27988939

Language experience changes subsequent learning.

PubMed

Onnis, Luca; Thiessen, Erik

2013-02-01

What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non-linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses, and are driven by probabilistic preferences. Furthermore, although the Korean individuals had received extensive formal explicit training in English and lived in an English-speaking environment, they exhibited statistical learning biases congruent with their native language. Our findings suggest that mechanisms of statistical sequential learning are implicated in language across the lifespan, and experience with language may affect cognitive processes and later learning. Copyright © 2012 Elsevier B.V. All rights reserved.

Factors Affecting the Nutritional Status of Pregnant Women

DTIC Science & Technology

1989-01-01

asphyxia during the labor process , resulting in varying degrees of brain damage or even death. Statistically, there is a higher mortality rate for...1976). A person can respond to the various stimuli through two mechanisms. First is the regulator mechanism which involves the processes that result in...status also improves. This alteration in nutritional status is evidence of information processing , learning, and improved Jludgement which are all aspects

Competitive repetition suppression (CoRe) clustering: a biologically inspired learning model with application to robust clustering.

PubMed

Bacciu, Davide; Starita, Antonina

2008-11-01

Determining a compact neural coding for a set of input stimuli is an issue that encompasses several biological memory mechanisms as well as various artificial neural network models. In particular, establishing the optimal network structure is still an open problem when dealing with unsupervised learning models. In this paper, we introduce a novel learning algorithm, named competitive repetition-suppression (CoRe) learning, inspired by a cortical memory mechanism called repetition suppression (RS). We show how such a mechanism is used, at various levels of the cerebral cortex, to generate compact neural representations of the visual stimuli. From the general CoRe learning model, we derive a clustering algorithm, named CoRe clustering, that can automatically estimate the unknown cluster number from the data without using a priori information concerning the input distribution. We illustrate how CoRe clustering, besides its biological plausibility, posses strong theoretical properties in terms of robustness to noise and outliers, and we provide an error function describing CoRe learning dynamics. Such a description is used to analyze CoRe relationships with the state-of-the art clustering models and to highlight CoRe similitude with rival penalized competitive learning (RPCL), showing how CoRe extends such a model by strengthening the rival penalization estimation by means of loss functions from robust statistics.

The computational nature of memory modification.

PubMed

Gershman, Samuel J; Monfils, Marie-H; Norman, Kenneth A; Niv, Yael

2017-03-15

Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature.

Intact implicit learning in autism spectrum conditions.

PubMed

Brown, Jamie; Aczel, Balazs; Jiménez, Luis; Kaufman, Scott Barry; Grant, Kate Plaisted

2010-09-01

Individuals with autism spectrum condition (ASC) have diagnostic impairments in skills that are associated with an implicit acquisition; however, it is not clear whether ASC individuals show specific implicit learning deficits. We compared ASC and typically developing (TD) individuals matched for IQ on five learning tasks: four implicit learning tasks--contextual cueing, serial reaction time, artificial grammar learning, and probabilistic classification learning tasks--that used procedures expressly designed to minimize the use of explicit strategies, and one comparison explicit learning task, paired associates learning. We found implicit learning to be intact in ASC. Beyond no evidence of differences, there was evidence of statistical equivalence between the groups on all the implicit learning tasks. This was not a consequence of compensation by explicit learning ability or IQ. Furthermore, there was no evidence to relate implicit learning to ASC symptomatology. We conclude that implicit mechanisms are preserved in ASC and propose that it is disruption by other atypical processes that impact negatively on the development of skills associated with an implicit acquisition.

Particle Swarm Optimization with Double Learning Patterns.

PubMed

Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

2016-01-01

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants.

Equifinality in empirical studies of cultural transmission.

PubMed

Barrett, Brendan J

2018-01-31

Cultural systems exhibit equifinal behavior - a single final state may be arrived at via different mechanisms and/or from different initial states. Potential for equifinality exists in all empirical studies of cultural transmission including controlled experiments, observational field research, and computational simulations. Acknowledging and anticipating the existence of equifinality is important in empirical studies of social learning and cultural evolution; it helps us understand the limitations of analytical approaches and can improve our ability to predict the dynamics of cultural transmission. Here, I illustrate and discuss examples of equifinality in studies of social learning, and how certain experimental designs might be prone to it. I then review examples of equifinality discussed in the social learning literature, namely the use of s-shaped diffusion curves to discern individual from social learning and operational definitions and analytical approaches used in studies of conformist transmission. While equifinality exists to some extent in all studies of social learning, I make suggestions for how to address instances of it, with an emphasis on using data simulation and methodological verification alongside modern statistical approaches that emphasize prediction and model comparison. In cases where evaluated learning mechanisms are equifinal due to non-methodological factors, I suggest that this is not always a problem if it helps us predict cultural change. In some cases, equifinal learning mechanisms might offer insight into how both individual learning, social learning strategies and other endogenous social factors might by important in structuring cultural dynamics and within- and between-group heterogeneity. Copyright © 2018 Elsevier B.V. All rights reserved.

Statistical learning of music- and language-like sequences and tolerance for spectral shifts.

PubMed

Daikoku, Tatsuya; Yatomi, Yutaka; Yumoto, Masato

2015-02-01

In our previous study (Daikoku, Yatomi, & Yumoto, 2014), we demonstrated that the N1m response could be a marker for the statistical learning process of pitch sequence, in which each tone was ordered by a Markov stochastic model. The aim of the present study was to investigate how the statistical learning of music- and language-like auditory sequences is reflected in the N1m responses based on the assumption that both language and music share domain generality. By using vowel sounds generated by a formant synthesizer, we devised music- and language-like auditory sequences in which higher-ordered transitional rules were embedded according to a Markov stochastic model by controlling fundamental (F0) and/or formant frequencies (F1-F2). In each sequence, F0 and/or F1-F2 were spectrally shifted in the last one-third of the tone sequence. Neuromagnetic responses to the tone sequences were recorded from 14 right-handed normal volunteers. In the music- and language-like sequences with pitch change, the N1m responses to the tones that appeared with higher transitional probability were significantly decreased compared with the responses to the tones that appeared with lower transitional probability within the first two-thirds of each sequence. Moreover, the amplitude difference was even retained within the last one-third of the sequence after the spectral shifts. However, in the language-like sequence without pitch change, no significant difference could be detected. The pitch change may facilitate the statistical learning in language and music. Statistically acquired knowledge may be appropriated to process altered auditory sequences with spectral shifts. The relative processing of spectral sequences may be a domain-general auditory mechanism that is innate to humans. Copyright © 2014 Elsevier Inc. All rights reserved.

Cross-situational word learning in aphasia.

PubMed

Peñaloza, Claudia; Mirman, Daniel; Cardona, Pedro; Juncadella, Montserrat; Martin, Nadine; Laine, Matti; Rodríguez-Fornells, Antoni

2017-08-01

Human learners can resolve referential ambiguity and discover the relationships between words and meanings through a cross-situational learning (CSL) strategy. Some people with aphasia (PWA) can learn word-referent pairings under referential uncertainty supported by online feedback. However, it remains unknown whether PWA can learn new words cross-situationally and if such learning ability is supported by statistical learning (SL) mechanisms. The present study examined whether PWA can learn novel word-referent mappings in a CSL task without feedback. We also studied whether CSL is related to SL in PWA and neurologically healthy individuals. We further examined whether aphasia severity, phonological processing and verbal short-term memory (STM) predict CSL in aphasia, and also whether individual differences in verbal STM modulate CSL in healthy older adults. Sixteen people with chronic aphasia underwent a CSL task that involved exposure to a series of individually ambiguous learning trials and a SL task that taps speech segmentation. Their learning ability was compared to 18 older controls and 39 young adults recruited for task validation. CSL in the aphasia group was below the older controls and young adults and took place at a slower rate. Importantly, we found a strong association between SL and CSL performance in all three groups. CSL was modulated by aphasia severity in the aphasia group, and by verbal STM capacity in the older controls. Our findings indicate that some PWA can preserve the ability to learn new word-referent associations cross-situationally. We suggest that both PWA and neurologically intact individuals may rely on SL mechanisms to achieve CSL and that verbal STM also influences CSL. These findings contribute to the ongoing debate on the cognitive mechanisms underlying this learning ability. Copyright © 2017 Elsevier Ltd. All rights reserved.

Learning the Language of Statistics: Challenges and Teaching Approaches

ERIC Educational Resources Information Center

Dunn, Peter K.; Carey, Michael D.; Richardson, Alice M.; McDonald, Christine

2016-01-01

Learning statistics requires learning the language of statistics. Statistics draws upon words from general English, mathematical English, discipline-specific English and words used primarily in statistics. This leads to many linguistic challenges in teaching statistics and the way in which the language is used in statistics creates an extra layer…

Sampling over Nonuniform Distributions: A Neural Efficiency Account of the Primacy Effect in Statistical Learning.

PubMed

Karuza, Elisabeth A; Li, Ping; Weiss, Daniel J; Bulgarelli, Federica; Zinszer, Benjamin D; Aslin, Richard N

2016-10-01

Successful knowledge acquisition requires a cognitive system that is both sensitive to statistical information and able to distinguish among multiple structures (i.e., to detect pattern shifts and form distinct representations). Extensive behavioral evidence has highlighted the importance of cues to structural change, demonstrating how, without them, learners fail to detect pattern shifts and are biased in favor of early experience. Here, we seek a neural account of the mechanism underpinning this primacy effect in learning. During fMRI scanning, adult participants were presented with two artificial languages: a familiar language (L1) on which they had been pretrained followed by a novel language (L2). The languages were composed of the same syllable inventory organized according to unique statistical structures. In the absence of cues to the transition between languages, posttest familiarity judgments revealed that learners on average more accurately segmented words from the familiar language compared with the novel one. Univariate activation and functional connectivity analyses showed that participants with the strongest learning of L1 had decreased recruitment of fronto-subcortical and posterior parietal regions, in addition to a dissociation between downstream regions and early auditory cortex. Participants with a strong new language learning capacity (i.e., higher L2 scores) showed the opposite trend. Thus, we suggest that a bias toward neural efficiency, particularly as manifested by decreased sampling from the environment, accounts for the primacy effect in learning. Potential implications of this hypothesis are discussed, including the possibility that "inefficient" learning systems may be more sensitive to structural changes in a dynamic environment.

The computational nature of memory modification

PubMed Central

Gershman, Samuel J; Monfils, Marie-H; Norman, Kenneth A; Niv, Yael

2017-01-01

Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature. DOI: http://dx.doi.org/10.7554/eLife.23763.001 PMID:28294944

Self-Regulated Learning Strategies in Relation with Statistics Anxiety

ERIC Educational Resources Information Center

Kesici, Sahin; Baloglu, Mustafa; Deniz, M. Engin

2011-01-01

Dealing with students' attitudinal problems related to statistics is an important aspect of statistics instruction. Employing the appropriate learning strategies may have a relationship with anxiety during the process of statistics learning. Thus, the present study investigated multivariate relationships between self-regulated learning strategies…

Alternative to Proctoring in Introductory Statistics Community College Courses

ERIC Educational Resources Information Center

Feinman, Yalena

2018-01-01

The credibility of unsupervised exams, one of the biggest challenges of e-learning, is currently maintained by proctoring. However, little has been done to determine whether expensive and inconvenient proctoring is necessary. The purpose of this quantitative study was to determine whether the use of security mechanisms, based on the taxonomy of…

Statistical learning of speech sounds is most robust during the period of perceptual attunement.

PubMed

Liu, Liquan; Kager, René

2017-12-01

Although statistical learning has been shown to be a domain-general mechanism, its constraints, such as its interactions with perceptual development, are less well understood and discussed. This study is among the first to investigate the distributional learning of lexical pitch in non-tone-language-learning infants, exploring its interaction with language-specific perceptual attunement during the first 2years after birth. A total of 88 normally developing Dutch infants of 5, 11, and 14months were tested via a distributional learning paradigm and were familiarized on a unimodal or bimodal distribution of high-level versus high-falling tones in Mandarin Chinese. After familiarization, they were tested on a tonal contrast that shared equal distributional information in either modality. At 5months, infants in both conditions discriminated the contrast, whereas 11-month-olds showed discrimination only in the bimodal condition. By 14months, infants failed to discriminate the contrast in either condition. Results indicate interplay between infants' long-term linguistic experience throughout development and short-term distributional learning during the experiment, and they suggest that the influence of tonal distributional learning varies along the perceptual attunement trajectory, such that opportunities for distributional learning effects appear to be constrained in the beginning and at the end of perceptual attunement. The current study contributes to previous research by demonstrating an effect of age on learning from distributional cues. Copyright © 2017 Elsevier Inc. All rights reserved.

The efficacy of student-centered instruction in supporting science learning.

PubMed

Granger, E M; Bevis, T H; Saka, Y; Southerland, S A; Sampson, V; Tate, R L

2012-10-05

Transforming science learning through student-centered instruction that engages students in a variety of scientific practices is central to national science-teaching reform efforts. Our study employed a large-scale, randomized-cluster experimental design to compare the effects of student-centered and teacher-centered approaches on elementary school students' understanding of space-science concepts. Data included measures of student characteristics and learning and teacher characteristics and fidelity to the instructional approach. Results reveal that learning outcomes were higher for students enrolled in classrooms engaging in scientific practices through a student-centered approach; two moderators were identified. A statistical search for potential causal mechanisms for the observed outcomes uncovered two potential mediators: students' understanding of models and evidence and the self-efficacy of teachers.

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures

PubMed Central

Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology. PMID:27014147

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures.

PubMed

Rock, Adam J; Coventry, William L; Morgan, Methuen I; Loi, Natasha M

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.

Infant Statistical-Learning Ability Is Related to Real-Time Language Processing

ERIC Educational Resources Information Center

Lany, Jill; Shoaib, Amber; Thompson, Abbie; Estes, Katharine Graf

2018-01-01

Infants are adept at learning statistical regularities in artificial language materials, suggesting that the ability to learn statistical structure may support language development. Indeed, infants who perform better on statistical learning tasks tend to be more advanced in parental reports of infants' language skills. Work with adults suggests…

Statistical Learning Is Related to Early Literacy-Related Skills

ERIC Educational Resources Information Center

Spencer, Mercedes; Kaschak, Michael P.; Jones, John L.; Lonigan, Christopher J.

2015-01-01

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one's environment, plays a role in young children's acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from…

Statistical mechanics of competitive resource allocation using agent-based models

NASA Astrophysics Data System (ADS)

Chakraborti, Anirban; Challet, Damien; Chatterjee, Arnab; Marsili, Matteo; Zhang, Yi-Cheng; Chakrabarti, Bikas K.

2015-01-01

Demand outstrips available resources in most situations, which gives rise to competition, interaction and learning. In this article, we review a broad spectrum of multi-agent models of competition (El Farol Bar problem, Minority Game, Kolkata Paise Restaurant problem, Stable marriage problem, Parking space problem and others) and the methods used to understand them analytically. We emphasize the power of concepts and tools from statistical mechanics to understand and explain fully collective phenomena such as phase transitions and long memory, and the mapping between agent heterogeneity and physical disorder. As these methods can be applied to any large-scale model of competitive resource allocation made up of heterogeneous adaptive agent with non-linear interaction, they provide a prospective unifying paradigm for many scientific disciplines.

Origin of the spike-timing-dependent plasticity rule

NASA Astrophysics Data System (ADS)

Cho, Myoung Won; Choi, M. Y.

2016-08-01

A biological synapse changes its efficacy depending on the difference between pre- and post-synaptic spike timings. Formulating spike-timing-dependent interactions in terms of the path integral, we establish a neural-network model, which makes it possible to predict relevant quantities rigorously by means of standard methods in statistical mechanics and field theory. In particular, the biological synaptic plasticity rule is shown to emerge as the optimal form for minimizing the free energy. It is further revealed that maximization of the entropy of neural activities gives rise to the competitive behavior of biological learning. This demonstrates that statistical mechanics helps to understand rigorously key characteristic behaviors of a neural network, thus providing the possibility of physics serving as a useful and relevant framework for probing life.

Statistical Mechanics: A Concise Introduction for Chemists (by Benjamin Widom)

NASA Astrophysics Data System (ADS)

Kovac, Jeffrey

2002-11-01

This book should be in the hands of everyone who teaches undergraduate physical chemistry to provide a model for what can be taught in that course beyond the material contained in the standard textbooks. Graduate students and faculty who need to learn statistical mechanics can hardly find a better introduction. Even those who regularly teach a graduate course in this area will get some new ideas and inspiration from one of the leading practitioners of the field. For completeness, I must add that the book has one weakness. Although there are excellent in-chapter exercises with solutions, there are no end-of-chapter problems. Since there are many sources of good problems, this is a minor flaw in an otherwise wonderful book.

The extraction and integration framework: a two-process account of statistical learning.

PubMed

Thiessen, Erik D; Kronstein, Alexandra T; Hufnagle, Daniel G

2013-07-01

The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other. 2013 APA, all rights reserved

Helping Students Develop Statistical Reasoning: Implementing a Statistical Reasoning Learning Environment

ERIC Educational Resources Information Center

Garfield, Joan; Ben-Zvi, Dani

2009-01-01

This article describes a model for an interactive, introductory secondary- or tertiary-level statistics course that is designed to develop students' statistical reasoning. This model is called a "Statistical Reasoning Learning Environment" and is built on the constructivist theory of learning.

Statistical Learning is Related to Early Literacy-Related Skills

PubMed Central

Spencer, Mercedes; Kaschak, Michael P.; Jones, John L.; Lonigan, Christopher J.

2015-01-01

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one’s environment, plays a role in young children’s acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from fluent speech and the learning of syntactic structure, some recent studies have explored the extent to which individual differences in statistical learning are related to literacy-relevant knowledge and skills. The present study extends on this literature by investigating the relations between two measures of statistical learning and multiple measures of skills that are critical to the development of literacy—oral language, vocabulary knowledge, and phonological processing—within a single model. Our sample included a total of 553 typically developing children from prekindergarten through second grade. Structural equation modeling revealed that statistical learning accounted for a unique portion of the variance in these literacy-related skills. Practical implications for instruction and assessment are discussed. PMID:26478658

A Study of the Effectiveness of the Contextual Lab Activity in the Teaching and Learning Statistics at the UTHM (Universiti Tun Hussein Onn Malaysia)

ERIC Educational Resources Information Center

Kamaruddin, Nafisah Kamariah Md; Jaafar, Norzilaila bt; Amin, Zulkarnain Md

2012-01-01

Inaccurate concept in statistics contributes to the assumption by the students that statistics do not relate to the real world and are not relevant to the engineering field. There are universities which introduced learning statistics using statistics lab activities. However, the learning is more on the learning how to use software and not to…

Statistical Machine Learning for Structured and High Dimensional Data

DTIC Science & Technology

2014-09-17

AFRL-OSR-VA-TR-2014-0234 STATISTICAL MACHINE LEARNING FOR STRUCTURED AND HIGH DIMENSIONAL DATA Larry Wasserman CARNEGIE MELLON UNIVERSITY Final...Re . 8-98) v Prescribed by ANSI Std. Z39.18 14-06-2014 Final Dec 2009 - Aug 2014 Statistical Machine Learning for Structured and High Dimensional...area of resource-constrained statistical estimation. machine learning , high-dimensional statistics U U U UU John Lafferty 773-702-3813 > Research under

Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials

ERIC Educational Resources Information Center

Potter, Christine E.; Wang, Tianlin; Saffran, Jenny R.

2017-01-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning…

Commentary: Decaying Numerical Skills. "I Can't Divide by 60 in My Head!"

ERIC Educational Resources Information Center

Parslow, Graham R.

2010-01-01

As an undergraduate in the 1960s, the author mostly used a slide rule for calculations and a Marchant-brand motor-operated mechanical calculator for statistics. This was after an elementary education replete with learning multiplication tables and taking speed and accuracy tests in arithmetic. Times have changed and assuming even basic calculation…

Enhanced transformation of incidentally learned knowledge into explicit memory by dopaminergic modulation.

PubMed

Clos, Mareike; Sommer, Tobias; Schneider, Signe L; Rose, Michael

2018-01-01

During incidental learning statistical regularities are extracted from the environment without the intention to learn. Acquired implicit memory of these regularities can affect behavior in the absence of awareness. However, conscious insight in the underlying regularities can also develop during learning. Such emergence of explicit memory is an important learning mechanism that is assumed to involve prediction errors in the striatum and to be dopamine-dependent. Here we directly tested this hypothesis by manipulating dopamine levels during incidental learning in a modified serial reaction time task (SRTT) featuring a hidden regular sequence of motor responses in a placebo-controlled between-group study. Awareness for the sequential regularity was subsequently assessed using cued generation and additionally verified using free recall. The results demonstrated that dopaminergic modulation nearly doubled the amount of explicit sequence knowledge emerged during learning in comparison to the placebo group. This strong effect clearly argues for a causal role of dopamine-dependent processing for the development of awareness for sequential regularities during learning.

Particle Swarm Optimization with Double Learning Patterns

PubMed Central

Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

2016-01-01

Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747

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.

Is Statistical Learning Constrained by Lower Level Perceptual Organization?

PubMed Central

Emberson, Lauren L.; Liu, Ran; Zevin, Jason D.

2013-01-01

In order for statistical information to aid in complex developmental processes such as language acquisition, learning from higher-order statistics (e.g. across successive syllables in a speech stream to support segmentation) must be possible while perceptual abilities (e.g. speech categorization) are still developing. The current study examines how perceptual organization interacts with statistical learning. Adult participants were presented with multiple exemplars from novel, complex sound categories designed to reflect some of the spectral complexity and variability of speech. These categories were organized into sequential pairs and presented such that higher-order statistics, defined based on sound categories, could support stream segmentation. Perceptual similarity judgments and multi-dimensional scaling revealed that participants only perceived three perceptual clusters of sounds and thus did not distinguish the four experimenter-defined categories, creating a tension between lower level perceptual organization and higher-order statistical information. We examined whether the resulting pattern of learning is more consistent with statistical learning being “bottom-up,” constrained by the lower levels of organization, or “top-down,” such that higher-order statistical information of the stimulus stream takes priority over the perceptual organization, and perhaps influences perceptual organization. We consistently find evidence that learning is constrained by perceptual organization. Moreover, participants generalize their learning to novel sounds that occupy a similar perceptual space, suggesting that statistical learning occurs based on regions of or clusters in perceptual space. Overall, these results reveal a constraint on learning of sound sequences, such that statistical information is determined based on lower level organization. These findings have important implications for the role of statistical learning in language acquisition. PMID:23618755

What Can Reinforcement Learning Teach Us About Non-Equilibrium Quantum Dynamics

NASA Astrophysics Data System (ADS)

Bukov, Marin; Day, Alexandre; Sels, Dries; Weinberg, Phillip; Polkovnikov, Anatoli; Mehta, Pankaj

Equilibrium thermodynamics and statistical physics are the building blocks of modern science and technology. Yet, our understanding of thermodynamic processes away from equilibrium is largely missing. In this talk, I will reveal the potential of what artificial intelligence can teach us about the complex behaviour of non-equilibrium systems. Specifically, I will discuss the problem of finding optimal drive protocols to prepare a desired target state in quantum mechanical systems by applying ideas from Reinforcement Learning [one can think of Reinforcement Learning as the study of how an agent (e.g. a robot) can learn and perfect a given policy through interactions with an environment.]. The driving protocols learnt by our agent suggest that the non-equilibrium world features possibilities easily defying intuition based on equilibrium physics.

Distinct contributions of attention and working memory to visual statistical learning and ensemble processing.

PubMed

Hall, Michelle G; Mattingley, Jason B; Dux, Paul E

2015-08-01

The brain exploits redundancies in the environment to efficiently represent the complexity of the visual world. One example of this is ensemble processing, which provides a statistical summary of elements within a set (e.g., mean size). Another is statistical learning, which involves the encoding of stable spatial or temporal relationships between objects. It has been suggested that ensemble processing over arrays of oriented lines disrupts statistical learning of structure within the arrays (Zhao, Ngo, McKendrick, & Turk-Browne, 2011). Here we asked whether ensemble processing and statistical learning are mutually incompatible, or whether this disruption might occur because ensemble processing encourages participants to process the stimulus arrays in a way that impedes statistical learning. In Experiment 1, we replicated Zhao and colleagues' finding that ensemble processing disrupts statistical learning. In Experiments 2 and 3, we found that statistical learning was unimpaired by ensemble processing when task demands necessitated (a) focal attention to individual items within the stimulus arrays and (b) the retention of individual items in working memory. Together, these results are consistent with an account suggesting that ensemble processing and statistical learning can operate over the same stimuli given appropriate stimulus processing demands during exposure to regularities. (c) 2015 APA, all rights reserved).

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Musicians' edge: A comparison of auditory processing, cognitive abilities and statistical learning.

PubMed

Mandikal Vasuki, Pragati Rao; Sharma, Mridula; Demuth, Katherine; Arciuli, Joanne

2016-12-01

It has been hypothesized that musical expertise is associated with enhanced auditory processing and cognitive abilities. Recent research has examined the relationship between musicians' advantage and implicit statistical learning skills. In the present study, we assessed a variety of auditory processing skills, cognitive processing skills, and statistical learning (auditory and visual forms) in age-matched musicians (N = 17) and non-musicians (N = 18). Musicians had significantly better performance than non-musicians on frequency discrimination, and backward digit span. A key finding was that musicians had better auditory, but not visual, statistical learning than non-musicians. Performance on the statistical learning tasks was not correlated with performance on auditory and cognitive measures. Musicians' superior performance on auditory (but not visual) statistical learning suggests that musical expertise is associated with an enhanced ability to detect statistical regularities in auditory stimuli. Copyright © 2016 Elsevier B.V. All rights reserved.

Explorations in Statistics: Hypothesis Tests and P Values

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This second installment of "Explorations in Statistics" delves into test statistics and P values, two concepts fundamental to the test of a scientific null hypothesis. The essence of a test statistic is that it compares what…

Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum

PubMed Central

Milic, Natasa M.; Trajkovic, Goran Z.; Bukumiric, Zoran M.; Cirkovic, Andja; Nikolic, Ivan M.; Milin, Jelena S.; Milic, Nikola V.; Savic, Marko D.; Corac, Aleksandar M.; Marinkovic, Jelena M.; Stanisavljevic, Dejana M.

2016-01-01

Background Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. Methods This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013–14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Results Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001). Conclusion This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics. PMID:26859832

Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum.

PubMed

Milic, Natasa M; Trajkovic, Goran Z; Bukumiric, Zoran M; Cirkovic, Andja; Nikolic, Ivan M; Milin, Jelena S; Milic, Nikola V; Savic, Marko D; Corac, Aleksandar M; Marinkovic, Jelena M; Stanisavljevic, Dejana M

2016-01-01

Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013-14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001). This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics.

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. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation. PMID:27139779

Statistical Learning, Syllable Processing, and Speech Production in Healthy Hearing and Hearing-Impaired Preschool Children: A Mismatch Negativity Study.

PubMed

Studer-Eichenberger, Esther; Studer-Eichenberger, Felix; Koenig, Thomas

2016-01-01

The objectives of the present study were to investigate temporal/spectral sound-feature processing in preschool children (4 to 7 years old) with peripheral hearing loss compared with age-matched controls. The results verified the presence of statistical learning, which was diminished in children with hearing impairments (HIs), and elucidated possible perceptual mediators of speech production. Perception and production of the syllables /ba/, /da/, /ta/, and /na/ were recorded in 13 children with normal hearing and 13 children with HI. Perception was assessed physiologically through event-related potentials (ERPs) recorded by EEG in a multifeature mismatch negativity paradigm and behaviorally through a discrimination task. Temporal and spectral features of the ERPs during speech perception were analyzed, and speech production was quantitatively evaluated using speech motor maximum performance tasks. Proximal to stimulus onset, children with HI displayed a difference in map topography, indicating diminished statistical learning. In later ERP components, children with HI exhibited reduced amplitudes in the N2 and early parts of the late disciminative negativity components specifically, which are associated with temporal and spectral control mechanisms. Abnormalities of speech perception were only subtly reflected in speech production, as the lone difference found in speech production studies was a mild delay in regulating speech intensity. In addition to previously reported deficits of sound-feature discriminations, the present study results reflect diminished statistical learning in children with HI, which plays an early and important, but so far neglected, role in phonological processing. Furthermore, the lack of corresponding behavioral abnormalities in speech production implies that impaired perceptual capacities do not necessarily translate into productive deficits.

Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm.

PubMed

Sandoval, Michelle; Patterson, Dianne; Dai, Huanping; Vance, Christopher J; Plante, Elena

2017-01-01

The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the "rules" for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system.

Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm

PubMed Central

Sandoval, Michelle; Patterson, Dianne; Dai, Huanping; Vance, Christopher J.; Plante, Elena

2017-01-01

The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the “rules” for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system. PMID:28798703

Online neural monitoring of statistical learning

PubMed Central

Batterink, Laura J.; Paller, Ken A.

2017-01-01

The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the RT task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning. PMID:28324696

Learning Statistics at the Farmers Market? A Comparison of Academic Service Learning and Case Studies in an Introductory Statistics Course

ERIC Educational Resources Information Center

Hiedemann, Bridget; Jones, Stacey M.

2010-01-01

We compare the effectiveness of academic service learning to that of case studies in an undergraduate introductory business statistics course. Students in six sections of the course were assigned either an academic service learning project (ASL) or business case studies (CS). We examine two learning outcomes: students' performance on the final…

Stochastic Dynamics of Lexicon Learning in an Uncertain and Nonuniform World

NASA Astrophysics Data System (ADS)

Reisenauer, Rainer; Smith, Kenny; Blythe, Richard A.

2013-06-01

We study the time taken by a language learner to correctly identify the meaning of all words in a lexicon under conditions where many plausible meanings can be inferred whenever a word is uttered. We show that the most basic form of cross-situational learning—whereby information from multiple episodes is combined to eliminate incorrect meanings—can perform badly when words are learned independently and meanings are drawn from a nonuniform distribution. If learners further assume that no two words share a common meaning, we find a phase transition between a maximally efficient learning regime, where the learning time is reduced to the shortest it can possibly be, and a partially efficient regime where incorrect candidate meanings for words persist at late times. We obtain exact results for the word-learning process through an equivalence to a statistical mechanical problem of enumerating loops in the space of word-meaning mappings.

Evaluation of ambiguous associations in the amygdala by learning the structure of the environment

PubMed Central

Madarasz, Tamas J.; Diaz-Mataix, Lorenzo; Akhand, Omar; Ycu, Edgar A.; LeDoux, Joseph E.; Johansen, Joshua P.

2017-01-01

Recognizing predictive relationships is critical for survival, but an understanding of the underlying neural mechanisms remains elusive. In particular it is unclear how the brain distinguishes predictive relationships from spurious ones when evidence about a relationship is ambiguous, or how it computes predictions given such uncertainty. To better understand this process we introduced ambiguity into an associative learning task by presenting aversive outcomes both in the presence and absence of a predictive cue. Electrophysiological and optogenetic approaches revealed that amygdala neurons directly regulate and track the effects of ambiguity on learning. Contrary to established accounts of associative learning however, interference from competing associations was not required to assess an ambiguous cue-outcome contingency. Instead, animals’ behavior was explained by a normative account that evaluates different models of the environment’s statistical structure. These findings suggest an alternative view on the role of amygdala circuits in resolving ambiguity during aversive learning. PMID:27214568

Evaluation of ambiguous associations in the amygdala by learning the structure of the environment.

PubMed

Madarasz, Tamas J; Diaz-Mataix, Lorenzo; Akhand, Omar; Ycu, Edgar A; LeDoux, Joseph E; Johansen, Joshua P

2016-07-01

Recognizing predictive relationships is critical for survival, but an understanding of the underlying neural mechanisms remains elusive. In particular, it is unclear how the brain distinguishes predictive relationships from spurious ones when evidence about a relationship is ambiguous, or how it computes predictions given such uncertainty. To better understand this process, we introduced ambiguity into an associative learning task by presenting aversive outcomes both in the presence and in the absence of a predictive cue. Electrophysiological and optogenetic approaches revealed that amygdala neurons directly regulated and tracked the effects of ambiguity on learning. Contrary to established accounts of associative learning, however, interference from competing associations was not required to assess an ambiguous cue-outcome contingency. Instead, animals' behavior was explained by a normative account that evaluates different models of the environment's statistical structure. These findings suggest an alternative view of amygdala circuits in resolving ambiguity during aversive learning.

Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD.

PubMed

Jeste, Shafali S; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F N; Johnson, Scott P

2015-01-01

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event-related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non-verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non-verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non-verbal cognition and adaptive social function. © 2014 John Wiley & Sons Ltd.

Changing viewer perspectives reveals constraints to implicit visual statistical learning.

PubMed

Jiang, Yuhong V; Swallow, Khena M

2014-10-07

Statistical learning-learning environmental regularities to guide behavior-likely plays an important role in natural human behavior. One potential use is in search for valuable items. Because visual statistical learning can be acquired quickly and without intention or awareness, it could optimize search and thereby conserve energy. For this to be true, however, visual statistical learning needs to be viewpoint invariant, facilitating search even when people walk around. To test whether implicit visual statistical learning of spatial information is viewpoint independent, we asked participants to perform a visual search task from variable locations around a monitor placed flat on a stand. Unbeknownst to participants, the target was more often in some locations than others. In contrast to previous research on stationary observers, visual statistical learning failed to produce a search advantage for targets in high-probable regions that were stable within the environment but variable relative to the viewer. This failure was observed even when conditions for spatial updating were optimized. However, learning was successful when the rich locations were referenced relative to the viewer. We conclude that changing viewer perspective disrupts implicit learning of the target's location probability. This form of learning shows limited integration with spatial updating or spatiotopic representations. © 2014 ARVO.

Functional Differences between Statistical Learning with and without Explicit Training

ERIC Educational Resources Information Center

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

2015-01-01

Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and…

Statistically Modeling Individual Students' Learning over Successive Collaborative Practice Opportunities

ERIC Educational Resources Information Center

Olsen, Jennifer; Aleven, Vincent; Rummel, Nikol

2017-01-01

Within educational data mining, many statistical models capture the learning of students working individually. However, not much work has been done to extend these statistical models of individual learning to a collaborative setting, despite the effectiveness of collaborative learning activities. We extend a widely used model (the additive factors…

Explorations in Statistics: the Bootstrap

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fourth installment of Explorations in Statistics explores the bootstrap. The bootstrap gives us an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the…

SUSTAINABLE ALLOY DESIGN: SEARCHING FOR RARE EARTH ELEMENT ALTERNATIVES THROUGH CRYSTAL ENGINEERING

DTIC Science & Technology

2016-02-26

Property Maps to Guide Materials Design via Statistical Learning Summer Research Group Meeting – Materials by Design Los Alamos National Laboratory, July...Informatics, Rational design , Quantitative correlative spectroscopy and imaging, DFT, In situ high pressure mechanical property measurements, Superalloy...final, technical, interim, memorandum, master’s thesis, progress, quarterly, research , special, group study, etc. 3. DATES COVERED. Indicate the

Statistical Learning Is Not Affected by a Prior Bout of Physical Exercise.

PubMed

Stevens, David J; Arciuli, Joanne; Anderson, David I

2016-05-01

This study examined the effect of a prior bout of exercise on implicit cognition. Specifically, we examined whether a prior bout of moderate intensity exercise affected performance on a statistical learning task in healthy adults. A total of 42 participants were allocated to one of three conditions-a control group, a group that exercised for 15 min prior to the statistical learning task, and a group that exercised for 30 min prior to the statistical learning task. The participants in the exercise groups cycled at 60% of their respective V˙O2 max. Each group demonstrated significant statistical learning, with similar levels of learning among the three groups. Contrary to previous research that has shown that a prior bout of exercise can affect performance on explicit cognitive tasks, the results of the current study suggest that the physiological stress induced by moderate-intensity exercise does not affect implicit cognition as measured by statistical learning. Copyright © 2015 Cognitive Science Society, Inc.

Electrophysiological Evidence of Heterogeneity in Visual Statistical Learning in Young Children with ASD

ERIC Educational Resources Information Center

Jeste, Shafali S.; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J.; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F. N.; Johnson, Scott P.

2015-01-01

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism…

The Necessity of the Hippocampus for Statistical Learning

PubMed Central

Covington, Natalie V.; Brown-Schmidt, Sarah; Duff, Melissa C.

2018-01-01

Converging evidence points to a role for the hippocampus in statistical learning, but open questions about its necessity remain. Evidence for necessity comes from Schapiro and colleagues who report that a single patient with damage to hippocampus and broader medial temporal lobe cortex was unable to discriminate new from old sequences in several statistical learning tasks. The aim of the current study was to replicate these methods in a larger group of patients who have either damage localized to hippocampus or a broader medial temporal lobe damage, to ascertain the necessity of the hippocampus in statistical learning. Patients with hippocampal damage consistently showed less learning overall compared with healthy comparison participants, consistent with an emerging consensus for hippocampal contributions to statistical learning. Interestingly, lesion size did not reliably predict performance. However, patients with hippocampal damage were not uniformly at chance and demonstrated above-chance performance in some task variants. These results suggest that hippocampus is necessary for statistical learning levels achieved by most healthy comparison participants but significant hippocampal pathology alone does not abolish such learning. PMID:29308986

Online neural monitoring of statistical learning.

PubMed

Batterink, Laura J; Paller, Ken A

2017-05-01

The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the reaction time (RT) task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning. Copyright © 2017 Elsevier Ltd. All rights reserved.

Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models

DTIC Science & Technology

2015-09-12

AFRL-AFOSR-VA-TR-2015-0278 DERIVATIVE FREE OPTIMIZATION OF COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS Katya Scheinberg...COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-11-1-0239 5c.  PROGRAM ELEMENT...developed, which has been the focus of our research. 15. SUBJECT TERMS optimization, Derivative-Free Optimization, Statistical Machine Learning 16. SECURITY

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

PubMed

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

Investigating Students' Acceptance of a Statistics Learning Platform Using Technology Acceptance Model

ERIC Educational Resources Information Center

Song, Yanjie; Kong, Siu-Cheung

2017-01-01

The study aims at investigating university students' acceptance of a statistics learning platform to support the learning of statistics in a blended learning context. Three kinds of digital resources, which are simulations, online videos, and online quizzes, were provided on the platform. Premised on the technology acceptance model, we adopted a…

The Impact of Language Experience on Language and Reading: A Statistical Learning Approach

ERIC Educational Resources Information Center

Seidenberg, Mark S.; MacDonald, Maryellen C.

2018-01-01

This article reviews the important role of statistical learning for language and reading development. Although statistical learning--the unconscious encoding of patterns in language input--has become widely known as a force in infants' early interpretation of speech, the role of this kind of learning for language and reading comprehension in…

Reducing statistics anxiety and enhancing statistics learning achievement: effectiveness of a one-minute strategy.

PubMed

Chiou, Chei-Chang; Wang, Yu-Min; Lee, Li-Tze

2014-08-01

Statistical knowledge is widely used in academia; however, statistics teachers struggle with the issue of how to reduce students' statistics anxiety and enhance students' statistics learning. This study assesses the effectiveness of a "one-minute paper strategy" in reducing students' statistics-related anxiety and in improving students' statistics-related achievement. Participants were 77 undergraduates from two classes enrolled in applied statistics courses. An experiment was implemented according to a pretest/posttest comparison group design. The quasi-experimental design showed that the one-minute paper strategy significantly reduced students' statistics anxiety and improved students' statistics learning achievement. The strategy was a better instructional tool than the textbook exercise for reducing students' statistics anxiety and improving students' statistics achievement.

Having students create short video clips to help transition from naïve conceptions about mechanics to true Newtonian physics

NASA Astrophysics Data System (ADS)

Corten-Gualtieri, Pascale; Ritter, Christian; Plumat, Jim; Keunings, Roland; Lebrun, Marcel; Raucent, Benoit

2016-07-01

Most students enter their first university physics course with a system of beliefs and intuitions which are often inconsistent with the Newtonian frame of reference. This article presents an experiment of collaborative learning aiming at helping first-year students in an engineering programme to transition from their naïve intuition about dynamics to the Newtonian way of thinking. In a first activity, students were asked to critically analyse the contents of two video clips from the point of view of Newtonian mechanics. In a second activity, students had to design and realise their own video clip to illustrate a given aspect of Newtonian mechanics. The preparation of the scenario for the second activity required looking up and assimilating scientific knowledge. The efficiency of the activity was assessed on an enhanced version of the statistical analysis method proposed by Hestenes and Halloun, which relies on a pre-test and a post-test to measure individual learning.

Finnish upper secondary students' collaborative processes in learning statistics in a CSCL environment

NASA Astrophysics Data System (ADS)

Kaleva Oikarinen, Juho; Järvelä, Sanna; Kaasila, Raimo

2014-04-01

This design-based research project focuses on documenting statistical learning among 16-17-year-old Finnish upper secondary school students (N = 78) in a computer-supported collaborative learning (CSCL) environment. One novel value of this study is in reporting the shift from teacher-led mathematical teaching to autonomous small-group learning in statistics. The main aim of this study is to examine how student collaboration occurs in learning statistics in a CSCL environment. The data include material from videotaped classroom observations and the researcher's notes. In this paper, the inter-subjective phenomena of students' interactions in a CSCL environment are analysed by using a contact summary sheet (CSS). The development of the multi-dimensional coding procedure of the CSS instrument is presented. Aptly selected video episodes were transcribed and coded in terms of conversational acts, which were divided into non-task-related and task-related categories to depict students' levels of collaboration. The results show that collaborative learning (CL) can facilitate cohesion and responsibility and reduce students' feelings of detachment in our classless, periodic school system. The interactive .pdf material and collaboration in small groups enable statistical learning. It is concluded that CSCL is one possible method of promoting statistical teaching. CL using interactive materials seems to foster and facilitate statistical learning processes.

Assessing segmentation processes by click detection: online measure of statistical learning, or simple interference?

PubMed

Franco, Ana; Gaillard, Vinciane; Cleeremans, Axel; Destrebecqz, Arnaud

2015-12-01

Statistical learning can be used to extract the words from continuous speech. Gómez, Bion, and Mehler (Language and Cognitive Processes, 26, 212-223, 2011) proposed an online measure of statistical learning: They superimposed auditory clicks on a continuous artificial speech stream made up of a random succession of trisyllabic nonwords. Participants were instructed to detect these clicks, which could be located either within or between words. The results showed that, over the length of exposure, reaction times (RTs) increased more for within-word than for between-word clicks. This result has been accounted for by means of statistical learning of the between-word boundaries. However, even though statistical learning occurs without an intention to learn, it nevertheless requires attentional resources. Therefore, this process could be affected by a concurrent task such as click detection. In the present study, we evaluated the extent to which the click detection task indeed reflects successful statistical learning. Our results suggest that the emergence of RT differences between within- and between-word click detection is neither systematic nor related to the successful segmentation of the artificial language. Therefore, instead of being an online measure of learning, the click detection task seems to interfere with the extraction of statistical regularities.

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

PubMed

Zeng, Irene Sui Lan; Lumley, Thomas

2018-01-01

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Criticality in the brain

NASA Astrophysics Data System (ADS)

de Arcangelis, L.; Lombardi, F.; Herrmann, H. J.

2014-03-01

Spontaneous brain activity has been recently characterized by avalanche dynamics with critical features for systems in vitro and in vivo. In this contribution we present a review of experimental results on neuronal avalanches in cortex slices, together with numerical results from a neuronal model implementing several physiological properties of living neurons. Numerical data reproduce experimental results for avalanche statistics. The temporal organization of avalanches can be characterized by the distribution of waiting times between successive avalanches. Experimental measurements exhibit a non-monotonic behaviour, not usually found in other natural processes. Numerical simulations provide evidence that this behaviour is a consequence of the alternation between states of high and low activity, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homoeostatic mechanisms. Interestingly, the same homoeostatic balance is detected for neuronal activity at the scale of the whole brain. We finally review 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 and the learning dynamics exhibits universal features as a function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

Language at Three Timescales: The Role of Real-Time Processes in Language Development and Evolution.

PubMed

McMurray, Bob

2016-04-01

Evolutionary developmental systems (evo-devo) theory stresses that selection pressures operate on entire developmental systems rather than just genes. This study extends this approach to language evolution, arguing that selection pressure may operate on two quasi-independent timescales. First, children clearly must acquire language successfully (as acknowledged in traditional evo-devo accounts) and evolution must equip them with the tools to do so. Second, while this is developing, they must also communicate with others in the moment using partially developed knowledge. These pressures may require different solutions, and their combination may underlie the evolution of complex mechanisms for language development and processing. I present two case studies to illustrate how the demands of both real-time communication and language acquisition may be subtly different (and interact). The first case study examines infant-directed speech (IDS). A recent view is that IDS underwent cultural to statistical learning mechanisms that infants use to acquire the speech categories of their language. However, recent data suggest is it may not have evolved to enhance development, but rather to serve a more real-time communicative function. The second case study examines the argument for seemingly specialized mechanisms for learning word meanings (e.g., fast-mapping). Both behavioral and computational work suggest that learning may be much slower and served by general-purpose mechanisms like associative learning. Fast-mapping, then, may be a real-time process meant to serve immediate communication, not learning, by augmenting incomplete vocabulary knowledge with constraints from the current context. Together, these studies suggest that evolutionary accounts consider selection pressure arising from both real-time communicative demands and from the need for accurate language development. Copyright © 2016 Cognitive Science Society, Inc.

Effect of Internet-Based Cognitive Apprenticeship Model (i-CAM) on Statistics Learning among Postgraduate Students.

PubMed

Saadati, Farzaneh; Ahmad Tarmizi, Rohani; Mohd Ayub, Ahmad Fauzi; Abu Bakar, Kamariah

2015-01-01

Because students' ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is 'value added' because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students' problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students.

Students' attitudes towards learning statistics

NASA Astrophysics Data System (ADS)

Ghulami, Hassan Rahnaward; Hamid, Mohd Rashid Ab; Zakaria, Roslinazairimah

2015-05-01

Positive attitude towards learning is vital in order to master the core content of the subject matters under study. This is unexceptional in learning statistics course especially at the university level. Therefore, this study investigates the students' attitude towards learning statistics. Six variables or constructs have been identified such as affect, cognitive competence, value, difficulty, interest, and effort. The instrument used for the study is questionnaire that was adopted and adapted from the reliable instrument of Survey of Attitudes towards Statistics(SATS©). This study is conducted to engineering undergraduate students in one of the university in the East Coast of Malaysia. The respondents consist of students who were taking the applied statistics course from different faculties. The results are analysed in terms of descriptive analysis and it contributes to the descriptive understanding of students' attitude towards the teaching and learning process of statistics.

Statistically optimal perception and learning: from behavior to neural representations

PubMed Central

Fiser, József; Berkes, Pietro; Orbán, Gergő; Lengyel, Máté

2010-01-01

Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and reevaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty. PMID:20153683

Evaluating Computer-Based Simulations, Multimedia and Animations that Help Integrate Blended Learning with Lectures in First Year Statistics

ERIC Educational Resources Information Center

Neumann, David L.; Neumann, Michelle M.; Hood, Michelle

2011-01-01

The discipline of statistics seems well suited to the integration of technology in a lecture as a means to enhance student learning and engagement. Technology can be used to simulate statistical concepts, create interactive learning exercises, and illustrate real world applications of statistics. The present study aimed to better understand the…

Attitudes of Medical Graduate and Undergraduate Students toward the Learning and Application of Medical Statistics

ERIC Educational Resources Information Center

Wu, Yazhou; Zhang, Ling; Liu, Ling; Zhang, Yanqi; Liu, Xiaoyu; Yi, Dong

2015-01-01

It is clear that the teaching of medical statistics needs to be improved, yet areas for priority are unclear as medical students' learning and application of statistics at different levels is not well known. Our goal is to assess the attitudes of medical students toward the learning and application of medical statistics, and discover their…

Developing Conceptual Understanding in a Statistics Course: Merrill's First Principles and Real Data at Work

ERIC Educational Resources Information Center

Tu, Wendy; Snyder, Martha M.

2017-01-01

Difficulties in learning statistics primarily at the college-level led to a reform movement in statistics education in the early 1990s. Although much work has been done, effective learning designs that facilitate active learning, conceptual understanding of statistics, and the use of real-data in the classroom are needed. Guided by Merrill's First…

Diagnosis of students' ability in a statistical course based on Rasch probabilistic outcome

NASA Astrophysics Data System (ADS)

Mahmud, Zamalia; Ramli, Wan Syahira Wan; Sapri, Shamsiah; Ahmad, Sanizah

2017-06-01

Measuring students' ability and performance are important in assessing how well students have learned and mastered the statistical courses. Any improvement in learning will depend on the student's approaches to learning, which are relevant to some factors of learning, namely assessment methods carrying out tasks consisting of quizzes, tests, assignment and final examination. This study has attempted an alternative approach to measure students' ability in an undergraduate statistical course based on the Rasch probabilistic model. Firstly, this study aims to explore the learning outcome patterns of students in a statistics course (Applied Probability and Statistics) based on an Entrance-Exit survey. This is followed by investigating students' perceived learning ability based on four Course Learning Outcomes (CLOs) and students' actual learning ability based on their final examination scores. Rasch analysis revealed that students perceived themselves as lacking the ability to understand about 95% of the statistics concepts at the beginning of the class but eventually they had a good understanding at the end of the 14 weeks class. In terms of students' performance in their final examination, their ability in understanding the topics varies at different probability values given the ability of the students and difficulty of the questions. Majority found the probability and counting rules topic to be the most difficult to learn.

Statistical Learning and Language: An Individual Differences Study

ERIC Educational Resources Information Center

Misyak, Jennifer B.; Christiansen, Morten H.

2012-01-01

Although statistical learning and language have been assumed to be intertwined, this theoretical presupposition has rarely been tested empirically. The present study investigates the relationship between statistical learning and language using a within-subject design embedded in an individual-differences framework. Participants were administered…

Statistical Learning of Probabilistic Nonadjacent Dependencies by Multiple-Cue Integration

ERIC Educational Resources Information Center

van den Bos, Esther; Christiansen, Morten H.; Misyak, Jennifer B.

2012-01-01

Previous studies have indicated that dependencies between nonadjacent elements can be acquired by statistical learning when each element predicts only one other element (deterministic dependencies). The present study investigates statistical learning of probabilistic nonadjacent dependencies, in which each element predicts several other elements…

Do statistical segmentation abilities predict lexical-phonological and lexical-semantic abilities in children with and without SLI?

PubMed Central

Mainela-Arnold, Elina; Evans, Julia L.

2014-01-01

This study tested the predictions of the procedural deficit hypothesis by investigating the relationship between sequential statistical learning and two aspects of lexical ability, lexical-phonological and lexical-semantic, in children with and without specific language impairment (SLI). Participants included 40 children (ages 8;5–12;3), 20 children with SLI and 20 with typical development. Children completed Saffran’s statistical word segmentation task, a lexical-phonological access task (gating task), and a word definition task. Poor statistical learners were also poor at managing lexical-phonological competition during the gating task. However, statistical learning was not a significant predictor of semantic richness in word definitions. The ability to track statistical sequential regularities may be important for learning the inherently sequential structure of lexical-phonology, but not as important for learning lexical-semantic knowledge. Consistent with the procedural/declarative memory distinction, the brain networks associated with the two types of lexical learning are likely to have different learning properties. PMID:23425593

Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system.

PubMed

Bal, Mert; Amasyali, M Fatih; Sever, Hayri; Kose, Guven; Demirhan, Ayse

2014-01-01

The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.

Grounding statistical learning in context: The effects of learning and retrieval contexts on cross-situational word learning.

PubMed

Chen, Chi-Hsin; Yu, Chen

2017-06-01

Natural language environments usually provide structured contexts for learning. This study examined the effects of semantically themed contexts-in both learning and retrieval phases-on statistical word learning. Results from 2 experiments consistently showed that participants had higher performance in semantically themed learning contexts. In contrast, themed retrieval contexts did not affect performance. Our work suggests that word learners are sensitive to statistical regularities not just at the level of individual word-object co-occurrences but also at another level containing a whole network of associations among objects and their properties.

Statistical learning and auditory processing in children with music training: An ERP study.

PubMed

Mandikal Vasuki, Pragati Rao; Sharma, Mridula; Ibrahim, Ronny; Arciuli, Joanne

2017-07-01

The question whether musical training is associated with enhanced auditory and cognitive abilities in children is of considerable interest. In the present study, we compared children with music training versus those without music training across a range of auditory and cognitive measures, including the ability to detect implicitly statistical regularities in input (statistical learning). Statistical learning of regularities embedded in auditory and visual stimuli was measured in musically trained and age-matched untrained children between the ages of 9-11years. In addition to collecting behavioural measures, we recorded electrophysiological measures to obtain an online measure of segmentation during the statistical learning tasks. Musically trained children showed better performance on melody discrimination, rhythm discrimination, frequency discrimination, and auditory statistical learning. Furthermore, grand-averaged ERPs showed that triplet onset (initial stimulus) elicited larger responses in the musically trained children during both auditory and visual statistical learning tasks. In addition, children's music skills were associated with performance on auditory and visual behavioural statistical learning tasks. Our data suggests that individual differences in musical skills are associated with children's ability to detect regularities. The ERP data suggest that musical training is associated with better encoding of both auditory and visual stimuli. Although causality must be explored in further research, these results may have implications for developing music-based remediation strategies for children with learning impairments. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Concurrent Movement Impairs Incidental but Not Intentional Statistical Learning

ERIC Educational Resources Information Center

Stevens, David J.; Arciuli, Joanne; Anderson, David I.

2015-01-01

The effect of concurrent movement on incidental versus intentional statistical learning was examined in two experiments. In Experiment 1, participants learned the statistical regularities embedded within familiarization stimuli implicitly, whereas in Experiment 2 they were made aware of the embedded regularities and were instructed explicitly to…

Explorations in Statistics: Correlation

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2010-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This sixth installment of "Explorations in Statistics" explores correlation, a familiar technique that estimates the magnitude of a straight-line relationship between two variables. Correlation is meaningful only when the…

Learning of Grammar-Like Visual Sequences by Adults with and without Language-Learning Disabilities

ERIC Educational Resources Information Center

Aguilar, Jessica M.; Plante, Elena

2014-01-01

Purpose: Two studies examined learning of grammar-like visual sequences to determine whether a general deficit in statistical learning characterizes this population. Furthermore, we tested the hypothesis that difficulty in sustaining attention during the learning task might account for differences in statistical learning. Method: In Study 1,…

Learning Styles Preferences of Statistics Students: A Study in the Faculty of Business and Economics at the UAE University

ERIC Educational Resources Information Center

Yousef, Darwish Abdulrahman

2016-01-01

Purpose: Although there are many studies addressing the learning styles of business students as well as students of other disciplines, there are few studies which address the learning style preferences of statistics students. The purpose of this study is to explore the learning style preferences of statistics students at a United Arab Emirates…

Statistics Anxiety, Trait Anxiety, Learning Behavior, and Academic Performance

ERIC Educational Resources Information Center

Macher, Daniel; Paechter, Manuela; Papousek, Ilona; Ruggeri, Kai

2012-01-01

The present study investigated the relationship between statistics anxiety, individual characteristics (e.g., trait anxiety and learning strategies), and academic performance. Students enrolled in a statistics course in psychology (N = 147) filled in a questionnaire on statistics anxiety, trait anxiety, interest in statistics, mathematical…

Musical Experience Influences Statistical Learning of a Novel Language

PubMed Central

Shook, Anthony; Marian, Viorica; Bartolotti, James; Schroeder, Scott R.

2014-01-01

Musical experience may benefit learning a new language by enhancing the fidelity with which the auditory system encodes sound. In the current study, participants with varying degrees of musical experience were exposed to two statistically-defined languages consisting of auditory Morse-code sequences which varied in difficulty. We found an advantage for highly-skilled musicians, relative to less-skilled musicians, in learning novel Morse-code based words. Furthermore, in the more difficult learning condition, performance of lower-skilled musicians was mediated by their general cognitive abilities. We suggest that musical experience may lead to enhanced processing of statistical information and that musicians’ enhanced ability to learn statistical probabilities in a novel Morse-code language may extend to natural language learning. PMID:23505962

Teaching statistics in biology: using inquiry-based learning to strengthen understanding of statistical analysis in biology laboratory courses.

PubMed

Metz, Anneke M

2008-01-01

There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly at the lower-division level. Elements of statistics were incorporated into an introductory biology course, including a review of statistics concepts and opportunity for students to perform statistical analysis in a biological context. Learning gains were measured with an 11-item statistics learning survey instrument developed for the course. Students showed a statistically significant 25% (p < 0.005) increase in statistics knowledge after completing introductory biology. Students improved their scores on the survey after completing introductory biology, even if they had previously completed an introductory statistics course (9%, improvement p < 0.005). Students retested 1 yr after completing introductory biology showed no loss of their statistics knowledge as measured by this instrument, suggesting that the use of statistics in biology course work may aid long-term retention of statistics knowledge. No statistically significant differences in learning were detected between male and female students in the study.

Explorations in Statistics: Power

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2010-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fifth installment of "Explorations in Statistics" revisits power, a concept fundamental to the test of a null hypothesis. Power is the probability that we reject the null hypothesis when it is false. Four…

Explorations in Statistics: Confidence Intervals

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This third installment of "Explorations in Statistics" investigates confidence intervals. A confidence interval is a range that we expect, with some level of confidence, to include the true value of a population parameter…

Explorations in Statistics: The Analysis of Change

ERIC Educational Resources Information Center

Curran-Everett, Douglas; Williams, Calvin L.

2015-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This tenth installment of "Explorations in Statistics" explores the analysis of a potential change in some physiological response. As researchers, we often express absolute change as percent change so we can…

APA's Learning Objectives for Research Methods and Statistics in Practice: A Multimethod Analysis

ERIC Educational Resources Information Center

Tomcho, Thomas J.; Rice, Diana; Foels, Rob; Folmsbee, Leah; Vladescu, Jason; Lissman, Rachel; Matulewicz, Ryan; Bopp, Kara

2009-01-01

Research methods and statistics courses constitute a core undergraduate psychology requirement. We analyzed course syllabi and faculty self-reported coverage of both research methods and statistics course learning objectives to assess the concordance with APA's learning objectives (American Psychological Association, 2007). We obtained a sample of…

Explorations in Statistics: Permutation Methods

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2012-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This eighth installment of "Explorations in Statistics" explores permutation methods, empiric procedures we can use to assess an experimental result--to test a null hypothesis--when we are reluctant to trust statistical…

Infant Directed Speech Enhances Statistical Learning in Newborn Infants: An ERP Study

PubMed Central

Teinonen, Tuomas; Tervaniemi, Mari; Huotilainen, Minna

2016-01-01

Statistical learning and the social contexts of language addressed to infants are hypothesized to play important roles in early language development. Previous behavioral work has found that the exaggerated prosodic contours of infant-directed speech (IDS) facilitate statistical learning in 8-month-old infants. Here we examined the neural processes involved in on-line statistical learning and investigated whether the use of IDS facilitates statistical learning in sleeping newborns. Event-related potentials (ERPs) were recorded while newborns were exposed to12 pseudo-words, six spoken with exaggerated pitch contours of IDS and six spoken without exaggerated pitch contours (ADS) in ten alternating blocks. We examined whether ERP amplitudes for syllable position within a pseudo-word (word-initial vs. word-medial vs. word-final, indicating statistical word learning) and speech register (ADS vs. IDS) would interact. The ADS and IDS registers elicited similar ERP patterns for syllable position in an early 0–100 ms component but elicited different ERP effects in both the polarity and topographical distribution at 200–400 ms and 450–650 ms. These results provide the first evidence that the exaggerated pitch contours of IDS result in differences in brain activity linked to on-line statistical learning in sleeping newborns. PMID:27617967

A neuroconstructivist model of past tense development and processing.

PubMed

Westermann, Gert; Ruh, Nicolas

2012-07-01

We present a neural network model of learning and processing the English past tense that is based on the notion that experience-dependent cortical development is a core aspect of cognitive development. During learning the model adds and removes units and connections to develop a task-specific final architecture. The model provides an integrated account of characteristic errors during learning the past tense, adult generalization to pseudoverbs, and dissociations between verbs observed after brain damage in aphasic patients. We put forward a theory of verb inflection in which a functional processing architecture develops through interactions between experience-dependent brain development and the structure of the environment, in this case, the statistical properties of verbs in the language. The outcome of this process is a structured processing system giving rise to graded dissociations between verbs that are easy and verbs that are hard to learn and process. In contrast to dual-mechanism accounts of inflection, we argue that describing dissociations as a dichotomy between regular and irregular verbs is a post hoc abstraction and is not linked to underlying processing mechanisms. We extend current single-mechanism accounts of inflection by highlighting the role of structural adaptation in development and in the formation of the adult processing system. In contrast to some single-mechanism accounts, we argue that the link between irregular inflection and verb semantics is not causal and that existing data can be explained on the basis of phonological representations alone. This work highlights the benefit of taking brain development seriously in theories of cognitive development. Copyright 2012 APA, all rights reserved.

Machine learning bandgaps of double perovskites

PubMed Central

Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; Ramprasad, R.; Gubernatis, J. E.; Lookman, T.

2016-01-01

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance. PMID:26783247

Effect of Internet-Based Cognitive Apprenticeship Model (i-CAM) on Statistics Learning among Postgraduate Students

PubMed Central

Saadati, Farzaneh; Ahmad Tarmizi, Rohani

2015-01-01

Because students’ ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is ‘value added’ because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students’ problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students. PMID:26132553

Methods of comparing associative models and an application to retrospective revaluation.

PubMed

Witnauer, James E; Hutchings, Ryan; Miller, Ralph R

2017-11-01

Contemporary theories of associative learning are increasingly complex, which necessitates the use of computational methods to reveal predictions of these models. We argue that comparisons across multiple models in terms of goodness of fit to empirical data from experiments often reveal more about the actual mechanisms of learning and behavior than do simulations of only a single model. Such comparisons are best made when the values of free parameters are discovered through some optimization procedure based on the specific data being fit (e.g., hill climbing), so that the comparisons hinge on the psychological mechanisms assumed by each model rather than being biased by using parameters that differ in quality across models with respect to the data being fit. Statistics like the Bayesian information criterion facilitate comparisons among models that have different numbers of free parameters. These issues are examined using retrospective revaluation data. Copyright © 2017 Elsevier B.V. All rights reserved.

Empirical Models of Social Learning in a Large, Evolving Network.

PubMed

Bener, Ayşe Başar; Çağlayan, Bora; Henry, Adam Douglas; Prałat, Paweł

2016-01-01

This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals' access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.

Empirical Models of Social Learning in a Large, Evolving Network

PubMed Central

Bener, Ayşe Başar; Çağlayan, Bora; Henry, Adam Douglas; Prałat, Paweł

2016-01-01

This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals’ access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends. PMID:27701430

Action video game play facilitates the development of better perceptual templates.

PubMed

Bejjanki, Vikranth R; Zhang, Ruyuan; Li, Renjie; Pouget, Alexandre; Green, C Shawn; Lu, Zhong-Lin; Bavelier, Daphne

2014-11-25

The field of perceptual learning has identified changes in perceptual templates as a powerful mechanism mediating the learning of statistical regularities in our environment. By measuring threshold-vs.-contrast curves using an orientation identification task under varying levels of external noise, the perceptual template model (PTM) allows one to disentangle various sources of signal-to-noise changes that can alter performance. We use the PTM approach to elucidate the mechanism that underlies the wide range of improvements noted after action video game play. We show that action video game players make use of improved perceptual templates compared with nonvideo game players, and we confirm a causal role for action video game play in inducing such improvements through a 50-h training study. Then, by adapting a recent neural model to this task, we demonstrate how such improved perceptual templates can arise from reweighting the connectivity between visual areas. Finally, we establish that action gamers do not enter the perceptual task with improved perceptual templates. Instead, although performance in action gamers is initially indistinguishable from that of nongamers, action gamers more rapidly learn the proper template as they experience the task. Taken together, our results establish for the first time to our knowledge the development of enhanced perceptual templates following action game play. Because such an improvement can facilitate the inference of the proper generative model for the task at hand, unlike perceptual learning that is quite specific, it thus elucidates a general learning mechanism that can account for the various behavioral benefits noted after action game play.

Action video game play facilitates the development of better perceptual templates

PubMed Central

Bejjanki, Vikranth R.; Zhang, Ruyuan; Li, Renjie; Pouget, Alexandre; Green, C. Shawn; Lu, Zhong-Lin; Bavelier, Daphne

2014-01-01

The field of perceptual learning has identified changes in perceptual templates as a powerful mechanism mediating the learning of statistical regularities in our environment. By measuring threshold-vs.-contrast curves using an orientation identification task under varying levels of external noise, the perceptual template model (PTM) allows one to disentangle various sources of signal-to-noise changes that can alter performance. We use the PTM approach to elucidate the mechanism that underlies the wide range of improvements noted after action video game play. We show that action video game players make use of improved perceptual templates compared with nonvideo game players, and we confirm a causal role for action video game play in inducing such improvements through a 50-h training study. Then, by adapting a recent neural model to this task, we demonstrate how such improved perceptual templates can arise from reweighting the connectivity between visual areas. Finally, we establish that action gamers do not enter the perceptual task with improved perceptual templates. Instead, although performance in action gamers is initially indistinguishable from that of nongamers, action gamers more rapidly learn the proper template as they experience the task. Taken together, our results establish for the first time to our knowledge the development of enhanced perceptual templates following action game play. Because such an improvement can facilitate the inference of the proper generative model for the task at hand, unlike perceptual learning that is quite specific, it thus elucidates a general learning mechanism that can account for the various behavioral benefits noted after action game play. PMID:25385590

Network Supervision of Adult Experience and Learning Dependent Sensory Cortical Plasticity.

PubMed

Blake, David T

2017-06-18

The brain is capable of remodeling throughout life. The sensory cortices provide a useful preparation for studying neuroplasticity both during development and thereafter. In adulthood, sensory cortices change in the cortical area activated by behaviorally relevant stimuli, by the strength of response within that activated area, and by the temporal profiles of those responses. Evidence supports forms of unsupervised, reinforcement, and fully supervised network learning rules. Studies on experience-dependent plasticity have mostly not controlled for learning, and they find support for unsupervised learning mechanisms. Changes occur with greatest ease in neurons containing α-CamKII, which are pyramidal neurons in layers II/III and layers V/VI. These changes use synaptic mechanisms including long term depression. Synaptic strengthening at NMDA-containing synapses does occur, but its weak association with activity suggests other factors also initiate changes. Studies that control learning find support of reinforcement learning rules and limited evidence of other forms of supervised learning. Behaviorally associating a stimulus with reinforcement leads to a strengthening of cortical response strength and enlarging of response area with poor selectivity. Associating a stimulus with omission of reinforcement leads to a selective weakening of responses. In some preparations in which these associations are not as clearly made, neurons with the most informative discharges are relatively stronger after training. Studies analyzing the temporal profile of responses associated with omission of reward, or of plasticity in studies with different discriminanda but statistically matched stimuli, support the existence of limited supervised network learning. © 2017 American Physiological Society. Compr Physiol 7:977-1008, 2017. Copyright © 2017 John Wiley & Sons, Inc.

Learning to associate auditory and visual stimuli: behavioral and neural mechanisms.

PubMed

Altieri, Nicholas; Stevenson, Ryan A; Wallace, Mark T; Wenger, Michael J

2015-05-01

The ability to effectively combine sensory inputs across modalities is vital for acquiring a unified percept of events. For example, watching a hammer hit a nail while simultaneously identifying the sound as originating from the event requires the ability to identify spatio-temporal congruencies and statistical regularities. In this study, we applied a reaction time and hazard function measure known as capacity (e.g., Townsend and AshbyCognitive Theory 200-239, 1978) to quantify the extent to which observers learn paired associations between simple auditory and visual patterns in a model theoretic manner. As expected, results showed that learning was associated with an increase in accuracy, but more significantly, an increase in capacity. The aim of this study was to associate capacity measures of multisensory learning, with neural based measures, namely mean global field power (GFP). We observed a co-variation between an increase in capacity, and a decrease in GFP amplitude as learning occurred. This suggests that capacity constitutes a reliable behavioral index of efficient energy expenditure in the neural domain.

Explorations in Statistics: The Analysis of Ratios and Normalized Data

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2013-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This ninth installment of "Explorations in Statistics" explores the analysis of ratios and normalized--or standardized--data. As researchers, we compute a ratio--a numerator divided by a denominator--to compute a…

"Dear Fresher …"--How Online Questionnaires Can Improve Learning and Teaching Statistics

ERIC Educational Resources Information Center

Bebermeier, Sarah; Nussbeck, Fridtjof W.; Ontrup, Greta

2015-01-01

Lecturers teaching statistics are faced with several challenges supporting students' learning in appropriate ways. A variety of methods and tools exist to facilitate students' learning on statistics courses. The online questionnaires presented in this report are a new, slightly different computer-based tool: the central aim was to support students…

A Constructivist Approach in a Blended E-Learning Environment for Statistics

ERIC Educational Resources Information Center

Poelmans, Stephan; Wessa, Patrick

2015-01-01

In this study, we report on the students' evaluation of a self-constructed constructivist e-learning environment for statistics, the compendium platform (CP). The system was built to endorse deeper learning with the incorporation of statistical reproducibility and peer review practices. The deployment of the CP, with interactive workshops and…

Statistical Learning Effects in Musicians and Non-Musicians: An MEG Study

ERIC Educational Resources Information Center

Paraskevopoulos, Evangelos; Kuchenbuch, Anja; Herholz, Sibylle C.; Pantev, Christo

2012-01-01

This study aimed to assess the effect of musical training in statistical learning of tone sequences using Magnetoencephalography (MEG). Specifically, MEG recordings were used to investigate the neural and functional correlates of the pre-attentive ability for detection of deviance, from a statistically learned tone sequence. The effect of…

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

Physical fitness modulates incidental but not intentional statistical learning of simultaneous auditory sequences during concurrent physical exercise.

PubMed

Daikoku, Tatsuya; Takahashi, Yuji; Futagami, Hiroko; Tarumoto, Nagayoshi; Yasuda, Hideki

2017-02-01

In real-world auditory environments, humans are exposed to overlapping auditory information such as those made by human voices and musical instruments even during routine physical activities such as walking and cycling. The present study investigated how concurrent physical exercise affects performance of incidental and intentional learning of overlapping auditory streams, and whether physical fitness modulates the performances of learning. Participants were grouped with 11 participants with lower and higher fitness each, based on their Vo 2 max value. They were presented simultaneous auditory sequences with a distinct statistical regularity each other (i.e. statistical learning), while they were pedaling on the bike and seating on a bike at rest. In experiment 1, they were instructed to attend to one of the two sequences and ignore to the other sequence. In experiment 2, they were instructed to attend to both of the two sequences. After exposure to the sequences, learning effects were evaluated by familiarity test. In the experiment 1, performance of statistical learning of ignored sequences during concurrent pedaling could be higher in the participants with high than low physical fitness, whereas in attended sequence, there was no significant difference in performance of statistical learning between high than low physical fitness. Furthermore, there was no significant effect of physical fitness on learning while resting. In the experiment 2, the both participants with high and low physical fitness could perform intentional statistical learning of two simultaneous sequences in the both exercise and rest sessions. The improvement in physical fitness might facilitate incidental but not intentional statistical learning of simultaneous auditory sequences during concurrent physical exercise.

Teaching Statistics in Biology: Using Inquiry-based Learning to Strengthen Understanding of Statistical Analysis in Biology Laboratory Courses

PubMed Central

2008-01-01

There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly at the lower-division level. Elements of statistics were incorporated into an introductory biology course, including a review of statistics concepts and opportunity for students to perform statistical analysis in a biological context. Learning gains were measured with an 11-item statistics learning survey instrument developed for the course. Students showed a statistically significant 25% (p < 0.005) increase in statistics knowledge after completing introductory biology. Students improved their scores on the survey after completing introductory biology, even if they had previously completed an introductory statistics course (9%, improvement p < 0.005). Students retested 1 yr after completing introductory biology showed no loss of their statistics knowledge as measured by this instrument, suggesting that the use of statistics in biology course work may aid long-term retention of statistics knowledge. No statistically significant differences in learning were detected between male and female students in the study. PMID:18765754

Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience

PubMed Central

Galatzer-Levy, Isaac R.; Ruggles, Kelly; Chen, Zhe

2017-01-01

Diverse environmental and biological systems interact to influence individual differences in response to environmental stress. Understanding the nature of these complex relationships can enhance the development of methods to: (1) identify risk, (2) classify individuals as healthy or ill, (3) understand mechanisms of change, and (4) develop effective treatments. The Research Domain Criteria (RDoC) initiative provides a theoretical framework to understand health and illness as the product of multiple inter-related systems but does not provide a framework to characterize or statistically evaluate such complex relationships. Characterizing and statistically evaluating models that integrate multiple levels (e.g. synapses, genes, environmental factors) as they relate to outcomes that a free from prior diagnostic benchmarks represents a challenge requiring new computational tools that are capable to capture complex relationships and identify clinically relevant populations. In the current review, we will summarize machine learning methods that can achieve these goals. PMID:29527592

The words children hear: Picture books and the statistics for language learning

PubMed Central

Montag, Jessica L.; Jones, Michael N.; Smith, Linda B.

2015-01-01

Young children learn language from the speech they hear. Previous work suggests that the statistical diversity of words and of linguistic contexts is associated with better language outcomes. One potential source of lexical diversity is the text of picture books that caregivers read aloud to children. Many parents begin reading to their children shortly after birth, so this is potentially an important source of linguistic input for many children. We constructed a corpus of 100 children’s picture books and compared word type and token counts to a matched sample of child-directed speech. Overall, the picture books contained more unique word types than the child-directed speech. Further, individual picture books generally contained more unique word types than length-matched, child-directed conversations. The text of picture books may be an important source of vocabulary for young children, and these findings suggest a mechanism that underlies the language benefits associated with reading to children. PMID:26243292

The Words Children Hear: Picture Books and the Statistics for Language Learning.

PubMed

Montag, Jessica L; Jones, Michael N; Smith, Linda B

2015-09-01

Young children learn language from the speech they hear. Previous work suggests that greater statistical diversity of words and of linguistic contexts is associated with better language outcomes. One potential source of lexical diversity is the text of picture books that caregivers read aloud to children. Many parents begin reading to their children shortly after birth, so this is potentially an important source of linguistic input for many children. We constructed a corpus of 100 children's picture books and compared word type and token counts in that sample and a matched sample of child-directed speech. Overall, the picture books contained more unique word types than the child-directed speech. Further, individual picture books generally contained more unique word types than length-matched, child-directed conversations. The text of picture books may be an important source of vocabulary for young children, and these findings suggest a mechanism that underlies the language benefits associated with reading to children. © The Author(s) 2015.

Redefining "Learning" in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

PubMed

Siegelman, Noam; Bogaerts, Louisa; Kronenfeld, Ofer; Frost, Ram

2017-10-07

From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has been monitoring participants' performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL. © 2017 Cognitive Science Society, Inc.

Explorations in Statistics: Standard Deviations and Standard Errors

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2008-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This series in "Advances in Physiology Education" provides an opportunity to do just that: we will investigate basic concepts in statistics using the free software package R. Because this series uses R solely as a vehicle…

Educational Statistics Authentic Learning CAPSULES: Community Action Projects for Students Utilizing Leadership and E-Based Statistics

ERIC Educational Resources Information Center

Thompson, Carla J.

2009-01-01

Since educational statistics is a core or general requirement of all students enrolled in graduate education programs, the need for high quality student engagement and appropriate authentic learning experiences is critical for promoting student interest and student success in the course. Based in authentic learning theory and engagement theory…

Trans-species learning of cellular signaling systems with bimodal deep belief networks.

PubMed

Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

2015-09-15

Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These 'deep learning' models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. xinghua@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Under the hood of statistical learning: A statistical MMN reflects the magnitude of transitional probabilities in auditory sequences.

PubMed

Koelsch, Stefan; Busch, Tobias; Jentschke, Sebastian; Rohrmeier, Martin

2016-02-02

Within the framework of statistical learning, many behavioural studies investigated the processing of unpredicted events. However, surprisingly few neurophysiological studies are available on this topic, and no statistical learning experiment has investigated electroencephalographic (EEG) correlates of processing events with different transition probabilities. We carried out an EEG study with a novel variant of the established statistical learning paradigm. Timbres were presented in isochronous sequences of triplets. The first two sounds of all triplets were equiprobable, while the third sound occurred with either low (10%), intermediate (30%), or high (60%) probability. Thus, the occurrence probability of the third item of each triplet (given the first two items) was varied. Compared to high-probability triplet endings, endings with low and intermediate probability elicited an early anterior negativity that had an onset around 100 ms and was maximal at around 180 ms. This effect was larger for events with low than for events with intermediate probability. Our results reveal that, when predictions are based on statistical learning, events that do not match a prediction evoke an early anterior negativity, with the amplitude of this mismatch response being inversely related to the probability of such events. Thus, we report a statistical mismatch negativity (sMMN) that reflects statistical learning of transitional probability distributions that go beyond auditory sensory memory capabilities.

11.2 YIP Human In the Loop Statistical RelationalLearners

DTIC Science & Technology

2017-10-23

learning formalisms including inverse reinforcement learning [4] and statistical relational learning [7, 5, 8]. We have also applied our algorithms in...one introduced for label preferences. 4 Figure 2: Active Advice Seeking for Inverse Reinforcement Learning. active advice seeking is in selecting the...learning tasks. 1.2.1 Sequential Decision-Making Our previous work on advice for inverse reinforcement learning (IRL) defined advice as action

The Effects of Cooperative Learning and Feedback on E-Learning in Statistics

ERIC Educational Resources Information Center

Krause, Ulrike-Marie; Stark, Robin; Mandl, Heinz

2009-01-01

This study examined whether cooperative learning and feedback facilitate situated, example-based e-learning in the field of statistics. The factors "social context" (individual vs. cooperative) and "feedback intervention" (available vs. not available) were varied; participants were 137 university students. Results showed that…

A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback.

PubMed

Rahman, Md Mahmudur; Bhattacharya, Prabir; Desai, Bipin C

2007-01-01

A content-based image retrieval (CBIR) framework for diverse collection of medical images of different imaging modalities, anatomic regions with different orientations and biological systems is proposed. Organization of images in such a database (DB) is well defined with predefined semantic categories; hence, it can be useful for category-specific searching. The proposed framework consists of machine learning methods for image prefiltering, similarity matching using statistical distance measures, and a relevance feedback (RF) scheme. To narrow down the semantic gap and increase the retrieval efficiency, we investigate both supervised and unsupervised learning techniques to associate low-level global image features (e.g., color, texture, and edge) in the projected PCA-based eigenspace with their high-level semantic and visual categories. Specially, we explore the use of a probabilistic multiclass support vector machine (SVM) and fuzzy c-mean (FCM) clustering for categorization and prefiltering of images to reduce the search space. A category-specific statistical similarity matching is proposed in a finer level on the prefiltered images. To incorporate a better perception subjectivity, an RF mechanism is also added to update the query parameters dynamically and adjust the proposed matching functions. Experiments are based on a ground-truth DB consisting of 5000 diverse medical images of 20 predefined categories. Analysis of results based on cross-validation (CV) accuracy and precision-recall for image categorization and retrieval is reported. It demonstrates the improvement, effectiveness, and efficiency achieved by the proposed framework.

Using Guided Reinvention to Develop Teachers' Understanding of Hypothesis Testing Concepts

ERIC Educational Resources Information Center

Dolor, Jason; Noll, Jennifer

2015-01-01

Statistics education reform efforts emphasize the importance of informal inference in the learning of statistics. Research suggests statistics teachers experience similar difficulties understanding statistical inference concepts as students and how teacher knowledge can impact student learning. This study investigates how teachers reinvented an…

Statistical learning in social action contexts.

PubMed

Monroy, Claire; Meyer, Marlene; Gerson, Sarah; Hunnius, Sabine

2017-01-01

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and-if so-whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together ('Joint' condition) or stated the intention to act alone ('Parallel' condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor's action reliably predicted the second actor's action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects.

Statistical learning in social action contexts

PubMed Central

Meyer, Marlene; Gerson, Sarah; Hunnius, Sabine

2017-01-01

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and—if so—whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together (‘Joint’ condition) or stated the intention to act alone (‘Parallel’ condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor’s action reliably predicted the second actor’s action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects. PMID:28475619

Infants' statistical learning: 2- and 5-month-olds' segmentation of continuous visual sequences.

PubMed

Slone, Lauren Krogh; Johnson, Scott P

2015-05-01

Past research suggests that infants have powerful statistical learning abilities; however, studies of infants' visual statistical learning offer differing accounts of the developmental trajectory of and constraints on this learning. To elucidate this issue, the current study tested the hypothesis that young infants' segmentation of visual sequences depends on redundant statistical cues to segmentation. A sample of 20 2-month-olds and 20 5-month-olds observed a continuous sequence of looming shapes in which unit boundaries were defined by both transitional probability and co-occurrence frequency. Following habituation, only 5-month-olds showed evidence of statistically segmenting the sequence, looking longer to a statistically improbable shape pair than to a probable pair. These results reaffirm the power of statistical learning in infants as young as 5 months but also suggest considerable development of statistical segmentation ability between 2 and 5 months of age. Moreover, the results do not support the idea that infants' ability to segment visual sequences based on transitional probabilities and/or co-occurrence frequencies is functional at the onset of visual experience, as has been suggested previously. Rather, this type of statistical segmentation appears to be constrained by the developmental state of the learner. Factors contributing to the development of statistical segmentation ability during early infancy, including memory and attention, are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Correlation between musical aptitude and learning foreign languages: an epidemiological study in secondary school Italian students

PubMed Central

PICCIOTTI, P.M.; BUSSU, F.; CALò, L.; GALLUS, R.; SCARANO, E.; DI CINTIO, G.; CASSARÀ, F.; D’ALATRI, L.

2018-01-01

SUMMARY The aim of this study was to assess if a correlation exists between language learning skills and musical aptitude through the analysis of scholarly outcomes concerning the study of foreign languages and music. We enrolled 502 students from a secondary Italian school (10-14 years old), attending both traditional courses (2 hours/week of music classes scheduled) and special courses (six hours). For statistical analysis, we considered grades in English, French and Music. Our results showed a significant correlation between grades in the two foreign languages and in music, both in the traditional courses and in special courses, and better results in French than for special courses. These results are discussed and interpreted through the literature about neuroanatomical and physiological mechanisms of foreign language learning and music perception. PMID:29756615

Machine learning bandgaps of double perovskites

DOE PAGES

Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; ...

2016-01-19

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the mostmore » crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.« less

Studying Student Benefits of Assigning a Service-Learning Project Compared to a Traditional Final Project in a Business Statistics Class

ERIC Educational Resources Information Center

Phelps, Amy L.; Dostilio, Lina

2008-01-01

The present study addresses the efficacy of using service-learning methods to meet the GAISE guidelines (http://www.amstat.org/education/gaise/GAISECollege.htm) in a second business statistics course and further explores potential advantages of assigning a service-learning (SL) project as compared to the traditional statistics project assignment.…

More Limitations to Monolingualism: Bilinguals Outperform Monolinguals in Implicit Word Learning.

PubMed

Escudero, Paola; Mulak, Karen E; Fu, Charlene S L; Singh, Leher

2016-01-01

To succeed at cross-situational word learning, learners must infer word-object mappings by attending to the statistical co-occurrences of novel objects and labels across multiple encounters. While past studies have investigated this as a learning mechanism for infants and monolingual adults, bilinguals' cross-situational word learning abilities have yet to be tested. Here, we compared monolinguals' and bilinguals' performance on a cross-situational word learning paradigm that featured phonologically distinct word pairs (e.g., BON-DEET) and phonologically similar word pairs that varied by a single consonant or vowel segment (e.g., BON-TON, DEET-DIT, respectively). Both groups learned the novel word-referent mappings, providing evidence that cross-situational word learning is a learning strategy also available to bilingual adults. Furthermore, bilinguals were overall more accurate than monolinguals. This supports that bilingualism fosters a wide range of cognitive advantages that may benefit implicit word learning. Additionally, response patterns to the different trial types revealed a relative difficulty for vowel minimal pairs than consonant minimal pairs, replicating the pattern found in monolinguals by Escudero et al. (2016) in a different English accent. Specifically, all participants failed to learn vowel contrasts differentiated by vowel height. We discuss evidence for this bilingual advantage as a language-specific or general advantage.

More Limitations to Monolingualism: Bilinguals Outperform Monolinguals in Implicit Word Learning

PubMed Central

Escudero, Paola; Mulak, Karen E.; Fu, Charlene S. L.; Singh, Leher

2016-01-01

To succeed at cross-situational word learning, learners must infer word-object mappings by attending to the statistical co-occurrences of novel objects and labels across multiple encounters. While past studies have investigated this as a learning mechanism for infants and monolingual adults, bilinguals’ cross-situational word learning abilities have yet to be tested. Here, we compared monolinguals’ and bilinguals’ performance on a cross-situational word learning paradigm that featured phonologically distinct word pairs (e.g., BON-DEET) and phonologically similar word pairs that varied by a single consonant or vowel segment (e.g., BON-TON, DEET-DIT, respectively). Both groups learned the novel word-referent mappings, providing evidence that cross-situational word learning is a learning strategy also available to bilingual adults. Furthermore, bilinguals were overall more accurate than monolinguals. This supports that bilingualism fosters a wide range of cognitive advantages that may benefit implicit word learning. Additionally, response patterns to the different trial types revealed a relative difficulty for vowel minimal pairs than consonant minimal pairs, replicating the pattern found in monolinguals by Escudero et al. (2016) in a different English accent. Specifically, all participants failed to learn vowel contrasts differentiated by vowel height. We discuss evidence for this bilingual advantage as a language-specific or general advantage. PMID:27574513

Entropy, a Unifying Concept: from Physics to Cognitive Psychology

NASA Astrophysics Data System (ADS)

Tsallis, Constantino; Tsallis, Alexandra C.

Together with classical, relativistic and quantum mechanics, as well as Maxwell electromagnetism, Boltzmann-Gibbs (BG) statistical mechanics constitutes one of the main theories of contemporary physics. This theory primarily concerns inanimate matter, and at its generic foundation we find nonlinear dynamical systems satisfying the ergodic hypothesis. This hypothesis is typically guaranteed for systems whose maximal Lyapunov exponent is positive. What happens when this crucial quantity is zero instead? We suggest here that, in what concerns thermostatistical properties, we typically enter what in some sense may be considered as a new world — the world of living systems — . The need emerges, at least for many systems, for generalizing the basis of BG statistical mechanics, namely the Boltzmann-Gibbs-von Neumann-Shannon en-tropic functional form, which connects the oscopic, thermodynamic quantity, with the occurrence probabilities of microscopic configurations. This unifying approach is briefly reviewed here, and its widespread applications — from physics to cognitive psychology — are overviewed. Special attention is dedicated to the learning/memorizing process in humans and computers. The present observations might be related to the gestalt theory of visual perceptions and the actor-network theory.

Some Variables in Relation to Students' Anxiety in Learning Statistics.

ERIC Educational Resources Information Center

Sutarso, Toto

The purpose of this study was to investigate some variables that relate to students' anxiety in learning statistics. The variables included sex, class level, students' achievement, school, mathematical background, previous statistics courses, and race. The instrument used was the 24-item Students' Attitudes Toward Statistics (STATS), which was…

Functional differences between statistical learning with and without explicit training

PubMed Central

Reber, Paul J.; Paller, Ken A.

2015-01-01

Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and prepare for incoming input. In this study, we ask whether the function of statistical learning may be enhanced through supplementary explicit training, in which underlying regularities are explicitly taught rather than simply abstracted through exposure. Learners were randomly assigned either to an explicit group or an implicit group. All learners were exposed to a continuous stream of repeating nonsense words. Prior to this implicit training, learners in the explicit group received supplementary explicit training on the nonsense words. Statistical learning was assessed through a speeded reaction-time (RT) task, which measured the extent to which learners used acquired statistical knowledge to optimize online processing. Both RTs and brain potentials revealed significant differences in online processing as a function of training condition. RTs showed a crossover interaction; responses in the explicit group were faster to predictable targets and marginally slower to less predictable targets relative to responses in the implicit group. P300 potentials to predictable targets were larger in the explicit group than in the implicit group, suggesting greater recruitment of controlled, effortful processes. Taken together, these results suggest that information abstracted through passive exposure during statistical learning may be processed more automatically and with less effort than information that is acquired explicitly. PMID:26472644

Determining significant material properties: A discovery approach

NASA Technical Reports Server (NTRS)

Karplus, Alan K.

1992-01-01

The following is a laboratory experiment designed to further understanding of materials science. The experiment itself can be informative for persons of any age past elementary school, and even for some in elementary school. The preparation of the plastic samples is readily accomplished by persons with resonable dexterity in the cutting of paper designs. The completion of the statistical Design of Experiments, which uses Yates' Method, requires basic math (addition and subtraction). Interpretive work requires plotting of data and making observations. Knowledge of statistical methods would be helpful. The purpose of this experiment is to acquaint students with the seven classes of recyclable plastics, and provide hands-on learning about the response of these plastics to mechanical tensile loading.

"If You're Doubting Yourself Then, What's the Fun in That?" An Exploration of Why Prospective Secondary Mathematics Teachers Perceive Statistics as Difficult

ERIC Educational Resources Information Center

Leavy, Aisling M.; Hannigan, Ailish; Fitzmaurice, Olivia

2013-01-01

Most research into prospective secondary mathematics teachers' attitudes towards statistics indicates generally positive attitudes but a perception that statistics is difficult to learn. These perceptions of statistics as a difficult subject to learn may impact the approaches of prospective teachers to teaching statistics and in turn their…

Statistical learning in reading: variability in irrelevant letters helps children learn phonics skills.

PubMed

Apfelbaum, Keith S; Hazeltine, Eliot; McMurray, Bob

2013-07-01

Early reading abilities are widely considered to derive in part from statistical learning of regularities between letters and sounds. Although there is substantial evidence from laboratory work to support this, how it occurs in the classroom setting has not been extensively explored; there are few investigations of how statistics among letters and sounds influence how children actually learn to read or what principles of statistical learning may improve learning. We examined 2 conflicting principles that may apply to learning grapheme-phoneme-correspondence (GPC) regularities for vowels: (a) variability in irrelevant units may help children derive invariant relationships and (b) similarity between words may force children to use a deeper analysis of lexical structure. We trained 224 first-grade students on a small set of GPC regularities for vowels, embedded in words with either high or low consonant similarity, and tested their generalization to novel tasks and words. Variability offered a consistent benefit over similarity for trained and new words in both trained and new tasks.

Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

NASA Astrophysics Data System (ADS)

Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

2017-12-01

Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network

PubMed Central

Del Papa, Bruno; Priesemann, Viola

2017-01-01

Many experiments have suggested that the brain operates close to a critical state, based on signatures of criticality such as power-law distributed neuronal avalanches. In neural network models, criticality is a dynamical state that maximizes information processing capacities, e.g. sensitivity to input, dynamical range and storage capacity, which makes it a favorable candidate state for brain function. Although models that self-organize towards a critical state have been proposed, the relation between criticality signatures and learning is still unclear. Here, we investigate signatures of criticality in a self-organizing recurrent neural network (SORN). Investigating criticality in the SORN is of particular interest because it has not been developed to show criticality. Instead, the SORN has been shown to exhibit spatio-temporal pattern learning through a combination of neural plasticity mechanisms and it reproduces a number of biological findings on neural variability and the statistics and fluctuations of synaptic efficacies. We show that, after a transient, the SORN spontaneously self-organizes into a dynamical state that shows criticality signatures comparable to those found in experiments. The plasticity mechanisms are necessary to attain that dynamical state, but not to maintain it. Furthermore, onset of external input transiently changes the slope of the avalanche distributions – matching recent experimental findings. Interestingly, the membrane noise level necessary for the occurrence of the criticality signatures reduces the model’s performance in simple learning tasks. Overall, our work shows that the biologically inspired plasticity and homeostasis mechanisms responsible for the SORN’s spatio-temporal learning abilities can give rise to criticality signatures in its activity when driven by random input, but these break down under the structured input of short repeating sequences. PMID:28552964

A Modified Moore Approach to Teaching Mathematical Statistics: An Inquiry Based Learning Technique to Teaching Mathematical Statistics

ERIC Educational Resources Information Center

McLoughlin, M. Padraig M. M.

2008-01-01

The author of this paper submits the thesis that learning requires doing; only through inquiry is learning achieved, and hence this paper proposes a programme of use of a modified Moore method in a Probability and Mathematical Statistics (PAMS) course sequence to teach students PAMS. Furthermore, the author of this paper opines that set theory…

The Role of Statistical Learning and Working Memory in L2 Speakers' Pattern Learning

ERIC Educational Resources Information Center

McDonough, Kim; Trofimovich, Pavel

2016-01-01

This study investigated whether second language (L2) speakers' morphosyntactic pattern learning was predicted by their statistical learning and working memory abilities. Across three experiments, Thai English as a Foreign Language (EFL) university students (N = 140) were exposed to either the transitive construction in Esperanto (e.g., "tauro…

Enhancing interest in statistics among computer science students using computer tool entrepreneur role play

NASA Astrophysics Data System (ADS)

Judi, Hairulliza Mohamad; Sahari @ Ashari, Noraidah; Eksan, Zanaton Hj

2017-04-01

Previous research in Malaysia indicates that there is a problem regarding attitude towards statistics among students. They didn't show positive attitude in affective, cognitive, capability, value, interest and effort aspects although did well in difficulty. This issue should be given substantial attention because students' attitude towards statistics may give impacts on the teaching and learning process of the subject. Teaching statistics using role play is an appropriate attempt to improve attitudes to statistics, to enhance the learning of statistical techniques and statistical thinking, and to increase generic skills. The objectives of the paper are to give an overview on role play in statistics learning and to access the effect of these activities on students' attitude and learning in action research framework. The computer tool entrepreneur role play is conducted in a two-hour tutorial class session of first year students in Faculty of Information Sciences and Technology (FTSM), Universiti Kebangsaan Malaysia, enrolled in Probability and Statistics course. The results show that most students feel that they have enjoyable and great time in the role play. Furthermore, benefits and disadvantages from role play activities were highlighted to complete the review. Role play is expected to serve as an important activities that take into account students' experience, emotions and responses to provide useful information on how to modify student's thinking or behavior to improve learning.

Measuring Student Learning in Social Statistics: A Pretest-Posttest Study of Knowledge Gain

ERIC Educational Resources Information Center

Delucchi, Michael

2014-01-01

This study used a pretest-posttest design to measure student learning in undergraduate statistics. Data were derived from 185 students enrolled in six different sections of a social statistics course taught over a seven-year period by the same sociology instructor. The pretest-posttest instrument reveals statistically significant gains in…

Survey of Native English Speakers and Spanish-Speaking English Language Learners in Tertiary Introductory Statistics

ERIC Educational Resources Information Center

Lesser, Lawrence M.; Wagler, Amy E.; Esquinca, Alberto; Valenzuela, M. Guadalupe

2013-01-01

The framework of linguistic register and case study research on Spanish-speaking English language learners (ELLs) learning statistics informed the construction of a quantitative instrument, the Communication, Language, And Statistics Survey (CLASS). CLASS aims to assess whether ELLs and non-ELLs approach the learning of statistics differently with…

Statistical learning of multisensory regularities is enhanced in musicians: An MEG study.

PubMed

Paraskevopoulos, Evangelos; Chalas, Nikolas; Kartsidis, Panagiotis; Wollbrink, Andreas; Bamidis, Panagiotis

2018-07-15

The present study used magnetoencephalography (MEG) to identify the neural correlates of audiovisual statistical learning, while disentangling the differential contributions of uni- and multi-modal statistical mismatch responses in humans. The applied paradigm was based on a combination of a statistical learning paradigm and a multisensory oddball one, combining an audiovisual, an auditory and a visual stimulation stream, along with the corresponding deviances. Plasticity effects due to musical expertise were investigated by comparing the behavioral and MEG responses of musicians to non-musicians. The behavioral results indicated that the learning was successful for both musicians and non-musicians. The unimodal MEG responses are consistent with previous studies, revealing the contribution of Heschl's gyrus for the identification of auditory statistical mismatches and the contribution of medial temporal and visual association areas for the visual modality. The cortical network underlying audiovisual statistical learning was found to be partly common and partly distinct from the corresponding unimodal networks, comprising right temporal and left inferior frontal sources. Musicians showed enhanced activation in superior temporal and superior frontal gyrus. Connectivity and information processing flow amongst the sources comprising the cortical network of audiovisual statistical learning, as estimated by transfer entropy, was reorganized in musicians, indicating enhanced top-down processing. This neuroplastic effect showed a cross-modal stability between the auditory and audiovisual modalities. Copyright © 2018 Elsevier Inc. All rights reserved.

Active Learning with Statistical Models.

DTIC Science & Technology

1995-01-01

Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with

Inverse tissue mechanics of cell monolayer expansion.

PubMed

Kondo, Yohei; Aoki, Kazuhiro; Ishii, Shin

2018-03-01

Living tissues undergo deformation during morphogenesis. In this process, cells generate mechanical forces that drive the coordinated cell motion and shape changes. Recent advances in experimental and theoretical techniques have enabled in situ measurement of the mechanical forces, but the characterization of mechanical properties that determine how these forces quantitatively affect tissue deformation remains challenging, and this represents a major obstacle for the complete understanding of morphogenesis. Here, we proposed a non-invasive reverse-engineering approach for the estimation of the mechanical properties, by combining tissue mechanics modeling and statistical machine learning. Our strategy is to model the tissue as a continuum mechanical system and to use passive observations of spontaneous tissue deformation and force fields to statistically estimate the model parameters. This method was applied to the analysis of the collective migration of Madin-Darby canine kidney cells, and the tissue flow and force were simultaneously observed by the phase contrast imaging and traction force microscopy. We found that our monolayer elastic model, whose elastic moduli were reverse-engineered, enabled a long-term forecast of the traction force fields when given the tissue flow fields, indicating that the elasticity contributes to the evolution of the tissue stress. Furthermore, we investigated the tissues in which myosin was inhibited by blebbistatin treatment, and observed a several-fold reduction in the elastic moduli. The obtained results validate our framework, which paves the way to the estimation of mechanical properties of living tissues during morphogenesis.

Real-world visual statistics and infants' first-learned object names

PubMed Central

Clerkin, Elizabeth M.; Hart, Elizabeth; Rehg, James M.; Yu, Chen

2017-01-01

We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 month-old infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present—a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872373

Student engagement in pharmacology courses using online learning tools.

PubMed

Karaksha, Abdullah; Grant, Gary; Anoopkumar-Dukie, Shailendra; Nirthanan, S Niru; Davey, Andrew K

2013-08-12

To assess factors influencing student engagement with e-tools used as a learning supplement to the standard curriculum in pharmacology courses. A suite of 148 e-tools (interactive online teaching materials encompassing the basic mechanisms of action for different drug classes) were designed and implemented across 2 semesters for third-year pharmacy students. Student engagement and use of this new teaching strategy were assessed using a survey instrument and usage statistics for the material. Use of e-tools during semester 1 was low, a finding attributable to a majority (75%) of students either being unaware of or forgetting about the embedded e-tools and a few (20%) lacking interest in accessing additional learning materials. In contrast to semester 1, e-tool use significantly increased in semester 2 with the use of frequent reminders and announcements (p<0.001). The provision of online teaching and learning resources were only effective in increasing student engagement after the implementation of a "marketing strategy" that included e-mail reminders and motivation.

Student Engagement in Pharmacology Courses Using Online Learning Tools

PubMed Central

Karaksha, Abdullah; Grant, Gary; Anoopkumar-Dukie, Shailendra; Nirthanan, S. Niru

2013-01-01

Objective. To assess factors influencing student engagement with e-tools used as a learning supplement to the standard curriculum in pharmacology courses. Design. A suite of 148 e-tools (interactive online teaching materials encompassing the basic mechanisms of action for different drug classes) were designed and implemented across 2 semesters for third-year pharmacy students. Assessment. Student engagement and use of this new teaching strategy were assessed using a survey instrument and usage statistics for the material. Use of e-tools during semester 1 was low, a finding attributable to a majority (75%) of students either being unaware of or forgetting about the embedded e-tools and a few (20%) lacking interest in accessing additional learning materials. In contrast to semester 1, e-tool use significantly increased in semester 2 with the use of frequent reminders and announcements (p<0.001). Conclusion. The provision of online teaching and learning resources were only effective in increasing student engagement after the implementation of a “marketing strategy” that included e-mail reminders and motivation. PMID:23966728

Current Developments in Machine Learning Techniques in Biological Data Mining.

PubMed

Dumancas, Gerard G; Adrianto, Indra; Bello, Ghalib; Dozmorov, Mikhail

2017-01-01

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

Learning predictive statistics from temporal sequences: Dynamics and strategies.

PubMed

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-10-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

A Primer on the Statistical Modelling of Learning Curves in Health Professions Education

ERIC Educational Resources Information Center

Pusic, Martin V.; Boutis, Kathy; Pecaric, Martin R.; Savenkov, Oleksander; Beckstead, Jason W.; Jaber, Mohamad Y.

2017-01-01

Learning curves are a useful way of representing the rate of learning over time. Features include an index of baseline performance (y-intercept), the efficiency of learning over time (slope parameter) and the maximal theoretical performance achievable (upper asymptote). Each of these parameters can be statistically modelled on an individual and…

Students' Perceptions of Computer-Based Learning Environments, Their Attitude towards Business Statistics, and Their Academic Achievement: Implications from a UK University

ERIC Educational Resources Information Center

Nguyen, ThuyUyen H.; Charity, Ian; Robson, Andrew

2016-01-01

This study investigates students' perceptions of computer-based learning environments, their attitude towards business statistics, and their academic achievement in higher education. Guided by learning environments concepts and attitudinal theory, a theoretical model was proposed with two instruments, one for measuring the learning environment and…

Content, Affective, and Behavioral Challenges to Learning: Students' Experiences Learning Statistics

ERIC Educational Resources Information Center

McGrath, April L.

2014-01-01

This study examined the experiences of and challenges faced by students when completing a statistics course. As part of the requirement for this course, students completed a learning check-in, which consisted of an individual meeting with the instructor to discuss questions and the completion of a learning reflection and study plan. Forty…

Finnish Upper Secondary Students' Collaborative Processes in Learning Statistics in a CSCL Environment

ERIC Educational Resources Information Center

Oikarinen, Juho Kaleva; Järvelä, Sanna; Kaasila, Raimo

2014-01-01

This design-based research project focuses on documenting statistical learning among 16-17-year-old Finnish upper secondary school students (N = 78) in a computer-supported collaborative learning (CSCL) environment. One novel value of this study is in reporting the shift from teacher-led mathematical teaching to autonomous small-group learning in…

Can You Explain that in Plain English? Making Statistics Group Projects Work in a Multicultural Setting

ERIC Educational Resources Information Center

Sisto, Michelle

2009-01-01

Students increasingly need to learn to communicate statistical results clearly and effectively, as well as to become competent consumers of statistical information. These two learning goals are particularly important for business students. In line with reform movements in Statistics Education and the GAISE guidelines, we are working to implement…

Designing a Course in Statistics for a Learning Health Systems Training Program

ERIC Educational Resources Information Center

Samsa, Gregory P.; LeBlanc, Thomas W.; Zaas, Aimee; Howie, Lynn; Abernethy, Amy P.

2014-01-01

The core pedagogic problem considered here is how to effectively teach statistics to physicians who are engaged in a "learning health system" (LHS). This is a special case of a broader issue--namely, how to effectively teach statistics to academic physicians for whom research--and thus statistics--is a requirement for professional…

Simulation-driven machine learning: Bearing fault classification

NASA Astrophysics Data System (ADS)

Sobie, Cameron; Freitas, Carina; Nicolai, Mike

2018-01-01

Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

Non-Gaussian Methods for Causal Structure Learning.

PubMed

Shimizu, Shohei

2018-05-22

Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

Mechanistic models versus machine learning, a fight worth fighting for the biological community?

PubMed

Baker, Ruth E; Peña, Jose-Maria; Jayamohan, Jayaratnam; Jérusalem, Antoine

2018-05-01

Ninety per cent of the world's data have been generated in the last 5 years ( Machine learning: the power and promise of computers that learn by example Report no. DES4702. Issued April 2017. Royal Society). A small fraction of these data is collected with the aim of validating specific hypotheses. These studies are led by the development of mechanistic models focused on the causality of input-output relationships. However, the vast majority is aimed at supporting statistical or correlation studies that bypass the need for causality and focus exclusively on prediction. Along these lines, there has been a vast increase in the use of machine learning models, in particular in the biomedical and clinical sciences, to try and keep pace with the rate of data generation. Recent successes now beg the question of whether mechanistic models are still relevant in this area. Said otherwise, why should we try to understand the mechanisms of disease progression when we can use machine learning tools to directly predict disease outcome? © 2018 The Author(s).

Statistical mechanics of complex neural systems and high dimensional data

NASA Astrophysics Data System (ADS)

Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

2013-03-01

Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks.

Humans Rapidly Learn Grammatical Structure in a New Musical Scale

PubMed Central

Loui, Psyche; Wessel, David L.; Hudson Kam, Carla L.

2010-01-01

Knowledge of musical rules and structures has been reliably demonstrated in humans of different ages, cultures, and levels of music training, and has been linked to our musical preferences. However, how humans acquire knowledge of and develop preferences for music remains unknown. The present study shows that humans rapidly develop knowledge and preferences when given limited exposure to a new musical system. Using a non-traditional, unfamiliar musical scale (Bohlen-Pierce scale), we created finite-state musical grammars from which we composed sets of melodies. After 25–30 min of passive exposure to the melodies, participants showed extensive learning as characterized by recognition, generalization, and sensitivity to the event frequencies in their given grammar, as well as increased preference for repeated melodies in the new musical system. Results provide evidence that a domain-general statistical learning mechanism may account for much of the human appreciation for music. PMID:20740059

Correlation between musical aptitude and learning foreign languages: an epidemiological study in secondary school Italian students.

PubMed

Picciotti, P M; Bussu, F; Calò, L; Gallus, R; Scarano, E; DI Cintio, G; Cassarà, F; D'Alatri, L

2018-02-01

The aim of this study was to assess if a correlation exists between language learning skills and musical aptitude through the analysis of scholarly outcomes concerning the study of foreign languages and music. We enrolled 502 students from a secondary Italian school (10-14 years old), attending both traditional courses (2 hours/week of music classes scheduled) and special courses (six hours). For statistical analysis, we considered grades in English, French and Music. Our results showed a significant correlation between grades in the two foreign languages and in music, both in the traditional courses and in special courses, and better results in French than for special courses. These results are discussed and interpreted through the literature about neuroanatomical and physiological mechanisms of foreign language learning and music perception. Copyright © 2018 Società Italiana di Otorinolaringologia e Chirurgia Cervico-Facciale, Rome, Italy.

Humans Rapidly Learn Grammatical Structure in a New Musical Scale.

PubMed

Loui, Psyche; Wessel, David L; Hudson Kam, Carla L

2010-06-01

Knowledge of musical rules and structures has been reliably demonstrated in humans of different ages, cultures, and levels of music training, and has been linked to our musical preferences. However, how humans acquire knowledge of and develop preferences for music remains unknown. The present study shows that humans rapidly develop knowledge and preferences when given limited exposure to a new musical system. Using a non-traditional, unfamiliar musical scale (Bohlen-Pierce scale), we created finite-state musical grammars from which we composed sets of melodies. After 25-30 min of passive exposure to the melodies, participants showed extensive learning as characterized by recognition, generalization, and sensitivity to the event frequencies in their given grammar, as well as increased preference for repeated melodies in the new musical system. Results provide evidence that a domain-general statistical learning mechanism may account for much of the human appreciation for music.

Robust short-term memory without synaptic learning.

PubMed

Johnson, Samuel; Marro, J; Torres, Joaquín J

2013-01-01

Short-term memory in the brain cannot in general be explained the way long-term memory can--as a gradual modification of synaptic weights--since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.

Domain General Constraints on Statistical Learning

ERIC Educational Resources Information Center

Thiessen, Erik D.

2011-01-01

All theories of language development suggest that learning is constrained. However, theories differ on whether these constraints arise from language-specific processes or have domain-general origins such as the characteristics of human perception and information processing. The current experiments explored constraints on statistical learning of…

Statistical Relational Learning (SRL) as an Enabling Technology for Data Acquisition and Data Fusion in Video

DTIC Science & Technology

2013-05-02

REPORT Statistical Relational Learning ( SRL ) as an Enabling Technology for Data Acquisition and Data Fusion in Video 14. ABSTRACT 16. SECURITY...particular, it is important to reason about which portions of video require expensive analysis and storage. This project aims to make these...inferences using new and existing tools from Statistical Relational Learning ( SRL ). SRL is a recently emerging technology that enables the effective 1

Time-course variation of statistics embedded in music: Corpus study on implicit learning and knowledge.

PubMed

Daikoku, Tatsuya

2018-01-01

Learning and knowledge of transitional probability in sequences like music, called statistical learning and knowledge, are considered implicit processes that occur without intention to learn and awareness of what one knows. This implicit statistical knowledge can be alternatively expressed via abstract medium such as musical melody, which suggests this knowledge is reflected in melodies written by a composer. This study investigates how statistics in music vary over a composer's lifetime. Transitional probabilities of highest-pitch sequences in Ludwig van Beethoven's Piano Sonata were calculated based on different hierarchical Markov models. Each interval pattern was ordered based on the sonata opus number. The transitional probabilities of sequential patterns that are musical universal in music gradually decreased, suggesting that time-course variations of statistics in music reflect time-course variations of a composer's statistical knowledge. This study sheds new light on novel methodologies that may be able to evaluate the time-course variation of composer's implicit knowledge using musical scores.

Region-specific network plasticity in simulated and living cortical networks: comparison of the center of activity trajectory (CAT) with other statistics

NASA Astrophysics Data System (ADS)

Chao, Zenas C.; Bakkum, Douglas J.; Potter, Steve M.

2007-09-01

Electrically interfaced cortical networks cultured in vitro can be used as a model for studying the network mechanisms of learning and memory. Lasting changes in functional connectivity have been difficult to detect with extracellular multi-electrode arrays using standard firing rate statistics. We used both simulated and living networks to compare the ability of various statistics to quantify functional plasticity at the network level. Using a simulated integrate-and-fire neural network, we compared five established statistical methods to one of our own design, called center of activity trajectory (CAT). CAT, which depicts dynamics of the location-weighted average of spatiotemporal patterns of action potentials across the physical space of the neuronal circuitry, was the most sensitive statistic for detecting tetanus-induced plasticity in both simulated and living networks. By reducing the dimensionality of multi-unit data while still including spatial information, CAT allows efficient real-time computation of spatiotemporal activity patterns. Thus, CAT will be useful for studies in vivo or in vitro in which the locations of recording sites on multi-electrode probes are important.

Synaptic and nonsynaptic plasticity approximating probabilistic inference

PubMed Central

Tully, Philip J.; Hennig, Matthias H.; Lansner, Anders

2014-01-01

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert. PMID:24782758

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning

PubMed Central

Turk-Browne, Nicholas B.; Botvinick, Matthew M.; Norman, Kenneth A.

2017-01-01

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway—the pathway connecting entorhinal cortex directly to region CA1—was able to support statistical learning, while the trisynaptic pathway—connecting entorhinal cortex to CA1 through dentate gyrus and CA3—learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872368

Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning.

PubMed

Schapiro, Anna C; Turk-Browne, Nicholas B; Botvinick, Matthew M; Norman, Kenneth A

2017-01-05

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway-the pathway connecting entorhinal cortex directly to region CA1-was able to support statistical learning, while the trisynaptic pathway-connecting entorhinal cortex to CA1 through dentate gyrus and CA3-learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

Learning place cells, grid cells and invariances with excitatory and inhibitory plasticity

PubMed Central

2018-01-01

Neurons in the hippocampus and adjacent brain areas show a large diversity in their tuning to location and head direction, and the underlying circuit mechanisms are not yet resolved. In particular, it is unclear why certain cell types are selective to one spatial variable, but invariant to another. For example, place cells are typically invariant to head direction. We propose that all observed spatial tuning patterns – in both their selectivity and their invariance – arise from the same mechanism: Excitatory and inhibitory synaptic plasticity driven by the spatial tuning statistics of synaptic inputs. Using simulations and a mathematical analysis, we show that combined excitatory and inhibitory plasticity can lead to localized, grid-like or invariant activity. Combinations of different input statistics along different spatial dimensions reproduce all major spatial tuning patterns observed in rodents. Our proposed model is robust to changes in parameters, develops patterns on behavioral timescales and makes distinctive experimental predictions. PMID:29465399

"Of Course I'm Communicating; I Lecture Every Day": Enhancing Teaching and Learning in Introductory Statistics. Scholarship of Teaching and Learning

ERIC Educational Resources Information Center

Wulff, Shaun S.; Wulff, Donald H.

2004-01-01

This article focuses on one instructor's evolution from formal lecturing to interactive teaching and learning in a statistics course. Student perception data are used to demonstrate the instructor's use of communication to align the content, students, and instructor throughout the course. Results indicate that the students learned, that…

Students' Perception of the Condition of Their Classroom Physical Learning Environment and Its Impact on Their Learning and Motivation

ERIC Educational Resources Information Center

Asiyai, Romina

2014-01-01

This study examined the perception of secondary school students on the condition of their classroom physical learning environment and its impact on their learning and motivation. Four research questions were asked and answered using descriptive statistics while three hypotheses were formulated and tested using t-test statistics at 0.05 level of…

Neural Evidence of Statistical Learning: Efficient Detection of Visual Regularities without Awareness

ERIC Educational Resources Information Center

Turk-Browne, Nicholas B.; Scholl, Brian J.; Chun, Marvin M.; Johnson, Marcia K.

2009-01-01

Our environment contains regularities distributed in space and time that can be detected by way of statistical learning. This unsupervised learning occurs without intent or awareness, but little is known about how it relates to other types of learning, how it affects perceptual processing, and how quickly it can occur. Here we use fMRI during…

Intuitive statistics by 8-month-old infants

PubMed Central

Xu, Fei; Garcia, Vashti

2008-01-01

Human learners make inductive inferences based on small amounts of data: we generalize from samples to populations and vice versa. The academic discipline of statistics formalizes these intuitive statistical inferences. What is the origin of this ability? We report six experiments investigating whether 8-month-old infants are “intuitive statisticians.” Our results showed that, given a sample, the infants were able to make inferences about the population from which the sample had been drawn. Conversely, given information about the entire population of relatively small size, the infants were able to make predictions about the sample. Our findings provide evidence that infants possess a powerful mechanism for inductive learning, either using heuristics or basic principles of probability. This ability to make inferences based on samples or information about the population develops early and in the absence of schooling or explicit teaching. Human infants may be rational learners from very early in development. PMID:18378901

Statistical assessment of the learning curves of health technologies.

PubMed

Ramsay, C R; Grant, A M; Wallace, S A; Garthwaite, P H; Monk, A F; Russell, I T

2001-01-01

(1) To describe systematically studies that directly assessed the learning curve effect of health technologies. (2) Systematically to identify 'novel' statistical techniques applied to learning curve data in other fields, such as psychology and manufacturing. (3) To test these statistical techniques in data sets from studies of varying designs to assess health technologies in which learning curve effects are known to exist. METHODS - STUDY SELECTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): For a study to be included, it had to include a formal analysis of the learning curve of a health technology using a graphical, tabular or statistical technique. METHODS - STUDY SELECTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): For a study to be included, it had to include a formal assessment of a learning curve using a statistical technique that had not been identified in the previous search. METHODS - DATA SOURCES: Six clinical and 16 non-clinical biomedical databases were searched. A limited amount of handsearching and scanning of reference lists was also undertaken. METHODS - DATA EXTRACTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): A number of study characteristics were abstracted from the papers such as study design, study size, number of operators and the statistical method used. METHODS - DATA EXTRACTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): The new statistical techniques identified were categorised into four subgroups of increasing complexity: exploratory data analysis; simple series data analysis; complex data structure analysis, generic techniques. METHODS - TESTING OF STATISTICAL METHODS: Some of the statistical methods identified in the systematic searches for single (simple) operator series data and for multiple (complex) operator series data were illustrated and explored using three data sets. The first was a case series of 190 consecutive laparoscopic fundoplication procedures performed by a single surgeon; the second was a case series of consecutive laparoscopic cholecystectomy procedures performed by ten surgeons; the third was randomised trial data derived from the laparoscopic procedure arm of a multicentre trial of groin hernia repair, supplemented by data from non-randomised operations performed during the trial. RESULTS - HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW: Of 4571 abstracts identified, 272 (6%) were later included in the study after review of the full paper. Some 51% of studies assessed a surgical minimal access technique and 95% were case series. The statistical method used most often (60%) was splitting the data into consecutive parts (such as halves or thirds), with only 14% attempting a more formal statistical analysis. The reporting of the studies was poor, with 31% giving no details of data collection methods. RESULTS - NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH: Of 9431 abstracts assessed, 115 (1%) were deemed appropriate for further investigation and, of these, 18 were included in the study. All of the methods for complex data sets were identified in the non-clinical literature. These were discriminant analysis, two-stage estimation of learning rates, generalised estimating equations, multilevel models, latent curve models, time series models and stochastic parameter models. In addition, eight new shapes of learning curves were identified. RESULTS - TESTING OF STATISTICAL METHODS: No one particular shape of learning curve performed significantly better than another. The performance of 'operation time' as a proxy for learning differed between the three procedures. Multilevel modelling using the laparoscopic cholecystectomy data demonstrated and measured surgeon-specific and confounding effects. The inclusion of non-randomised cases, despite the possible limitations of the method, enhanced the interpretation of learning effects. CONCLUSIONS - HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW: The statistical methods used for assessing learning effects in health technology assessment have been crude and the reporting of studies poor. CONCLUSIONS - NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH: A number of statistical methods for assessing learning effects were identified that had not hitherto been used in health technology assessment. There was a hierarchy of methods for the identification and measurement of learning, and the more sophisticated methods for both have had little if any use in health technology assessment. This demonstrated the value of considering fields outside clinical research when addressing methodological issues in health technology assessment. CONCLUSIONS - TESTING OF STATISTICAL METHODS: It has been demonstrated that the portfolio of techniques identified can enhance investigations of learning curve effects. (ABSTRACT TRUNCATED)

Assessment of Problem-Based Learning in the Undergraduate Statistics Course

ERIC Educational Resources Information Center

Karpiak, Christie P.

2011-01-01

Undergraduate psychology majors (N = 51) at a mid-sized private university took a statistics examination on the first day of the research methods course, a course for which a grade of "C" or higher in statistics is a prerequisite. Students who had taken a problem-based learning (PBL) section of the statistics course (n = 15) were compared to those…

Learning Essential Terms and Concepts in Statistics and Accounting

ERIC Educational Resources Information Center

Peters, Pam; Smith, Adam; Middledorp, Jenny; Karpin, Anne; Sin, Samantha; Kilgore, Alan

2014-01-01

This paper describes a terminological approach to the teaching and learning of fundamental concepts in foundation tertiary units in Statistics and Accounting, using an online dictionary-style resource (TermFinder) with customised "termbanks" for each discipline. Designed for independent learning, the termbanks support inquiring students…

Attitudes towards statistics of graduate entry medical students: the role of prior learning experiences

PubMed Central

2014-01-01

Background While statistics is increasingly taught as part of the medical curriculum, it can be an unpopular subject and feedback from students indicates that some find it more difficult than other subjects. Understanding attitudes towards statistics on entry to graduate entry medical programmes is particularly important, given that many students may have been exposed to quantitative courses in their previous degree and hence bring preconceptions of their ability and interest to their medical education programme. The aim of this study therefore is to explore, for the first time, attitudes towards statistics of graduate entry medical students from a variety of backgrounds and focus on understanding the role of prior learning experiences. Methods 121 first year graduate entry medical students completed the Survey of Attitudes toward Statistics instrument together with information on demographics and prior learning experiences. Results Students tended to appreciate the relevance of statistics in their professional life and be prepared to put effort into learning statistics. They had neutral to positive attitudes about their interest in statistics and their intellectual knowledge and skills when applied to it. Their feelings towards statistics were slightly less positive e.g. feelings of insecurity, stress, fear and frustration and they tended to view statistics as difficult. Even though 85% of students had taken a quantitative course in the past, only 24% of students described it as likely that they would take any course in statistics if the choice was theirs. How well students felt they had performed in mathematics in the past was a strong predictor of many of the components of attitudes. Conclusion The teaching of statistics to medical students should start with addressing the association between students’ past experiences in mathematics and their attitudes towards statistics and encouraging students to recognise the difference between the two disciplines. Addressing these issues may reduce students’ anxiety and perception of difficulty at the start of their learning experience and encourage students to engage with statistics in their future careers. PMID:24708762

Attitudes towards statistics of graduate entry medical students: the role of prior learning experiences.

PubMed

Hannigan, Ailish; Hegarty, Avril C; McGrath, Deirdre

2014-04-04

While statistics is increasingly taught as part of the medical curriculum, it can be an unpopular subject and feedback from students indicates that some find it more difficult than other subjects. Understanding attitudes towards statistics on entry to graduate entry medical programmes is particularly important, given that many students may have been exposed to quantitative courses in their previous degree and hence bring preconceptions of their ability and interest to their medical education programme. The aim of this study therefore is to explore, for the first time, attitudes towards statistics of graduate entry medical students from a variety of backgrounds and focus on understanding the role of prior learning experiences. 121 first year graduate entry medical students completed the Survey of Attitudes toward Statistics instrument together with information on demographics and prior learning experiences. Students tended to appreciate the relevance of statistics in their professional life and be prepared to put effort into learning statistics. They had neutral to positive attitudes about their interest in statistics and their intellectual knowledge and skills when applied to it. Their feelings towards statistics were slightly less positive e.g. feelings of insecurity, stress, fear and frustration and they tended to view statistics as difficult. Even though 85% of students had taken a quantitative course in the past, only 24% of students described it as likely that they would take any course in statistics if the choice was theirs. How well students felt they had performed in mathematics in the past was a strong predictor of many of the components of attitudes. The teaching of statistics to medical students should start with addressing the association between students' past experiences in mathematics and their attitudes towards statistics and encouraging students to recognise the difference between the two disciplines. Addressing these issues may reduce students' anxiety and perception of difficulty at the start of their learning experience and encourage students to engage with statistics in their future careers.

Why the Brain Knows More than We Do: Non-Conscious Representations and Their Role in the Construction of Conscious Experience

PubMed Central

Dresp-Langley, Birgitta

2011-01-01

Scientific studies have shown that non-conscious stimuli and representations influence information processing during conscious experience. In the light of such evidence, questions about potential functional links between non-conscious brain representations and conscious experience arise. This article discusses neural model capable of explaining how statistical learning mechanisms in dedicated resonant circuits could generate specific temporal activity traces of non-conscious representations in the brain. How reentrant signaling, top-down matching, and statistical coincidence of such activity traces may lead to the progressive consolidation of temporal patterns that constitute the neural signatures of conscious experience in networks extending across large distances beyond functionally specialized brain regions is then explained. PMID:24962683

Humans make efficient use of natural image statistics when performing spatial interpolation.

PubMed

D'Antona, Anthony D; Perry, Jeffrey S; Geisler, Wilson S

2013-12-16

Visual systems learn through evolution and experience over the lifespan to exploit the statistical structure of natural images when performing visual tasks. Understanding which aspects of this statistical structure are incorporated into the human nervous system is a fundamental goal in vision science. To address this goal, we measured human ability to estimate the intensity of missing image pixels in natural images. Human estimation accuracy is compared with various simple heuristics (e.g., local mean) and with optimal observers that have nearly complete knowledge of the local statistical structure of natural images. Human estimates are more accurate than those of simple heuristics, and they match the performance of an optimal observer that knows the local statistical structure of relative intensities (contrasts). This optimal observer predicts the detailed pattern of human estimation errors and hence the results place strong constraints on the underlying neural mechanisms. However, humans do not reach the performance of an optimal observer that knows the local statistical structure of the absolute intensities, which reflect both local relative intensities and local mean intensity. As predicted from a statistical analysis of natural images, human estimation accuracy is negligibly improved by expanding the context from a local patch to the whole image. Our results demonstrate that the human visual system exploits efficiently the statistical structure of natural images.

Trans-species learning of cellular signaling systems with bimodal deep belief networks

PubMed Central

Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

2015-01-01

Motivation: Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. Results: We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These ‘deep learning’ models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. Availability and implementation: The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. Contact: xinghua@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25995230

Nonlinear machine learning in soft materials engineering and design

NASA Astrophysics Data System (ADS)

Ferguson, Andrew

The inherently many-body nature of molecular folding and colloidal self-assembly makes it challenging to identify the underlying collective mechanisms and pathways governing system behavior, and has hindered rational design of soft materials with desired structure and function. Fundamentally, there exists a predictive gulf between the architecture and chemistry of individual molecules or colloids and the collective many-body thermodynamics and kinetics. Integrating machine learning techniques with statistical thermodynamics provides a means to bridge this divide and identify emergent folding pathways and self-assembly mechanisms from computer simulations or experimental particle tracking data. We will survey a few of our applications of this framework that illustrate the value of nonlinear machine learning in understanding and engineering soft materials: the non-equilibrium self-assembly of Janus colloids into pinwheels, clusters, and archipelagos; engineering reconfigurable ''digital colloids'' as a novel high-density information storage substrate; probing hierarchically self-assembling onjugated asphaltenes in crude oil; and determining macromolecular folding funnels from measurements of single experimental observables. We close with an outlook on the future of machine learning in soft materials engineering, and share some personal perspectives on working at this disciplinary intersection. We acknowledge support for this work from a National Science Foundation CAREER Award (Grant No. DMR-1350008) and the Donors of the American Chemical Society Petroleum Research Fund (ACS PRF #54240-DNI6).

Real-world visual statistics and infants' first-learned object names.

PubMed

Clerkin, Elizabeth M; Hart, Elizabeth; Rehg, James M; Yu, Chen; Smith, Linda B

2017-01-05

We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 month-old infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present-a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

Explaining Student Achievement: The Influence of Teachers' Pedagogical Content Knowledge in Statistics

ERIC Educational Resources Information Center

Callingham, Rosemary; Carmichael, Colin; Watson, Jane M.

2016-01-01

Statistics is an increasingly important component of the mathematics curriculum. "StatSmart" was a project intended to influence middle-years students' learning outcomes in statistics through the provision of appropriate professional learning opportunities and technology to teachers. Participating students in grade 5/6 to grade 9…

Improving Statistical Skills through Students' Participation in the Development of Resources

ERIC Educational Resources Information Center

Biza, Irene; Vande Hey, Eugénie

2015-01-01

This paper summarizes the evaluation of a project that involved undergraduate mathematics students in the development of teaching and learning resources for statistics modules taught in various departments of a university. This evaluation regards students' participation in the project and its impact on their learning of statistics, as…

A Model of Statistics Performance Based on Achievement Goal Theory.

ERIC Educational Resources Information Center

Bandalos, Deborah L.; Finney, Sara J.; Geske, Jenenne A.

2003-01-01

Tests a model of statistics performance based on achievement goal theory. Both learning and performance goals affected achievement indirectly through study strategies, self-efficacy, and test anxiety. Implications of these findings for teaching and learning statistics are discussed. (Contains 47 references, 3 tables, 3 figures, and 1 appendix.)…

Learning by Doing or Learning by Studying the History of Statistics? A Response to "The Sociology of Teaching Graduate Statistics"

ERIC Educational Resources Information Center

Farkas, George

2005-01-01

This article presents the author's response to Timothy Patrick Moran's article "The Sociology of Teaching Graduate Statistics." Since 1972, the author has taught the required graduate-level social statistics course in three different departments. During this time, he has seen the truth of the concerns that Moran expresses at the beginning of his…

Resistance gene identification from Larimichthys crocea with machine learning techniques

NASA Astrophysics Data System (ADS)

Cai, Yinyin; Liao, Zhijun; Ju, Ying; Liu, Juan; Mao, Yong; Liu, Xiangrong

2016-12-01

The research on resistance genes (R-gene) plays a vital role in bioinformatics as it has the capability of coping with adverse changes in the external environment, which can form the corresponding resistance protein by transcription and translation. It is meaningful to identify and predict R-gene of Larimichthys crocea (L.Crocea). It is friendly for breeding and the marine environment as well. Large amounts of L.Crocea’s immune mechanisms have been explored by biological methods. However, much about them is still unclear. In order to break the limited understanding of the L.Crocea’s immune mechanisms and to detect new R-gene and R-gene-like genes, this paper came up with a more useful combination prediction method, which is to extract and classify the feature of available genomic data by machine learning. The effectiveness of feature extraction and classification methods to identify potential novel R-gene was evaluated, and different statistical analyzes were utilized to explore the reliability of prediction method, which can help us further understand the immune mechanisms of L.Crocea against pathogens. In this paper, a webserver called LCRG-Pred is available at http://server.malab.cn/rg_lc/.

Content-based VLE designs improve learning efficiency in constructivist statistics education.

PubMed

Wessa, Patrick; De Rycker, Antoon; Holliday, Ian Edward

2011-01-01

We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific-purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a content-based design outperforms the traditional VLE-based design.

The mechanics of state dependent neural correlations

PubMed Central

Doiron, Brent; Litwin-Kumar, Ashok; Rosenbaum, Robert; Ocker, Gabriel K.; Josić, Krešimir

2016-01-01

Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of the population activity are the trial-to-trial correlated fluctuations of the spike train outputs of recorded neuron pairs. Like the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the network mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate biophysical mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations, and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways, and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high dimensional neural data. PMID:26906505

Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech

PubMed Central

Huebner, Philip A.; Willits, Jon A.

2018-01-01

Previous research has suggested that distributional learning mechanisms may contribute to the acquisition of semantic knowledge. However, distributional learning mechanisms, statistical learning, and contemporary “deep learning” approaches have been criticized for being incapable of learning the kind of abstract and structured knowledge that many think is required for acquisition of semantic knowledge. In this paper, we show that recurrent neural networks, trained on noisy naturalistic speech to children, do in fact learn what appears to be abstract and structured knowledge. We trained two types of recurrent neural networks (Simple Recurrent Network, and Long Short-Term Memory) to predict word sequences in a 5-million-word corpus of speech directed to children ages 0–3 years old, and assessed what semantic knowledge they acquired. We found that learned internal representations are encoding various abstract grammatical and semantic features that are useful for predicting word sequences. Assessing the organization of semantic knowledge in terms of the similarity structure, we found evidence of emergent categorical and hierarchical structure in both models. We found that the Long Short-term Memory (LSTM) and SRN are both learning very similar kinds of representations, but the LSTM achieved higher levels of performance on a quantitative evaluation. We also trained a non-recurrent neural network, Skip-gram, on the same input to compare our results to the state-of-the-art in machine learning. We found that Skip-gram achieves relatively similar performance to the LSTM, but is representing words more in terms of thematic compared to taxonomic relations, and we provide reasons why this might be the case. Our findings show that a learning system that derives abstract, distributed representations for the purpose of predicting sequential dependencies in naturalistic language may provide insight into emergence of many properties of the developing semantic system. PMID:29520243

The Developing Infant Creates a Curriculum for Statistical Learning.

PubMed

Smith, Linda B; Jayaraman, Swapnaa; Clerkin, Elizabeth; Yu, Chen

2018-04-01

New efforts are using head cameras and eye-trackers worn by infants to capture everyday visual environments from the point of view of the infant learner. From this vantage point, the training sets for statistical learning develop as the sensorimotor abilities of the infant develop, yielding a series of ordered datasets for visual learning that differ in content and structure between timepoints but are highly selective at each timepoint. These changing environments may constitute a developmentally ordered curriculum that optimizes learning across many domains. Future advances in computational models will be necessary to connect the developmentally changing content and statistics of infant experience to the internal machinery that does the learning. Copyright © 2018 Elsevier Ltd. All rights reserved.

Learning Object Names at Different Hierarchical Levels Using Cross-Situational Statistics.

PubMed

Chen, Chi-Hsin; Zhang, Yayun; Yu, Chen

2018-05-01

Objects in the world usually have names at different hierarchical levels (e.g., beagle, dog, animal). This research investigates adults' ability to use cross-situational statistics to simultaneously learn object labels at individual and category levels. The results revealed that adults were able to use co-occurrence information to learn hierarchical labels in contexts where the labels for individual objects and labels for categories were presented in completely separated blocks, in interleaved blocks, or mixed in the same trial. Temporal presentation schedules significantly affected the learning of individual object labels, but not the learning of category labels. Learners' subsequent generalization of category labels indicated sensitivity to the structure of statistical input. Copyright © 2017 Cognitive Science Society, Inc.

An Investigation of the Factors Motivating Meaningful Learning of Statistics by Graduate Systems Management Students at AFIT.

DTIC Science & Technology

1987-09-01

DAC-RiB 271 AN INVESTIGATION OF THE FACTORS MOTIVATING MEANINGFUL v’ LEARNING OF STATIST (U) AIR FORCE INST OF TECH WRIGHT-PATTERSON RFB OH SCHOOL OF...Furthermore, the views expressed in the document are those of the author and do not necessarily reflect the views of the School of Systems and...MEANINGFUL LEARNING OF STATISTICS BY GRADUATE SYSTEMS MANAGEMENT STUDENTS AT AFIT THESIS Presented to the Faculty of the School of Systems and Logistics

Learning of grammar-like visual sequences by adults with and without language-learning disabilities.

PubMed

Aguilar, Jessica M; Plante, Elena

2014-08-01

Two studies examined learning of grammar-like visual sequences to determine whether a general deficit in statistical learning characterizes this population. Furthermore, we tested the hypothesis that difficulty in sustaining attention during the learning task might account for differences in statistical learning. In Study 1, adults with normal language (NL) or language-learning disability (LLD) were familiarized with the visual artificial grammar and then tested using items that conformed or deviated from the grammar. In Study 2, a 2nd sample of adults with NL and LLD were presented auditory word pairs with weak semantic associations (e.g., groom + clean) along with the visual learning task. Participants were instructed to attend to visual sequences and to ignore the auditory stimuli. Incidental encoding of these words would indicate reduced attention to the primary task. In Studies 1 and 2, both groups demonstrated learning and generalization of the artificial grammar. In Study 2, neither the NL nor the LLD group appeared to encode the words presented during the learning phase. The results argue against a general deficit in statistical learning for individuals with LLD and demonstrate that both NL and LLD learners can ignore extraneous auditory stimuli during visual learning.

Intact implicit statistical learning in borderline personality disorder.

PubMed

Unoka, Zsolt; Vizin, Gabriella; Bjelik, Anna; Radics, Dóra; Nemeth, Dezso; Janacsek, Karolina

2017-09-01

Wide-spread neuropsychological deficits have been identified in borderline personality disorder (BPD). Previous research found impairments in decision making, declarative memory, working memory and executive functions; however, no studies have focused on implicit learning in BPD yet. The aim of our study was to investigate implicit statistical learning by comparing learning performance of 19 BPD patients and 19 healthy, age-, education- and gender-matched controls on a probabilistic sequence learning task. Moreover, we also tested whether participants retain the acquired knowledge after a delay period. To this end, participants were retested on a shorter version of the same task 24h after the learning phase. We found intact implicit statistical learning as well as retention of the acquired knowledge in this personality disorder. BPD patients seem to be able to extract and represent regularities implicitly, which is in line with the notion that implicit learning is less susceptible to illness compared to the more explicit processes. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Brain Tumor Risk Factors

MedlinePlus

... Factors Brain Tumor Statistics ABTA Publications Brain Tumor Dictionary Upcoming Webinars Anytime Learning Brain Tumor Educational Presentations ... Factors Brain Tumor Statistics ABTA Publications Brain Tumor Dictionary Upcoming Webinars Anytime Learning Brain Tumor Educational Presentations ...

Anatomy of the Brain

MedlinePlus

... Tumors Risk Factors Brain Tumor Statistics Brain Tumor Dictionary Webinars Anytime Learning About Us Our Founders Board ... Factors Brain Tumor Statistics ABTA Publications Brain Tumor Dictionary Upcoming Webinars Anytime Learning Brain Tumor Educational Presentations ...

Cooperative Learning in Virtual Environments: The Jigsaw Method in Statistical Courses

ERIC Educational Resources Information Center

Vargas-Vargas, Manuel; Mondejar-Jimenez, Jose; Santamaria, Maria-Letica Meseguer; Alfaro-Navarro, Jose-Luis; Fernandez-Aviles, Gema

2011-01-01

This document sets out a novel teaching methodology as used in subjects with statistical content, traditionally regarded by students as "difficult". In a virtual learning environment, instructional techniques little used in mathematical courses were employed, such as the Jigsaw cooperative learning method, which had to be adapted to the…

An Analysis of Research Trends in Dissertations and Theses Studying Blended Learning

ERIC Educational Resources Information Center

Drysdale, Jeffery S.; Graham, Charles R.; Spring, Kristian J.; Halverson, Lisa R.

2013-01-01

This article analyzes the research of 205 doctoral dissertations and masters' theses in the domain of blended learning. A summary of trends regarding the growth and context of blended learning research is presented. Methodological trends are described in terms of qualitative, inferential statistics, descriptive statistics, and combined approaches…

Statistical Learning as a Basis for Social Understanding in Children

ERIC Educational Resources Information Center

Ruffman, Ted; Taumoepeau, Mele; Perkins, Chris

2012-01-01

Many authors have argued that infants understand goals, intentions, and beliefs. We posit that infants' success on such tasks might instead reveal an understanding of behaviour, that infants' proficient statistical learning abilities might enable such insights, and that maternal talk scaffolds children's learning about the social world as well. We…

Effectiveness of Project Based Learning in Statistics for Lower Secondary Schools

ERIC Educational Resources Information Center

Siswono, Tatag Yuli Eko; Hartono, Sugi; Kohar, Ahmad Wachidul

2018-01-01

Purpose: This study aimed at investigating the effectiveness of implementing Project Based Learning (PBL) on the topic of statistics at a lower secondary school in Surabaya city, Indonesia, indicated by examining student learning outcomes, student responses, and student activity. Research Methods: A quasi experimental method was conducted over two…

Formal Operations and Learning Style Predict Success in Statistics and Computer Science Courses.

ERIC Educational Resources Information Center

Hudak, Mary A.; Anderson, David E.

1990-01-01

Studies 94 undergraduate students in introductory statistics and computer science courses. Applies Formal Operations Reasoning Test (FORT) and Kolb's Learning Style Inventory (LSI). Finds that substantial numbers of students have not achieved the formal operation level of cognitive maturity. Emphasizes need to examine students learning style and…

An Empirical Consideration of a Balanced Amalgamation of Learning Strategies in Graduate Introductory Statistics Classes

ERIC Educational Resources Information Center

Vaughn, Brandon K.

2009-01-01

This study considers the effectiveness of a "balanced amalgamated" approach to teaching graduate level introductory statistics. Although some research stresses replacing traditional lectures with more active learning methods, the approach of this study is to combine effective lecturing with active learning and team projects. The results of this…

Explorations in statistics: hypothesis tests and P values.

PubMed

Curran-Everett, Douglas

2009-06-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This second installment of Explorations in Statistics delves into test statistics and P values, two concepts fundamental to the test of a scientific null hypothesis. The essence of a test statistic is that it compares what we observe in the experiment to what we expect to see if the null hypothesis is true. The P value associated with the magnitude of that test statistic answers this question: if the null hypothesis is true, what proportion of possible values of the test statistic are at least as extreme as the one I got? Although statisticians continue to stress the limitations of hypothesis tests, there are two realities we must acknowledge: hypothesis tests are ingrained within science, and the simple test of a null hypothesis can be useful. As a result, it behooves us to explore the notions of hypothesis tests, test statistics, and P values.

Functional brain networks for learning predictive statistics.

PubMed

Giorgio, Joseph; Karlaftis, Vasilis M; Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew; Kourtzi, Zoe

2017-08-18

Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. This skill relies on extracting regular patterns in space and time by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the functional brain networks that mediate this type of statistical learning. Here, we test whether changes in the processing and connectivity of functional brain networks due to training relate to our ability to learn temporal regularities. By combining behavioral training and functional brain connectivity analysis, we demonstrate that individuals adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. Further, we show that individual learning of temporal structures relates to decision strategy. Our fMRI results demonstrate that learning-dependent changes in fMRI activation within and functional connectivity between brain networks relate to individual variability in strategy. In particular, extracting the exact sequence statistics (i.e., matching) relates to changes in brain networks known to be involved in memory and stimulus-response associations, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to changes in frontal and striatal networks. Thus, our findings provide evidence that dissociable brain networks mediate individual ability in learning behaviorally-relevant statistics. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Learning predictive statistics from temporal sequences: Dynamics and strategies

PubMed Central

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E.; Kourtzi, Zoe

2017-01-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics—that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments. PMID:28973111

Impaired Statistical Learning in Developmental Dyslexia

PubMed Central

Thiessen, Erik D.; Holt, Lori L.

2015-01-01

Purpose Developmental dyslexia (DD) is commonly thought to arise from phonological impairments. However, an emerging perspective is that a more general procedural learning deficit, not specific to phonological processing, may underlie DD. The current study examined if individuals with DD are capable of extracting statistical regularities across sequences of passively experienced speech and nonspeech sounds. Such statistical learning is believed to be domain-general, to draw upon procedural learning systems, and to relate to language outcomes. Method DD and control groups were familiarized with a continuous stream of syllables or sine-wave tones, the ordering of which was defined by high or low transitional probabilities across adjacent stimulus pairs. Participants subsequently judged two 3-stimulus test items with either high or low statistical coherence as being the most similar to the sounds heard during familiarization. Results As with control participants, the DD group was sensitive to the transitional probability structure of the familiarization materials as evidenced by above-chance performance. However, the performance of participants with DD was significantly poorer than controls across linguistic and nonlinguistic stimuli. In addition, reading-related measures were significantly correlated with statistical learning performance of both speech and nonspeech material. Conclusion Results are discussed in light of procedural learning impairments among participants with DD. PMID:25860795

Assessing Understanding of Sampling Distributions and Differences in Learning amongst Different Learning Styles

ERIC Educational Resources Information Center

Beeman, Jennifer Leigh Sloan

2013-01-01

Research has found that students successfully complete an introductory course in statistics without fully comprehending the underlying theory or being able to exhibit statistical reasoning. This is particularly true for the understanding about the sampling distribution of the mean, a crucial concept for statistical inference. This study…

Writing to Learn Statistics in an Advanced Placement Statistics Course

ERIC Educational Resources Information Center

Northrup, Christian Glenn

2012-01-01

This study investigated the use of writing in a statistics classroom to learn if writing provided a rich description of problem-solving processes of students as they solved problems. Through analysis of 329 written samples provided by students, it was determined that writing provided a rich description of problem-solving processes and enabled…

Students' Perspectives of Using Cooperative Learning in a Flipped Statistics Classroom

ERIC Educational Resources Information Center

Chen, Liwen; Chen, Tung-Liang; Chen, Nian-Shing

2015-01-01

Statistics has been recognised as one of the most anxiety-provoking subjects to learn in the higher education context. Educators have continuously endeavoured to find ways to integrate digital technologies and innovative pedagogies in the classroom to eliminate the fear of statistics. The purpose of this study is to systematically identify…

Teaching Engineering Statistics with Technology, Group Learning, Contextual Projects, Simulation Models and Student Presentations

ERIC Educational Resources Information Center

Romeu, Jorge Luis

2008-01-01

This article discusses our teaching approach in graduate level Engineering Statistics. It is based on the use of modern technology, learning groups, contextual projects, simulation models, and statistical and simulation software to entice student motivation. The use of technology to facilitate group projects and presentations, and to generate,…

Set size manipulations reveal the boundary conditions of perceptual ensemble learning.

PubMed

Chetverikov, Andrey; Campana, Gianluca; Kristjánsson, Árni

2017-11-01

Recent evidence suggests that observers can grasp patterns of feature variations in the environment with surprising efficiency. During visual search tasks where all distractors are randomly drawn from a certain distribution rather than all being homogeneous, observers are capable of learning highly complex statistical properties of distractor sets. After only a few trials (learning phase), the statistical properties of distributions - mean, variance and crucially, shape - can be learned, and these representations affect search during a subsequent test phase (Chetverikov, Campana, & Kristjánsson, 2016). To assess the limits of such distribution learning, we varied the information available to observers about the underlying distractor distributions by manipulating set size during the learning phase in two experiments. We found that robust distribution learning only occurred for large set sizes. We also used set size to assess whether the learning of distribution properties makes search more efficient. The results reveal how a certain minimum of information is required for learning to occur, thereby delineating the boundary conditions of learning of statistical variation in the environment. However, the benefits of distribution learning for search efficiency remain unclear. Copyright © 2017 Elsevier Ltd. All rights reserved.

Age and experience shape developmental changes in the neural basis of language-related learning.

PubMed

McNealy, Kristin; Mazziotta, John C; Dapretto, Mirella

2011-11-01

Very little is known about the neural underpinnings of language learning across the lifespan and how these might be modified by maturational and experiential factors. Building on behavioral research highlighting the importance of early word segmentation (i.e. the detection of word boundaries in continuous speech) for subsequent language learning, here we characterize developmental changes in brain activity as this process occurs online, using data collected in a mixed cross-sectional and longitudinal design. One hundred and fifty-six participants, ranging from age 5 to adulthood, underwent functional magnetic resonance imaging (fMRI) while listening to three novel streams of continuous speech, which contained either strong statistical regularities, strong statistical regularities and speech cues, or weak statistical regularities providing minimal cues to word boundaries. All age groups displayed significant signal increases over time in temporal cortices for the streams with high statistical regularities; however, we observed a significant right-to-left shift in the laterality of these learning-related increases with age. Interestingly, only the 5- to 10-year-old children displayed significant signal increases for the stream with low statistical regularities, suggesting an age-related decrease in sensitivity to more subtle statistical cues. Further, in a sample of 78 10-year-olds, we examined the impact of proficiency in a second language and level of pubertal development on learning-related signal increases, showing that the brain regions involved in language learning are influenced by both experiential and maturational factors. 2011 Blackwell Publishing Ltd.

Formation of automatic letter-colour associations in non-synaesthetes through likelihood manipulation of letter-colour pairings.

PubMed

Kusnir, Flor; Thut, Gregor

2012-12-01

Grapheme-colour synaesthesia is a well-characterized phenomenon in which achromatic letters and/or digits automatically and systematically trigger specific colour sensations. Models of its underlying mechanisms diverge on a central question: whether triggered sensations reflect (1) an overdeveloped capacity in normal cross-modal processing (i.e., sharing characteristics with the general population), or rather (2) qualitatively deviant processing (i.e., unique to a few individuals). To test to what extent synaesthesia-like (automatic) letter-colour associations may be learned by non-synaesthetes into adulthood, implied by (1), we developed a learning paradigm that aimed to implicitly train such associations via a visual search task that employed statistical probability learning of specific letter-colour pairs. In contrast to previous synaesthesia-training studies (Cohen Kadosh, Henik, Catena, Walsh, & Fuentes, 2009; Meier & Rothen, 2009), here all participants were naïve as to the end-goal of the experiment (i.e., the formation of letter-colour associations), mimicking the learning conditions of acquired grapheme-colour synaesthesia (Hancock, 2006; Witthoft & Winawer, 2006). In two experiments, we found evidence for significant binding of colours to letters by non-synaesthetes. These newly-formed associations showed synaesthesia-like characteristics, because they correlated in strength with performance on individual synaesthetic Stroop-tasks (experiment 1), and because interference between the learned (associated) colour and the real colour during letter processing depended on their relative positions in colour space (opponent vs. non-opponent colours, experiment 2) suggesting automatic formation on a perceptual rather than conceptual level, analogous to synaesthesia. Although not evoking conscious colour percepts, these learned, synaesthesia-like associations in non-synaesthetes support that common mechanisms may underlie letter-colour associations in synaesthetes and non-synaesthetes. Copyright © 2012 Elsevier Ltd. All rights reserved.

The relationship between procrastination, learning strategies and statistics anxiety among Iranian college students: a canonical correlation analysis.

PubMed

Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali

2012-01-01

Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction.

Tracking Multiple Statistics: Simultaneous Learning of Object Names and Categories in English and Mandarin Speakers

ERIC Educational Resources Information Center

Chen, Chi-hsin; Gershkoff-Stowe, Lisa; Wu, Chih-Yi; Cheung, Hintat; Yu, Chen

2017-01-01

Two experiments were conducted to examine adult learners' ability to extract multiple statistics in simultaneously presented visual and auditory input. Experiment 1 used a cross-situational learning paradigm to test whether English speakers were able to use co-occurrences to learn word-to-object mappings and concurrently form object categories…

Understanding Evaluation of Learning Support in Mathematics and Statistics

ERIC Educational Resources Information Center

MacGillivray, Helen; Croft, Tony

2011-01-01

With rapid and continuing growth of learning support initiatives in mathematics and statistics found in many parts of the world, and with the likelihood that this trend will continue, there is a need to ensure that robust and coherent measures are in place to evaluate the effectiveness of these initiatives. The nature of learning support brings…

2.5-Year-Olds Use Cross-Situational Consistency to Learn Verbs under Referential Uncertainty

ERIC Educational Resources Information Center

Scott, Rose M.; Fisher, Cynthia

2012-01-01

Recent evidence shows that children can use cross-situational statistics to learn new object labels under referential ambiguity (e.g., Smith & Yu, 2008). Such evidence has been interpreted as support for proposals that statistical information about word-referent co-occurrence plays a powerful role in word learning. But object labels represent only…

Why Students Need to Be Prepared to Cooperate: A Cooperative Nudge in Statistics Learning at University

ERIC Educational Resources Information Center

Buchs, Céline; Gilles, Ingrid; Antonietti, Jean-Philippe; Butera, Fabrizio

2016-01-01

Despite the potential benefits of cooperative learning at university, its implementation is challenging. Here, we propose a theory-based 90-min intervention with 185 first-year psychology students in the challenging domain of statistics, consisting of an exercise phase and an individual learning post-test. We compared three conditions that…

Predicting Student Success in a Psychological Statistics Course Emphasizing Collaborative Learning

ERIC Educational Resources Information Center

Gorvine, Benjamin J.; Smith, H. David

2015-01-01

This study describes the use of a collaborative learning approach in a psychological statistics course and examines the factors that predict which students benefit most from such an approach in terms of learning outcomes. In a course format with a substantial group work component, 166 students were surveyed on their preference for individual…

Enhancing an Undergraduate Business Statistics Course: Linking Teaching and Learning with Assessment Issues

ERIC Educational Resources Information Center

Fairfield-Sonn, James W.; Kolluri, Bharat; Rogers, Annette; Singamsetti, Rao

2009-01-01

This paper examines several ways in which teaching effectiveness and student learning in an undergraduate Business Statistics course can be enhanced. First, we review some key concepts in Business Statistics that are often challenging to teach and show how using real data sets assist students in developing deeper understanding of the concepts.…

Fostering Self-Concept and Interest for Statistics through Specific Learning Environments

ERIC Educational Resources Information Center

Sproesser, Ute; Engel, Joachim; Kuntze, Sebastian

2016-01-01

Supporting motivational variables such as self-concept or interest is an important goal of schooling as they relate to learning and achievement. In this study, we investigated whether specific interest and self-concept related to the domains of statistics and mathematics can be fostered through a four-lesson intervention focusing on statistics.…

Effects of basic character design and animation concepts using the flipped learning and project-based learning approach on learning achievement and creative thinking of higher education students

NASA Astrophysics Data System (ADS)

Autapao, Kanyarat; Minwong, Panthul

2018-01-01

Creative thinking was an important learning skill in the 21st Century via learning and innovation to promote students' creative thinking and working with others and to construct innovation. This is one of the important skills that determine the readiness of the participants to step into the complex society. The purposes of this research were 1) to compare the learning achievement of students after using basic character design and animation concepts using the flipped learning and project-based learning and 2) to make a comparison students' creative thinking between pretest and posttest. The populations were 29 students in Multimedia Technology program at Thepsatri Rajabhat University in the 2nd semester of the academic year 2016. The experimental instruments were lesson plans of basic character design and animation concepts using the flipped learning and project based learning. The data collecting instrument was creative thinking test. The data were analyzed by the arithmetic mean, standard deviation and The Wilcoxon Matched Pairs Signed-Ranks Test. The results of this research were 1) the learning achievement of students were statistically significance of .01 level and 2) the mean score of student's creativity assessment were statistically significance of .05 level. When considering all of 11 KPIs, showed that respondents' post-test mean scores higher than pre-test. And 5 KPIs were statistically significance of .05 level, consist of Originality, Fluency, Elaboration, Resistance to Premature Closure, and Intrinsic Motivation. It's were statistically significance of .042, .004, .049, .024 and .015 respectively. And 6 KPIs were non-statistically significant, include of Flexibility, Tolerance of Ambiguity, Divergent Thinking, Convergent Thinking, Risk Taking, and Extrinsic Motivation. The findings revealed that the flipped learning and project based learning provided students the freedom to simply learn on their own aptitude. When working together with project-based learning, Project based learning focusing on the students' project-based learning construction based on their own interests which allowed the students to increase creative project. This can be applied for other courses in order to plan activities to develop students' work process skills and creative skills. We also recommend that researchers carefully consider the design of lesson plans in accordance with all of 11 KPIs to promote students' creative thinking skills.

Alterations in phosphorylated cyclic adenosine monophosphate response element of binding protein activity: a pathway for fetal alcohol syndrome-related neurotoxicity.

PubMed

Roberson, Robin; Cameroni, Irene; Toso, Laura; Abebe, Daniel; Bissel, Stephanie; Spong, Catherine Y

2009-02-01

Fetal alcohol syndrome (FAS) is the leading cause of a spectrum of preventable nongenetic learning and behavioral disorders. In adult (FAS) mice, we measured phosphorylated cyclic adenosine monophosphate response element of binding protein (pCREB) staining in hippocampal subregions to evaluate a possible mechanism underlying FAS learning deficits. Pregnant C57BL6/J mice were treated on gestational day 8 with alcohol or control (saline). After learning assessment, the offspring were perfused for immunohistochemistry and brain sections probed using SER 133 pCREB antibody. Relative staining density was assessed using National Institutes of Health Image software. Statistical analysis included analysis of variance with P < .05 considered significant. In all hippocampal subregions, pCREB staining was greater in the control animals than in the alcohol-treated group (P < or = .0001). In utero alcohol exposure decreased pCREB activity in hippocampal subregions of adult mice. The dentate gyrus had the most robust cumulative decrease in pCREB staining, suggesting FAS adult learning deficits may correlate to enhanced dentate gyrus neurodegeneration.

Information-theoretic approach to interactive learning

NASA Astrophysics Data System (ADS)

Still, S.

2009-01-01

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.

The role of reference in cross-situational word learning.

PubMed

Wang, Felix Hao; Mintz, Toben H

2018-01-01

Word learning involves massive ambiguity, since in a particular encounter with a novel word, there are an unlimited number of potential referents. One proposal for how learners surmount the problem of ambiguity is that learners use cross-situational statistics to constrain the ambiguity: When a word and its referent co-occur across multiple situations, learners will associate the word with the correct referent. Yu and Smith (2007) propose that these co-occurrence statistics are sufficient for word-to-referent mapping. Alternative accounts hold that co-occurrence statistics alone are insufficient to support learning, and that learners are further guided by knowledge that words are referential (e.g., Waxman & Gelman, 2009). However, no behavioral word learning studies we are aware of explicitly manipulate subjects' prior assumptions about the role of the words in the experiments in order to test the influence of these assumptions. In this study, we directly test whether, when faced with referential ambiguity, co-occurrence statistics are sufficient for word-to-referent mappings in adult word-learners. Across a series of cross-situational learning experiments, we varied the degree to which there was support for the notion that the words were referential. At the same time, the statistical information about the words' meanings was held constant. When we overrode support for the notion that words were referential, subjects failed to learn the word-to-referent mappings, but otherwise they succeeded. Thus, cross-situational statistics were useful only when learners had the goal of discovering mappings between words and referents. We discuss the implications of these results for theories of word learning in children's language acquisition. Copyright © 2017 Elsevier B.V. All rights reserved.

Decoding the future from past experience: learning shapes predictions in early visual cortex.

PubMed

Luft, Caroline D B; Meeson, Alan; Welchman, Andrew E; Kourtzi, Zoe

2015-05-01

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex. Copyright © 2015 the American Physiological Society.

Machine Learning Methods for Attack Detection in the Smart Grid.

PubMed

Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

2016-08-01

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

An appeal to undergraduate wildlife programs: send scientists to learn statistics

USGS Publications Warehouse

Kendall, W.L.; Gould, W.R.

2002-01-01

Undergraduate wildlife students taking introductory statistics too often are poorly prepared and insufficiently motivated to learn statistics. We have also encountered too many wildlife professionals, even with graduate degrees, who exhibit an aversion to thinking statistically, either relying too heavily on statisticians or avoiding statistics altogether. We believe part of the reason for these problems is that wildlife majors are insufficiently grounded in the scientific method and analytical thinking before they take statistics. We suggest that a partial solution is to assure wildlife majors are trained in the scientific method at the very beginning of their academic careers.

Towards an explicit account of implicit learning.

PubMed

Forkstam, Christian; Petersson, Karl Magnus

2005-08-01

The human brain supports acquisition mechanisms that can extract structural regularities implicitly from experience without the induction of an explicit model. Reber defined the process by which an individual comes to respond appropriately to the statistical structure of the input ensemble as implicit learning. He argued that the capacity to generalize to new input is based on the acquisition of abstract representations that reflect underlying structural regularities in the acquisition input. We focus this review of the implicit learning literature on studies published during 2004 and 2005. We will not review studies of repetition priming ('implicit memory'). Instead we focus on two commonly used experimental paradigms: the serial reaction time task and artificial grammar learning. Previous comprehensive reviews can be found in Seger's 1994 article and the Handbook of Implicit Learning. Emerging themes include the interaction between implicit and explicit processes, the role of the medial temporal lobe, developmental aspects of implicit learning, age-dependence, the role of sleep and consolidation. The attempts to characterize the interaction between implicit and explicit learning are promising although not well understood. The same can be said about the role of sleep and consolidation. Despite the fact that lesion studies have relatively consistently suggested that the medial temporal lobe memory system is not necessary for implicit learning, a number of functional magnetic resonance studies have reported medial temporal lobe activation in implicit learning. This issue merits further research. Finally, the clinical relevance of implicit learning remains to be determined.

A canonical neural mechanism for behavioral variability

NASA Astrophysics Data System (ADS)

Darshan, Ran; Wood, William E.; Peters, Susan; Leblois, Arthur; Hansel, David

2017-05-01

The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5-6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these `universal' statistics.

Effect of Calendula officinalis hydroalcoholic extract on passive avoidance learning and memory in streptozotocin-induced diabetic rats

PubMed Central

Moradkhani, Shirin; Salehi, Iraj; Abdolmaleki, Somayeh; Komaki, Alireza

2015-01-01

Background: Medicinal plants, owing to their different mechanisms such as antioxidants effects, may improve learning and memory impairments in diabetic rats. Calendula officinalis (CO), has a significant antioxidant activity. Aims: To examine the effect of hydroalcoholic extract of CO on passive avoidance learning (PAL) and memory in streptozotocin (STZ)-induced diabetic male rats. Settings and Design: A total of 32 adult male Wistar rats were randomly allocated to four groups: Control, diabetic, control + extract of CO and diabetic control + extract of CO groups with free access to regular rat diet. Subjects and Methods: Diabetes in diabetic rats was induced by single intraperitoneal injection of 60 mg/kg STZ. After confirmation of diabetes, oral administration of 300 mg/kg CO extract to extract-treated groups have been done. PAL was tested 8 weeks after onset of treatment, and blood glucose and body weight were measured in all groups at the beginning and end of the experiment. Statistical Analysis Used: The statistical analysis of data was performed by ANOVA followed by least significant difference post-hoc analysis. Results: Diabetes decreased learning and memory. Effect of CO extract in retention test (after 24 and 48 h) has been shown a significant decrease in step-through latency and increase in time spent in the dark compartment part. Also the extract partially improved hyperglycemia and reduced body weight. Conclusion: Taken together, CO extract can improve PAL and memory impairments in STZ-diabetic rats. This improvement may be due to its antioxidant, anticholinergic activities or its power to reduce hyperglycemia. PMID:26120230

Comparison of Different Instructional Multimedia Designs for Improving Student Science-Process Skill Learning

NASA Astrophysics Data System (ADS)

Chien, Yu-Ta; Chang, Chun-Yen

2012-02-01

This study developed three forms of computer-based multimedia, including Static Graphics (SG), Simple Learner-Pacing Animation (SLPA), and Full Learner-Pacing Animation (FLPA), to assist students in learning topographic measuring. The interactive design of FLPA allowed students to physically manipulate the virtual measuring mechanism, rather than passively observe dynamic or static images. The students were randomly assigned to different multimedia groups. The results of a one-way ANOVA analysis indicated that (1) there was a significant difference with a large effect size ( f = .69) in mental effort ratings among three groups, and the post-hoc test indicated that FLPA imposed less cognitive load on students than did SG ( p = .007); (2) the differences of practical performance scores among groups reached the statistic significant level with a large effect size ( f = .76), and the post-hoc test indicated that FLPA fostered better learning outcomes than both SLPA and SG ( p = .004 and p = .05, respectively); (3) the difference in instructional efficiency that was computed by the z-score combination of students' mental effort ratings and practical performance scores among the three groups obtained the statistic significant level with a large effect size ( f = .79), and the post-hoc test indicated that FLPA brought students higher instructional efficiency than those of both SLPA and SG ( p = .01 and .005, respectively); (4) no significant effect was found in instructional time-spans between groups ( p = .637). Overall, FLPA was recommended as the best multimedia form to facilitate topographic measurement learning. The implications of instructional multimedia design were discussed from the perspective of cognitive load theory.

Side Effects of Being Blue: Influence of Sad Mood on Visual Statistical Learning

PubMed Central

Bertels, Julie; Demoulin, Catherine; Franco, Ana; Destrebecqz, Arnaud

2013-01-01

It is well established that mood influences many cognitive processes, such as learning and executive functions. Although statistical learning is assumed to be part of our daily life, as mood does, the influence of mood on statistical learning has never been investigated before. In the present study, a sad vs. neutral mood was induced to the participants through the listening of stories while they were exposed to a stream of visual shapes made up of the repeated presentation of four triplets, namely sequences of three shapes presented in a fixed order. Given that the inter-stimulus interval was held constant within and between triplets, the only cues available for triplet segmentation were the transitional probabilities between shapes. Direct and indirect measures of learning taken either immediately or 20 minutes after the exposure/mood induction phase revealed that participants learned the statistical regularities between shapes. Interestingly, although participants from the sad and neutral groups performed similarly in these tasks, subjective measures (confidence judgments taken after each trial) revealed that participants who experienced the sad mood induction showed increased conscious access to their statistical knowledge. These effects were not modulated by the time delay between the exposure/mood induction and the test phases. These results are discussed within the scope of the robustness principle and the influence of negative affects on processing style. PMID:23555797

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

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).

Cracking the Language Code: Neural Mechanisms Underlying Speech Parsing

PubMed Central

McNealy, Kristin; Mazziotta, John C.; Dapretto, Mirella

2013-01-01

Word segmentation, detecting word boundaries in continuous speech, is a critical aspect of language learning. Previous research in infants and adults demonstrated that a stream of speech can be readily segmented based solely on the statistical and speech cues afforded by the input. Using functional magnetic resonance imaging (fMRI), the neural substrate of word segmentation was examined on-line as participants listened to three streams of concatenated syllables, containing either statistical regularities alone, statistical regularities and speech cues, or no cues. Despite the participants’ inability to explicitly detect differences between the speech streams, neural activity differed significantly across conditions, with left-lateralized signal increases in temporal cortices observed only when participants listened to streams containing statistical regularities, particularly the stream containing speech cues. In a second fMRI study, designed to verify that word segmentation had implicitly taken place, participants listened to trisyllabic combinations that occurred with different frequencies in the streams of speech they just heard (“words,” 45 times; “partwords,” 15 times; “nonwords,” once). Reliably greater activity in left inferior and middle frontal gyri was observed when comparing words with partwords and, to a lesser extent, when comparing partwords with nonwords. Activity in these regions, taken to index the implicit detection of word boundaries, was positively correlated with participants’ rapid auditory processing skills. These findings provide a neural signature of on-line word segmentation in the mature brain and an initial model with which to study developmental changes in the neural architecture involved in processing speech cues during language learning. PMID:16855090

Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients.

PubMed

Liu, Nehemiah T; Holcomb, John B; Wade, Charles E; Batchinsky, Andriy I; Cancio, Leopoldo C; Darrah, Mark I; Salinas, José

2014-02-01

Accurate and effective diagnosis of actual injury severity can be problematic in trauma patients. Inherent physiologic compensatory mechanisms may prevent accurate diagnosis and mask true severity in many circumstances. The objective of this project was the development and validation of a multiparameter machine learning algorithm and system capable of predicting the need for life-saving interventions (LSIs) in trauma patients. Statistics based on means, slopes, and maxima of various vital sign measurements corresponding to 79 trauma patient records generated over 110,000 feature sets, which were used to develop, train, and implement the system. Comparisons among several machine learning models proved that a multilayer perceptron would best implement the algorithm in a hybrid system consisting of a machine learning component and basic detection rules. Additionally, 295,994 feature sets from 82 h of trauma patient data showed that the system can obtain 89.8 % accuracy within 5 min of recorded LSIs. Use of machine learning technologies combined with basic detection rules provides a potential approach for accurately assessing the need for LSIs in trauma patients. The performance of this system demonstrates that machine learning technology can be implemented in a real-time fashion and potentially used in a critical care environment.

Translating visual information into action predictions: Statistical learning in action and nonaction contexts.

PubMed

Monroy, Claire D; Gerson, Sarah A; Hunnius, Sabine

2018-05-01

Humans are sensitive to the statistical regularities in action sequences carried out by others. In the present eyetracking study, we investigated whether this sensitivity can support the prediction of upcoming actions when observing unfamiliar action sequences. In two between-subjects conditions, we examined whether observers would be more sensitive to statistical regularities in sequences performed by a human agent versus self-propelled 'ghost' events. Secondly, we investigated whether regularities are learned better when they are associated with contingent effects. Both implicit and explicit measures of learning were compared between agent and ghost conditions. Implicit learning was measured via predictive eye movements to upcoming actions or events, and explicit learning was measured via both uninstructed reproduction of the action sequences and verbal reports of the regularities. The findings revealed that participants, regardless of condition, readily learned the regularities and made correct predictive eye movements to upcoming events during online observation. However, different patterns of explicit-learning outcomes emerged following observation: Participants were most likely to re-create the sequence regularities and to verbally report them when they had observed an actor create a contingent effect. These results suggest that the shift from implicit predictions to explicit knowledge of what has been learned is facilitated when observers perceive another agent's actions and when these actions cause effects. These findings are discussed with respect to the potential role of the motor system in modulating how statistical regularities are learned and used to modify behavior.

Effects of Matching Multiple Memory Strategies with Computer-Assisted Instruction on Students' Statistics Learning Achievement

ERIC Educational Resources Information Center

Liao, Ying; Lin, Wen-He

2016-01-01

In the era when digitalization is pursued, numbers are the major medium of information performance and statistics is the primary instrument to interpret and analyze numerical information. For this reason, the cultivation of fundamental statistical literacy should be a key in the learning area of mathematics at the stage of compulsory education.…

For the Love of Statistics: Appreciating and Learning to Apply Experimental Analysis and Statistics through Computer Programming Activities

ERIC Educational Resources Information Center

Mascaró, Maite; Sacristán, Ana Isabel; Rufino, Marta M.

2016-01-01

For the past 4 years, we have been involved in a project that aims to enhance the teaching and learning of experimental analysis and statistics, of environmental and biological sciences students, through computational programming activities (using R code). In this project, through an iterative design, we have developed sequences of R-code-based…

The Effect of Project Based Learning on the Statistical Literacy Levels of Student 8th Grade

ERIC Educational Resources Information Center

Koparan, Timur; Güven, Bülent

2014-01-01

This study examines the effect of project based learning on 8th grade students' statistical literacy levels. A performance test was developed for this aim. Quasi-experimental research model was used in this article. In this context, the statistics were taught with traditional method in the control group and it was taught using project based…

The Effect on the 8th Grade Students' Attitude towards Statistics of Project Based Learning

ERIC Educational Resources Information Center

Koparan, Timur; Güven, Bülent

2014-01-01

This study investigates the effect of the project based learning approach on 8th grade students' attitude towards statistics. With this aim, an attitude scale towards statistics was developed. Quasi-experimental research model was used in this study. Following this model in the control group the traditional method was applied to teach statistics…

Blended Learning: The Perceptions of First-Year Geography Students

ERIC Educational Resources Information Center

Mitchell, Phillipa; Forer, Pip

2010-01-01

Focusing on "Digital Worlds", a first-year geography blended learning course at the University of Auckland, this paper gives voice to the students, examining how they perceived e-learning versus traditional learning mechanisms; how e-learning mechanisms have affected their learning behaviour; and why certain e-learning mechanisms offered…

A study of students' learning styles and mathematics anxiety amongst form four students in Kerian Perak

NASA Astrophysics Data System (ADS)

Esa, Suraya; Mohamed, Nurul Akmal

2017-05-01

This study aims to identify the relationship between students' learning styles and mathematics anxiety amongst Form Four students in Kerian, Perak. The study involves 175 Form Four students as respondents. The instrument which is used to assess the students' learning styles and mathematic anxiety is adapted from the Grasha's Learning Styles Inventory and the Mathematics Anxiety Scale (MAS) respectively. The types of learning styles used are independent, avoidant, collaborative, dependent, competitive and participant. The collected data is processed by SPSS (Statistical Packages for Social Sciences 16.0). The data is analysed by using descriptive statistics and inferential statistics that include t-test and Pearson correlation. The results show that majority of the students adopt collaborative learning style and the students have moderate level of mathematics anxiety. Moreover, it is found that there is significant difference between learning style avoidant, collaborative, dependent and participant based on gender. Amongst all students' learning style, there exists a weak but significant correlation between avoidant, independent and participant learning style and mathematics anxiety. It is very important for the teachers need to be concerned about the effects of learning styles on mathematics anxiety. Therefore, the teachers should understand mathematics anxiety and implement suitable learning strategies in order for the students to overcome their mathematics anxiety.

Content-Based VLE Designs Improve Learning Efficiency in Constructivist Statistics Education

PubMed Central

Wessa, Patrick; De Rycker, Antoon; Holliday, Ian Edward

2011-01-01

Background We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific–purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. Objectives The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Methods Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. Results The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a content–based design outperforms the traditional VLE–based design. PMID:21998652

Laparoscopic varicocelectomy: virtual reality training and learning curve.

PubMed

Wang, Zheng; Ni, Yuhua; Zhang, Yinan; Jin, Xunbo; Xia, Qinghua; Wang, Hanbo

2014-01-01

To explore the role that virtual reality training might play in the learning curve of laparoscopic varicocelectomy. A total of 1326 laparoscopic varicocelectomy cases performed by 16 participants from July 2005 to June 2012 were retrospectively analyzed. The participants were divided into 2 groups: group A was trained by laparoscopic trainer boxes; group B was trained by a virtual reality training course preoperatively. The operation time curves were drafted, and the learning, improving, and platform stages were divided and statistically confirmed. The operation time and number of cases in the learning and improving stages of both groups were compared. Testicular artery sparing failure and postoperative hydroceles rate were statistically analyzed for the confirmation of the learning curve. The learning curve of laparoscopic varicocelectomy was 15 cases, and with 14 cases more, it came into the platform stage. The number of cases for the learning stages of both groups showed no statistical difference (P=.49), but the operation time of group B for the learning stage was less than that of group A (P<.00001). The number of cases of group B for the improving stage was significantly less than that of group A (P=.005), but the operation time of both groups in the improving stage showed no difference (P=.30). The difference of testicular artery sparing failure rates among these 3 stages was proved significant (P<.0001), the postoperative hydroceles rate showed no statistical difference (P=.60). The virtual reality training shortened the operation time in the learning stage and hastened the trainees' steps in the improving stage, but did not shorten the learning curve as expected to.

Machine learning for neuroimaging with scikit-learn.

PubMed

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Machine learning for neuroimaging with scikit-learn

PubMed Central

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain. PMID:24600388

The Relationship Between Procrastination, Learning Strategies and Statistics Anxiety Among Iranian College Students: A Canonical Correlation Analysis

PubMed Central

Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali

2012-01-01

Objective: Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. Methods: A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Results: Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. Conclusion: These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction. PMID:24644468

Travelogue--a newcomer encounters statistics and the computer.

PubMed

Bruce, Peter

2011-11-01

Computer-intensive methods have revolutionized statistics, giving rise to new areas of analysis and expertise in predictive analytics, image processing, pattern recognition, machine learning, genomic analysis, and more. Interest naturally centers on the new capabilities the computer allows the analyst to bring to the table. This article, instead, focuses on the account of how computer-based resampling methods, with their relative simplicity and transparency, enticed one individual, untutored in statistics or mathematics, on a long journey into learning statistics, then teaching it, then starting an education institution.

Charge Density Engineering: A Feasibility Study

DTIC Science & Technology

2013-11-22

15. Statistical Learning Guided Design of Materials Fritz Haber Institute – Theory Group Berlin , Germany June 17th 2013 16...Technology San Diego, CA June 6th 2013 15. Statistical Learning Guided Design of Materials Fritz Haber Institute – Theory Group Berlin

Comparisons between physics-based, engineering, and statistical learning models for outdoor sound propagation.

PubMed

Hart, Carl R; Reznicek, Nathan J; Wilson, D Keith; Pettit, Chris L; Nykaza, Edward T

2016-05-01

Many outdoor sound propagation models exist, ranging from highly complex physics-based simulations to simplified engineering calculations, and more recently, highly flexible statistical learning methods. Several engineering and statistical learning models are evaluated by using a particular physics-based model, namely, a Crank-Nicholson parabolic equation (CNPE), as a benchmark. Narrowband transmission loss values predicted with the CNPE, based upon a simulated data set of meteorological, boundary, and source conditions, act as simulated observations. In the simulated data set sound propagation conditions span from downward refracting to upward refracting, for acoustically hard and soft boundaries, and low frequencies. Engineering models used in the comparisons include the ISO 9613-2 method, Harmonoise, and Nord2000 propagation models. Statistical learning methods used in the comparisons include bagged decision tree regression, random forest regression, boosting regression, and artificial neural network models. Computed skill scores are relative to sound propagation in a homogeneous atmosphere over a rigid ground. Overall skill scores for the engineering noise models are 0.6%, -7.1%, and 83.8% for the ISO 9613-2, Harmonoise, and Nord2000 models, respectively. Overall skill scores for the statistical learning models are 99.5%, 99.5%, 99.6%, and 99.6% for bagged decision tree, random forest, boosting, and artificial neural network regression models, respectively.

Auditory Magnetoencephalographic Frequency-Tagged Responses Mirror the Ongoing Segmentation Processes Underlying Statistical Learning.

PubMed

Farthouat, Juliane; Franco, Ana; Mary, Alison; Delpouve, Julie; Wens, Vincent; Op de Beeck, Marc; De Tiège, Xavier; Peigneux, Philippe

2017-03-01

Humans are highly sensitive to statistical regularities in their environment. This phenomenon, usually referred as statistical learning, is most often assessed using post-learning behavioural measures that are limited by a lack of sensibility and do not monitor the temporal dynamics of learning. In the present study, we used magnetoencephalographic frequency-tagged responses to investigate the neural sources and temporal development of the ongoing brain activity that supports the detection of regularities embedded in auditory streams. Participants passively listened to statistical streams in which tones were grouped as triplets, and to random streams in which tones were randomly presented. Results show that during exposure to statistical (vs. random) streams, tritone frequency-related responses reflecting the learning of regularities embedded in the stream increased in the left supplementary motor area and left posterior superior temporal sulcus (pSTS), whereas tone frequency-related responses decreased in the right angular gyrus and right pSTS. Tritone frequency-related responses rapidly developed to reach significance after 3 min of exposure. These results suggest that the incidental extraction of novel regularities is subtended by a gradual shift from rhythmic activity reflecting individual tone succession toward rhythmic activity synchronised with triplet presentation, and that these rhythmic processes are subtended by distinct neural sources.

Promoting Children's Healthy Eating in Obesogenic Environments: Lessons Learned from the Rat

PubMed Central

Birch, Leann L.; Anzman-Frasca, Stephanie

2011-01-01

Current statistics on children's eating patterns and obesity rates are consistent with the idea that genetic taste predispositions, traditional feeding practices, and the obesogenic environment combine to increase the likelihood of unhealthy outcomes in many individuals. In this paper, we focus on one particular level of analysis through which this unhealthy combination of factors may begin to be disassembled: children's learning about food and flavors. Much of the research on children's learning about food and flavors has been inspired by the animal literature, which has a long history of carefully controlled studies elucidating the mechanisms through which rats and other animals learn to prefer and avoid foods and flavors. This literature provides many clues as to the processes by which learning paradigms may be used to encourage the intake of healthy foods, altering the implicit learning of obesogenic eating patterns that is likely to occur without intervention in the current environment. Overall, the implications of the literature are that children should be repeatedly exposed to a variety of flavors early in life, and that new flavors should be paired with already-liked flavors and positive contexts. This message is consistent with recent research results from our laboratory, showing that familiarization and associative learning paradigms may be used to increase young children's acceptance of, preference for, and intake of previously-unfamiliar, healthy foods. PMID:21620880

Enhancing Learning in Statistics Classes Through The Use of Concrete Historical Examples: The Space Shuttle Challenger, Pearl Harbor, and the RMS Titanic.

ERIC Educational Resources Information Center

Schumm, Walter R.; Webb, Farrell J.; Castelo, Carlos S.; Akagi, Cynthia G.; Jensen, Erick J.; Ditto, Rose M.; Spencer Carver, Elaine; Brown, Beverlyn F.

2002-01-01

Discusses the use of historical events as examples for teaching college level statistics courses. Focuses on examples of the space shuttle Challenger, Pearl Harbor (Hawaii), and the RMS Titanic. Finds real life examples can bridge a link to short term experiential learning and provide a means for long term understanding of statistics. (KDR)

What Does Research Suggest about the Teaching and Learning of Introductory Statistics at the College Level? A Review of the Literature

ERIC Educational Resources Information Center

Zieffler, Andrew; Garfield, Joan; Alt, Shirley; Dupuis, Danielle; Holleque, Kristine; Chang, Beng

2008-01-01

Since the first studies on the teaching and learning of statistics appeared in the research literature, the scholarship in this area has grown dramatically. Given the diversity of disciplines, methodology, and orientation of the studies that may be classified as "statistics education research," summarizing and critiquing this body of work for…

Cultural Diversity and Best Practices in the Teaching and Learning of Statistics: A Faculty Perspective from A Historically Black College/University (HBCU)

ERIC Educational Resources Information Center

Whaley, Arthur L.

2017-01-01

The literature on the teaching and learning of statistics tend not to address issues of cultural diversity. Twenty-nine students enrolled in a statistics course at a historically Black college/university (HBCU) were the focus of this pilot study. Using structural equation modeling (SEM), the study tested models of the effects of writing…

The neural correlates of statistical learning in a word segmentation task: An fMRI study

PubMed Central

Karuza, Elisabeth A.; Newport, Elissa L.; Aslin, Richard N.; Starling, Sarah J.; Tivarus, Madalina E.; Bavelier, Daphne

2013-01-01

Functional magnetic resonance imaging (fMRI) was used to assess neural activation as participants learned to segment continuous streams of speech containing syllable sequences varying in their transitional probabilities. Speech streams were presented in four runs, each followed by a behavioral test to measure the extent of learning over time. Behavioral performance indicated that participants could discriminate statistically coherent sequences (words) from less coherent sequences (partwords). Individual rates of learning, defined as the difference in ratings for words and partwords, were used as predictors of neural activation to ask which brain areas showed activity associated with these measures. Results showed significant activity in the pars opercularis and pars triangularis regions of the left inferior frontal gyrus (LIFG). The relationship between these findings and prior work on the neural basis of statistical learning is discussed, and parallels to the frontal/subcortical network involved in other forms of implicit sequence learning are considered. PMID:23312790

Drosophila Courtship Conditioning As a Measure of Learning and Memory.

PubMed

Koemans, Tom S; Oppitz, Cornelia; Donders, Rogier A T; van Bokhoven, Hans; Schenck, Annette; Keleman, Krystyna; Kramer, Jamie M

2017-06-05

Many insights into the molecular mechanisms underlying learning and memory have been elucidated through the use of simple behavioral assays in model organisms such as the fruit fly, Drosophila melanogaster. Drosophila is useful for understanding the basic neurobiology underlying cognitive deficits resulting from mutations in genes associated with human cognitive disorders, such as intellectual disability (ID) and autism. This work describes a methodology for testing learning and memory using a classic paradigm in Drosophila known as courtship conditioning. Male flies court females using a distinct pattern of easily recognizable behaviors. Premated females are not receptive to mating and will reject the male's copulation attempts. In response to this rejection, male flies reduce their courtship behavior. This learned reduction in courtship behavior is measured over time, serving as an indicator of learning and memory. The basic numerical output of this assay is the courtship index (CI), which is defined as the percentage of time that a male spends courting during a 10 min interval. The learning index (LI) is the relative reduction of CI in flies that have been exposed to a premated female compared to naïve flies with no previous social encounters. For the statistical comparison of LIs between genotypes, a randomization test with bootstrapping is used. To illustrate how the assay can be used to address the role of a gene relating to learning and memory, the pan-neuronal knockdown of Dihydroxyacetone phosphate acyltransferase (Dhap-at) was characterized here. The human ortholog of Dhap-at, glyceronephosphate O-acyltransferase (GNPT), is involved in rhizomelic chondrodysplasia punctata type 2, an autosomal-recessive syndrome characterized by severe ID. Using the courtship conditioning assay, it was determined that Dhap-at is required for long-term memory, but not for short-term memory. This result serves as a basis for further investigation of the underlying molecular mechanisms.

Statistical Learning and Dyslexia: A Systematic Review

ERIC Educational Resources Information Center

Schmalz, Xenia; Altoè, Gianmarco; Mulatti, Claudio

2017-01-01

The existing literature on developmental dyslexia (hereafter: dyslexia) often focuses on isolating cognitive skills which differ across dyslexic and control participants. Among potential correlates, previous research has studied group differences between dyslexic and control participants in performance on statistical learning tasks. A statistical…

Word Recognition Reflects Dimension-Based Statistical Learning

ERIC Educational Resources Information Center

Idemaru, Kaori; Holt, Lori L.

2011-01-01

Speech processing requires sensitivity to long-term regularities of the native language yet demands listeners to flexibly adapt to perturbations that arise from talker idiosyncrasies such as nonnative accent. The present experiments investigate whether listeners exhibit "dimension-based statistical learning" of correlations between acoustic…

Dental hygiene students' perceptions of distance learning: do they change over time?

PubMed

Sledge, Rhonda; Vuk, Jasna; Long, Susan

2014-02-01

The University of Arkansas for Medical Sciences dental hygiene program established a distant site where the didactic curriculum was broadcast via interactive video from the main campus to the distant site, supplemented with on-line learning via Blackboard. This study compared the perceptions of students towards distance learning as they progressed through the 21 month curriculum. Specifically, the study sought to answer the following questions: Is there a difference in the initial perceptions of students on the main campus and at the distant site toward distance learning? Do students' perceptions change over time with exposure to synchronous distance learning over the course of the curriculum? All 39 subjects were women between the ages of 20 and 35 years. Of the 39 subjects, 37 were Caucasian and 2 were African-American. A 15-question Likert scale survey was administered at 4 different periods during the 21 month program to compare changes in perceptions toward distance learning as students progressed through the program. An independent sample t-test and ANOVA were utilized for statistical analysis. At the beginning of the program, independent samples t-test revealed that students at the main campus (n=34) perceived statistically significantly higher effectiveness of distance learning than students at the distant site (n=5). Repeated measures of ANOVA revealed that perceptions of students at the main campus on effectiveness and advantages of distance learning statistically significantly decreased whereas perceptions of students at distant site statistically significantly increased over time. Distance learning in the dental hygiene program was discussed, and replication of the study with larger samples of students was recommended.

Statistical Regularities Attract Attention when Task-Relevant.

PubMed

Alamia, Andrea; Zénon, Alexandre

2016-01-01

Visual attention seems essential for learning the statistical regularities in our environment, a process known as statistical learning. However, how attention is allocated when exploring a novel visual scene whose statistical structure is unknown remains unclear. In order to address this question, we investigated visual attention allocation during a task in which we manipulated the conditional probability of occurrence of colored stimuli, unbeknown to the subjects. Participants were instructed to detect a target colored dot among two dots moving along separate circular paths. We evaluated implicit statistical learning, i.e., the effect of color predictability on reaction times (RTs), and recorded eye position concurrently. Attention allocation was indexed by comparing the Mahalanobis distance between the position, velocity and acceleration of the eyes and the two colored dots. We found that learning the conditional probabilities occurred very early during the course of the experiment as shown by the fact that, starting already from the first block, predictable stimuli were detected with shorter RT than unpredictable ones. In terms of attentional allocation, we found that the predictive stimulus attracted gaze only when it was informative about the occurrence of the target but not when it predicted the occurrence of a task-irrelevant stimulus. This suggests that attention allocation was influenced by regularities only when they were instrumental in performing the task. Moreover, we found that the attentional bias towards task-relevant predictive stimuli occurred at a very early stage of learning, concomitantly with the first effects of learning on RT. In conclusion, these results show that statistical regularities capture visual attention only after a few occurrences, provided these regularities are instrumental to perform the task.

Attitudes toward statistics in medical postgraduates: measuring, evaluating and monitoring.

PubMed

Zhang, Yuhai; Shang, Lei; Wang, Rui; Zhao, Qinbo; Li, Chanjuan; Xu, Yongyong; Su, Haixia

2012-11-23

In medical training, statistics is considered a very difficult course to learn and teach. Current studies have found that students' attitudes toward statistics can influence their learning process. Measuring, evaluating and monitoring the changes of students' attitudes toward statistics are important. Few studies have focused on the attitudes of postgraduates, especially medical postgraduates. Our purpose was to understand current attitudes regarding statistics held by medical postgraduates and explore their effects on students' achievement. We also wanted to explore the influencing factors and the sources of these attitudes and monitor their changes after a systematic statistics course. A total of 539 medical postgraduates enrolled in a systematic statistics course completed the pre-form of the Survey of Attitudes Toward Statistics -28 scale, and 83 postgraduates were selected randomly from among them to complete the post-form scale after the course. Most medical postgraduates held positive attitudes toward statistics, but they thought statistics was a very difficult subject. The attitudes mainly came from experiences in a former statistical or mathematical class. Age, level of statistical education, research experience, specialty and mathematics basis may influence postgraduate attitudes toward statistics. There were significant positive correlations between course achievement and attitudes toward statistics. In general, student attitudes showed negative changes after completing a statistics course. The importance of student attitudes toward statistics must be recognized in medical postgraduate training. To make sure all students have a positive learning environment, statistics teachers should measure their students' attitudes and monitor their change of status during a course. Some necessary assistance should be offered for those students who develop negative attitudes.

Comparative analysis of positive and negative attitudes toward statistics

NASA Astrophysics Data System (ADS)

Ghulami, Hassan Rahnaward; Ab Hamid, Mohd Rashid; Zakaria, Roslinazairimah

2015-02-01

Many statistics lecturers and statistics education researchers are interested to know the perception of their students' attitudes toward statistics during the statistics course. In statistics course, positive attitude toward statistics is a vital because it will be encourage students to get interested in the statistics course and in order to master the core content of the subject matters under study. Although, students who have negative attitudes toward statistics they will feel depressed especially in the given group assignment, at risk for failure, are often highly emotional, and could not move forward. Therefore, this study investigates the students' attitude towards learning statistics. Six latent constructs have been the measurement of students' attitudes toward learning statistic such as affect, cognitive competence, value, difficulty, interest, and effort. The questionnaire was adopted and adapted from the reliable and validate instrument of Survey of Attitudes towards Statistics (SATS). This study is conducted among engineering undergraduate engineering students in the university Malaysia Pahang (UMP). The respondents consist of students who were taking the applied statistics course from different faculties. From the analysis, it is found that the questionnaire is acceptable and the relationships among the constructs has been proposed and investigated. In this case, students show full effort to master the statistics course, feel statistics course enjoyable, have confidence that they have intellectual capacity, and they have more positive attitudes then negative attitudes towards statistics learning. In conclusion in terms of affect, cognitive competence, value, interest and effort construct the positive attitude towards statistics was mostly exhibited. While negative attitudes mostly exhibited by difficulty construct.

Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior

PubMed Central

Gureckis, Todd M.; Love, Bradley C.

2009-01-01

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predict differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior. PMID:20396653

The Highly Adaptive Lasso Estimator

PubMed Central

Benkeser, David; van der Laan, Mark

2017-01-01

Estimation of a regression functions is a common goal of statistical learning. We propose a novel nonparametric regression estimator that, in contrast to many existing methods, does not rely on local smoothness assumptions nor is it constructed using local smoothing techniques. Instead, our estimator respects global smoothness constraints by virtue of falling in a class of right-hand continuous functions with left-hand limits that have variation norm bounded by a constant. Using empirical process theory, we establish a fast minimal rate of convergence of our proposed estimator and illustrate how such an estimator can be constructed using standard software. In simulations, we show that the finite-sample performance of our estimator is competitive with other popular machine learning techniques across a variety of data generating mechanisms. We also illustrate competitive performance in real data examples using several publicly available data sets. PMID:29094111

Helping Students to Climb the Mountain: A Study to Inform the Development of a Resource to Improve the Learning of Statistics in Psychology

ERIC Educational Resources Information Center

Davies, Emma L.; Morys-Carter, Wakefield L.; Paltoglou, Aspasia E.

2015-01-01

Students often struggle with learning about statistics, which encompass a large proportion of a psychology degree. This pilot study explored how first- and final-year students reflected on their experiences of being taught this topic, in order to identify needs that could be addressed in a project to improve their learning. First-year students…

The Effects of Using a Wiki on Student Engagement and Learning of Report Writing Skills in a University Statistics Course

ERIC Educational Resources Information Center

Neumann, David L.; Hood, Michelle

2009-01-01

A wiki was used as part of a blended learning approach to promote collaborative learning among students in a first year university statistics class. One group of students analysed a data set and communicated the results by jointly writing a practice report using a wiki. A second group analysed the same data but communicated the results in a…

Incremental Implicit Learning of Bundles of Statistical Patterns

PubMed Central

Qian, Ting; Jaeger, T. Florian; Aslin, Richard N.

2016-01-01

Forming an accurate representation of a task environment often takes place incrementally as the information relevant to learning the representation only unfolds over time. This incremental nature of learning poses an important problem: it is usually unclear whether a sequence of stimuli consists of only a single pattern, or multiple patterns that are spliced together. In the former case, the learner can directly use each observed stimulus to continuously revise its representation of the task environment. In the latter case, however, the learner must first parse the sequence of stimuli into different bundles, so as to not conflate the multiple patterns. We created a video-game statistical learning paradigm and investigated 1) whether learners without prior knowledge of the existence of multiple “stimulus bundles” — subsequences of stimuli that define locally coherent statistical patterns — could detect their presence in the input, and 2) whether learners are capable of constructing a rich representation that encodes the various statistical patterns associated with bundles. By comparing human learning behavior to the predictions of three computational models, we find evidence that learners can handle both tasks successfully. In addition, we discuss the underlying reasons for why the learning of stimulus bundles occurs even when such behavior may seem irrational. PMID:27639552

Cognitive components underpinning the development of model-based learning.

PubMed

Potter, Tracey C S; Bryce, Nessa V; Hartley, Catherine A

2017-06-01

Reinforcement learning theory distinguishes "model-free" learning, which fosters reflexive repetition of previously rewarded actions, from "model-based" learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9-25, we examined whether the abilities to infer sequential regularities in the environment ("statistical learning"), maintain information in an active state ("working memory") and integrate distant concepts to solve problems ("fluid reasoning") predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Evaluation of undergraduate nursing students' attitudes towards statistics courses, before and after a course in applied statistics.

PubMed

Hagen, Brad; Awosoga, Olu; Kellett, Peter; Dei, Samuel Ofori

2013-09-01

Undergraduate nursing students must often take a course in statistics, yet there is scant research to inform teaching pedagogy. The objectives of this study were to assess nursing students' overall attitudes towards statistics courses - including (among other things) overall fear and anxiety, preferred learning and teaching styles, and the perceived utility and benefit of taking a statistics course - before and after taking a mandatory course in applied statistics. The authors used a pre-experimental research design (a one-group pre-test/post-test research design), by administering a survey to nursing students at the beginning and end of the course. The study was conducted at a University in Western Canada that offers an undergraduate Bachelor of Nursing degree. Participants included 104 nursing students, in the third year of a four-year nursing program, taking a course in statistics. Although students only reported moderate anxiety towards statistics, student anxiety about statistics had dropped by approximately 40% by the end of the course. Students also reported a considerable and positive change in their attitudes towards learning in groups by the end of the course, a potential reflection of the team-based learning that was used. Students identified preferred learning and teaching approaches, including the use of real-life examples, visual teaching aids, clear explanations, timely feedback, and a well-paced course. Students also identified preferred instructor characteristics, such as patience, approachability, in-depth knowledge of statistics, and a sense of humor. Unfortunately, students only indicated moderate agreement with the idea that statistics would be useful and relevant to their careers, even by the end of the course. Our findings validate anecdotal reports on statistics teaching pedagogy, although more research is clearly needed, particularly on how to increase students' perceptions of the benefit and utility of statistics courses for their nursing careers. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

A canonical neural mechanism for behavioral variability

PubMed Central

Darshan, Ran; Wood, William E.; Peters, Susan; Leblois, Arthur; Hansel, David

2017-01-01

The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5–6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these ‘universal' statistics. PMID:28530225

Impaired Statistical Learning in Developmental Dyslexia

ERIC Educational Resources Information Center

Gabay, Yafit; Thiessen, Erik D.; Holt, Lori L.

2015-01-01

Purpose: Developmental dyslexia (DD) is commonly thought to arise from phonological impairments. However, an emerging perspective is that a more general procedural learning deficit, not specific to phonological processing, may underlie DD. The current study examined if individuals with DD are capable of extracting statistical regularities across…

Statistical Learning of Two Artificial Languages Presented Successively: How Conscious?

PubMed Central

Franco, Ana; Cleeremans, Axel; Destrebecqz, Arnaud

2011-01-01

Statistical learning is assumed to occur automatically and implicitly, but little is known about the extent to which the representations acquired over training are available to conscious awareness. In this study, we focus on whether the knowledge acquired in a statistical learning situation is available to conscious control. Participants were first exposed to an artificial language presented auditorily. Immediately thereafter, they were exposed to a second artificial language. Both languages were composed of the same corpus of syllables and differed only in the transitional probabilities. We first determined that both languages were equally learnable (Experiment 1) and that participants could learn the two languages and differentiate between them (Experiment 2). Then, in Experiment 3, we used an adaptation of the Process-Dissociation Procedure (Jacoby, 1991) to explore whether participants could consciously manipulate the acquired knowledge. Results suggest that statistical information can be used to parse and differentiate between two different artificial languages, and that the resulting representations are available to conscious control. PMID:21960981

Statistical learning and the challenge of syntax: Beyond finite state automata

NASA Astrophysics Data System (ADS)

Elman, Jeff

2003-10-01

Over the past decade, it has been clear that even very young infants are sensitive to the statistical structure of language input presented to them, and use the distributional regularities to induce simple grammars. But can such statistically-driven learning also explain the acquisition of more complex grammar, particularly when the grammar includes recursion? Recent claims (e.g., Hauser, Chomsky, and Fitch, 2002) have suggested that the answer is no, and that at least recursion must be an innate capacity of the human language acquisition device. In this talk evidence will be presented that indicates that, in fact, statistically-driven learning (embodied in recurrent neural networks) can indeed enable the learning of complex grammatical patterns, including those that involve recursion. When the results are generalized to idealized machines, it is found that the networks are at least equivalent to Push Down Automata. Perhaps more interestingly, with limited and finite resources (such as are presumed to exist in the human brain) these systems demonstrate patterns of performance that resemble those in humans.

Theory-based Bayesian models of inductive learning and reasoning.

PubMed

Tenenbaum, Joshua B; Griffiths, Thomas L; Kemp, Charles

2006-07-01

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

Circuit mechanisms of sensorimotor learning

PubMed Central

Makino, Hiroshi; Hwang, Eun Jung; Hedrick, Nathan G.; Komiyama, Takaki

2016-01-01

SUMMARY The relationship between the brain and the environment is flexible, forming the foundation for our ability to learn. Here we review the current state of our understanding of the modifications in the sensorimotor pathway related to sensorimotor learning. We divide the process in three hierarchical levels with distinct goals: 1) sensory perceptual learning, 2) sensorimotor associative learning, and 3) motor skill learning. Perceptual learning optimizes the representations of important sensory stimuli. Associative learning and the initial phase of motor skill learning are ensured by feedback-based mechanisms that permit trial-and-error learning. The later phase of motor skill learning may primarily involve feedback-independent mechanisms operating under the classic Hebbian rule. With these changes under distinct constraints and mechanisms, sensorimotor learning establishes dedicated circuitry for the reproduction of stereotyped neural activity patterns and behavior. PMID:27883902

ON THE DYNAMICAL DERIVATION OF EQUILIBRIUM STATISTICAL MECHANICS

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

Prigogine, I.; Balescu, R.; Henin, F.

1960-12-01

Work on nonequilibrium statistical mechanics, which allows an extension of the kinetic proof to all results of equilibrium statistical mechanics involving a finite number of degrees of freedom, is summarized. As an introduction to the general N-body problem, the scattering theory in classical mechanics is considered. The general N-body problem is considered for the case of classical mechanics, quantum mechanics with Boltzmann statistics, and quantum mechanics including quantum statistics. Six basic diagrams, which describe the elementary processes of the dynamics of correlations, were obtained. (M.C.G.)

Aging and the Statistical Learning of Grammatical Form Classes

PubMed Central

Schwab, Jessica F.; Schuler, Kathryn D.; Stillman, Chelsea M.; Newport, Elissa L.; Howard, James H.; Howard, Darlene V.

2016-01-01

Language learners must place unfamiliar words into categories, often with few explicit indicators about when and how that word can be used grammatically. Reeder, Newport, and Aslin (2013) showed that college students can learn grammatical form classes from an artificial language by relying solely on distributional information (i.e., contextual cues in the input). Here, two experiments revealed that healthy older adults also show such statistical learning, though they are poorer than young at distinguishing grammatical from ungrammatical strings. This finding expands knowledge of which aspects of learning vary with aging, with potential implications for second language learning in late adulthood. PMID:27294711

Statistical learning algorithms for identifying contrasting tillage practices with landsat thematic mapper data

USDA-ARS?s Scientific Manuscript database

Tillage management practices have direct impact on water holding capacity, evaporation, carbon sequestration, and water quality. This study examines the feasibility of two statistical learning algorithms, such as Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM), for cla...

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

PubMed Central

Zamli, Kamal Z.; Din, Fakhrud; Bures, Miroslav

2018-01-01

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level. PMID:29771918

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem.

PubMed

Zamli, Kamal Z; Din, Fakhrud; Ahmed, Bestoun S; Bures, Miroslav

2018-01-01

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

Proposed mechanism for learning and memory erasure in a white-noise-driven sleeping cortex.

PubMed

Steyn-Ross, Moira L; Steyn-Ross, D A; Sleigh, J W; Wilson, M T; Wilcocks, Lara C

2005-12-01

Understanding the structure and purpose of sleep remains one of the grand challenges of neurobiology. Here we use a mean-field linearized theory of the sleeping cortex to derive statistics for synaptic learning and memory erasure. The growth in correlated low-frequency high-amplitude voltage fluctuations during slow-wave sleep (SWS) is characterized by a probability density function that becomes broader and shallower as the transition into rapid-eye-movement (REM) sleep is approached. At transition, the Shannon information entropy of the fluctuations is maximized. If we assume Hebbian-learning rules apply to the cortex, then its correlated response to white-noise stimulation during SWS provides a natural mechanism for a synaptic weight change that will tend to shut down reverberant neural activity. In contrast, during REM sleep the weights will evolve in a direction that encourages excitatory activity. These entropy and weight-change predictions lead us to identify the final portion of deep SWS that occurs immediately prior to transition into REM sleep as a time of enhanced erasure of labile memory. We draw a link between the sleeping cortex and Landauer's dissipation theorem for irreversible computing [R. Landauer, IBM J. Res. Devel. 5, 183 (1961)], arguing that because information erasure is an irreversible computation, there is an inherent entropy cost as the cortex transits from SWS into REM sleep.

Proposed mechanism for learning and memory erasure in a white-noise-driven sleeping cortex

NASA Astrophysics Data System (ADS)

Steyn-Ross, Moira L.; Steyn-Ross, D. A.; Sleigh, J. W.; Wilson, M. T.; Wilcocks, Lara C.

2005-12-01

Understanding the structure and purpose of sleep remains one of the grand challenges of neurobiology. Here we use a mean-field linearized theory of the sleeping cortex to derive statistics for synaptic learning and memory erasure. The growth in correlated low-frequency high-amplitude voltage fluctuations during slow-wave sleep (SWS) is characterized by a probability density function that becomes broader and shallower as the transition into rapid-eye-movement (REM) sleep is approached. At transition, the Shannon information entropy of the fluctuations is maximized. If we assume Hebbian-learning rules apply to the cortex, then its correlated response to white-noise stimulation during SWS provides a natural mechanism for a synaptic weight change that will tend to shut down reverberant neural activity. In contrast, during REM sleep the weights will evolve in a direction that encourages excitatory activity. These entropy and weight-change predictions lead us to identify the final portion of deep SWS that occurs immediately prior to transition into REM sleep as a time of enhanced erasure of labile memory. We draw a link between the sleeping cortex and Landauer’s dissipation theorem for irreversible computing [R. Landauer, IBM J. Res. Devel. 5, 183 (1961)], arguing that because information erasure is an irreversible computation, there is an inherent entropy cost as the cortex transits from SWS into REM sleep.

Active Learning in the Physics Classroom

NASA Astrophysics Data System (ADS)

Naron, Carol

Many students enter physics classes filled with misconceptions about physics concepts. Students tend to retain these misconceptions into their adult lives, even after physics instruction. Constructivist researchers have found that students gain understanding through their experiences. Researchers have also found that active learning practices increase conceptual understanding of introductory physics students. This project study sought to examine whether incorporating active learning practices in an advanced placement physics classroom increased conceptual understanding as measured by the force concept inventory (FCI). Physics students at the study site were given the FCI as both a pre- and posttest. Test data were analyzed using two different methods---a repeated-measures t test and the Hake gain method. The results of this research project showed that test score gains were statistically significant, as measured by the t test. The Hake gain results indicated a low (22.5%) gain for the class. The resulting project was a curriculum plan for teaching the mechanics portion of Advanced Placement (AP) physics B as well as several active learning classroom practices supported by the research. This project will allow AP physics teachers an opportunity to improve their curricular practices. Locally, the results of this project study showed that research participants gained understanding of physics concepts. Social change may occur as teachers implement active learning strategies, thus creating improved student understanding of physics concepts.

Student Perceptions of Online Homework Use for Formative Assessment of Learning in Organic Chemistry.

PubMed

Richards-Babb, Michelle; Curtis, Reagan; Georgieva, Zomitsa; Penn, John H

2015-11-10

Use of online homework as a formative assessment tool for organic chemistry coursework was examined. Student perceptions of online homework in terms of (i) its ranking relative to other course aspects, (ii) their learning of organic chemistry, and (iii) whether it improved their study habits and how students used it as a learning tool were investigated. Our students perceived the online homework as one of the more useful course aspects for learning organic chemistry content. We found a moderate and statistically significant correlation between online homework performance and final grade. Gender as a variable was ruled out since significant gender differences in overall attitude toward online homework use and course success rates were not found. Our students expressed relatively positive attitudes toward use of online homework with a majority indicating improved study habits (e.g., study in a more consistent manner). Our students used a variety of resources to remediate incorrect responses (e.g., class materials, general online materials, and help from others). However, 39% of our students admitted to guessing at times, instead of working to remediate incorrect responses. In large enrollment organic chemistry courses, online homework may act to bridge the student-instructor gap by providing students with a supportive mechanism for regulated learning of content.

Teaching meta-analysis using MetaLight.

PubMed

Thomas, James; Graziosi, Sergio; Higgins, Steve; Coe, Robert; Torgerson, Carole; Newman, Mark

2012-10-18

Meta-analysis is a statistical method for combining the results of primary studies. It is often used in systematic reviews and is increasingly a method and topic that appears in student dissertations. MetaLight is a freely available software application that runs simple meta-analyses and contains specific functionality to facilitate the teaching and learning of meta-analysis. While there are many courses and resources for meta-analysis available and numerous software applications to run meta-analyses, there are few pieces of software which are aimed specifically at helping those teaching and learning meta-analysis. Valuable teaching time can be spent learning the mechanics of a new software application, rather than on the principles and practices of meta-analysis. We discuss ways in which the MetaLight tool can be used to present some of the main issues involved in undertaking and interpreting a meta-analysis. While there are many software tools available for conducting meta-analysis, in the context of a teaching programme such software can require expenditure both in terms of money and in terms of the time it takes to learn how to use it. MetaLight was developed specifically as a tool to facilitate the teaching and learning of meta-analysis and we have presented here some of the ways it might be used in a training situation.

Guidelines for Assessment and Instruction in Statistics Education (GAISE): extending GAISE into nursing education.

PubMed

Hayat, Matthew J

2014-04-01

Statistics coursework is usually a core curriculum requirement for nursing students at all degree levels. The American Association of Colleges of Nursing (AACN) establishes curriculum standards for academic nursing programs. However, the AACN provides little guidance on statistics education and does not offer standardized competency guidelines or recommendations about course content or learning objectives. Published standards may be used in the course development process to clarify course content and learning objectives. This article includes suggestions for implementing and integrating recommendations given in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) report into statistics education for nursing students. Copyright 2014, SLACK Incorporated.

Pure perceptual-based learning of second-, third-, and fourth-order sequential probabilities.

PubMed

Remillard, Gilbert

2011-07-01

There is evidence that sequence learning in the traditional serial reaction time task (SRTT), where target location is the response dimension, and sequence learning in the perceptual SRTT, where target location is not the response dimension, are handled by different mechanisms. The ability of the latter mechanism to learn sequential contingencies that can be learned by the former mechanism was examined. Prior research has established that people can learn second-, third-, and fourth-order probabilities in the traditional SRTT. The present study reveals that people can learn such probabilities in the perceptual SRTT. This suggests that the two mechanisms may have similar architectures. A possible neural basis of the two mechanisms is discussed.

Comparison between project-based learning and discovery learning toward students' metacognitive strategies on global warming concept

NASA Astrophysics Data System (ADS)

Tumewu, Widya Anjelia; Wulan, Ana Ratna; Sanjaya, Yayan

2017-05-01

The purpose of this study was to know comparing the effectiveness of learning using Project-based learning (PjBL) and Discovery Learning (DL) toward students metacognitive strategies on global warming concept. A quasi-experimental research design with a The Matching-Only Pretest-Posttest Control Group Design was used in this study. The subjects were students of two classes 7th grade of one of junior high school in Bandung City, West Java of 2015/2016 academic year. The study was conducted on two experimental class, that were project-based learning treatment on the experimental class I and discovery learning treatment was done on the experimental class II. The data was collected through questionnaire to know students metacognitive strategies. The statistical analysis showed that there were statistically significant differences in students metacognitive strategies between project-based learning and discovery learning.

The visual system’s internal model of the world

PubMed Central

Lee, Tai Sing

2015-01-01

The Bayesian paradigm has provided a useful conceptual theory for understanding perceptual computation in the brain. While the detailed neural mechanisms of Bayesian inference are not fully understood, recent computational and neurophysiological works have illuminated the underlying computational principles and representational architecture. The fundamental insights are that the visual system is organized as a modular hierarchy to encode an internal model of the world, and that perception is realized by statistical inference based on such internal model. In this paper, I will discuss and analyze the varieties of representational schemes of these internal models and how they might be used to perform learning and inference. I will argue for a unified theoretical framework for relating the internal models to the observed neural phenomena and mechanisms in the visual cortex. PMID:26566294

"Flipping" the introductory clerkship in radiology: impact on medical student performance and perceptions.

PubMed

Belfi, Lily M; Bartolotta, Roger J; Giambrone, Ashley E; Davi, Caryn; Min, Robert J

2015-06-01

Among methods of "blended learning" (ie, combining online modules with in-class instruction), the "flipped classroom" involves student preclass review of material while reserving class time for interactive knowledge application. We integrated blended learning methodology in a "flipped" introductory clerkship in radiology, and assessed the impact of this approach on the student educational experience (performance and perception). In preparation for the "flipped clerkship," radiology faculty and residents created e-learning modules that were uploaded to an open-source website. The clerkship's 101 rising third-year medical students were exposed to different teaching methods during the course, such as blended learning, traditional lecture learning, and independent learning. Students completed precourse and postcourse knowledge assessments and surveys. Student knowledge improved overall as a result of taking the course. Blended learning achieved greater pretest to post-test improvement of high statistical significance (P value, .0060) compared to lecture learning alone. Blended learning also achieved greater pretest to post-test improvement of borderline statistical significance (P value, .0855) in comparison to independent learning alone. The difference in effectiveness of independent learning versus lecture learning was not statistically significant (P value, .2730). Student perceptions of the online modules used in blended learning portions of the course were very positive. They specifically enjoyed the self-paced interactivity and the ability to return to the modules in the future. Blended learning can be successfully applied to the introductory clerkship in radiology. This teaching method offers educators an innovative and efficient approach to medical student education in radiology. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

The Construction of an Online Competitive Game-Based Learning System for Junior High School Students

ERIC Educational Resources Information Center

Cheng, Yuh-Ming; Kuo, Sheng-Huang; Lou, Shi-Jer; Shih, Ru-Chu

2012-01-01

The purpose of this study aimed to construct an online competitive game-based learning system by using freeware for junior high school students and to assess its effectiveness. From the learning standpoints, game mechanisms including learning points, competition mechanism, training room mechanism, questioning & answering mechanism, tips, and…

Cognitive Components Underpinning the Development of Model-Based Learning

PubMed Central

Potter, Tracey C.S.; Bryce, Nessa V.; Hartley, Catherine A.

2016-01-01

Reinforcement learning theory distinguishes “model-free” learning, which fosters reflexive repetition of previously rewarded actions, from “model-based” learning, which recruits a mental model of the environment to flexibly select goal-directed actions. Whereas model-free learning is evident across development, recruitment of model-based learning appears to increase with age. However, the cognitive processes underlying the development of model-based learning remain poorly characterized. Here, we examined whether age-related differences in cognitive processes underlying the construction and flexible recruitment of mental models predict developmental increases in model-based choice. In a cohort of participants aged 9–25, we examined whether the abilities to infer sequential regularities in the environment (“statistical learning”), maintain information in an active state (“working memory”) and integrate distant concepts to solve problems (“fluid reasoning”) predicted age-related improvements in model-based choice. We found that age-related improvements in statistical learning performance did not mediate the relationship between age and model-based choice. Ceiling performance on our working memory assay prevented examination of its contribution to model-based learning. However, age-related improvements in fluid reasoning statistically mediated the developmental increase in the recruitment of a model-based strategy. These findings suggest that gradual development of fluid reasoning may be a critical component process underlying the emergence of model-based learning. PMID:27825732

Preparing High School Students for Success in Advanced Placement Statistics: An Investigation of Pedagogies and Strategies Used in an Online Advanced Placement Statistics Course

ERIC Educational Resources Information Center

Potter, James Thomson, III

2012-01-01

Research into teaching practices and strategies has been performed separately in AP Statistics and in K-12 online learning (Garfield, 2002; Ferdig, DiPietro, Black & Dawson, 2009). This study seeks combine the two and build on the need for more investigation into online teaching and learning in specific content (Ferdig et al, 2009; DiPietro,…

The correlation between effective factors of e-learning and demographic variables in a post-graduate program of virtual medical education in Tehran University of Medical Sciences.

PubMed

Golband, Farnoosh; Hosseini, Agha Fatemeh; Mojtahedzadeh, Rita; Mirhosseini, Fakhrossadat; Bigdeli, Shoaleh

2014-01-01

E-learning as an educational approach has been adopted by diverse educational and academic centers worldwide as it facilitates learning in facing the challenges of the new era in education. Considering the significance of virtual education and its growing practice, it is of vital importance to examine its components for promoting and maintaining success. This analytical cross-sectional study was an attempt to determine the relationship between four factors of content, educator, learner and system, and effective e-learning in terms of demographic variables, including age, gender, educational background, and marital status of postgraduate master's students (MSc) studying at virtual faculty of Tehran University of Medical Sciences. The sample was selected by census (n=60); a demographic data gathering tool and a researcher-made questionnaire were used to collect data. The face and content validity of both tools were confirmed and the results were analyzed by descriptive statistics (frequency, percentile, standard deviation and mean) and inferential statistics (independent t-test, Scheffe's test, one-way ANOVA and Pearson correlation test) by using SPSS (V.16). The present study revealed that There was no statistically significant relationship between age and marital status and effective e-learning (P>0.05); whereas, there was a statistically significant difference between gender and educational background with effective e-learning (P<0.05). Knowing the extent to which these factors can influence effective e-learning can help managers and designers to make the right decisions about educational components of e-learning, i.e. content, educator, system and learner and improve them to create a more productive learning environment for learners.

Attitudes toward statistics in medical postgraduates: measuring, evaluating and monitoring

PubMed Central

2012-01-01

Background In medical training, statistics is considered a very difficult course to learn and teach. Current studies have found that students’ attitudes toward statistics can influence their learning process. Measuring, evaluating and monitoring the changes of students’ attitudes toward statistics are important. Few studies have focused on the attitudes of postgraduates, especially medical postgraduates. Our purpose was to understand current attitudes regarding statistics held by medical postgraduates and explore their effects on students’ achievement. We also wanted to explore the influencing factors and the sources of these attitudes and monitor their changes after a systematic statistics course. Methods A total of 539 medical postgraduates enrolled in a systematic statistics course completed the pre-form of the Survey of Attitudes Toward Statistics −28 scale, and 83 postgraduates were selected randomly from among them to complete the post-form scale after the course. Results Most medical postgraduates held positive attitudes toward statistics, but they thought statistics was a very difficult subject. The attitudes mainly came from experiences in a former statistical or mathematical class. Age, level of statistical education, research experience, specialty and mathematics basis may influence postgraduate attitudes toward statistics. There were significant positive correlations between course achievement and attitudes toward statistics. In general, student attitudes showed negative changes after completing a statistics course. Conclusions The importance of student attitudes toward statistics must be recognized in medical postgraduate training. To make sure all students have a positive learning environment, statistics teachers should measure their students’ attitudes and monitor their change of status during a course. Some necessary assistance should be offered for those students who develop negative attitudes. PMID:23173770

The Metamorphosis of the Statistical Segmentation Output: Lexicalization during Artificial Language Learning

ERIC Educational Resources Information Center

Fernandes, Tania; Kolinsky, Regine; Ventura, Paulo

2009-01-01

This study combined artificial language learning (ALL) with conventional experimental techniques to test whether statistical speech segmentation outputs are integrated into adult listeners' mental lexicon. Lexicalization was assessed through inhibitory effects of novel neighbors (created by the parsing process) on auditory lexical decisions to…

Statistical Learning in Specific Language Impairment: A Meta-Analysis

ERIC Educational Resources Information Center

Lammertink, Imme; Boersma, Paul; Wijnen, Frank; Rispens, Judith

2017-01-01

Purpose: The current meta-analysis provides a quantitative overview of published and unpublished studies on statistical learning in the auditory verbal domain in people with and without specific language impairment (SLI). The database used for the meta-analysis is accessible online and open to updates (Community-Augmented Meta-Analysis), which…

Learning Object Names at Different Hierarchical Levels Using Cross-Situational Statistics

ERIC Educational Resources Information Center

Chen, Chi-hsin; Zhang, Yayun; Yu, Chen

2018-01-01

Objects in the world usually have names at different hierarchical levels (e.g., "beagle," "dog," "animal"). This research investigates adults' ability to use cross-situational statistics to simultaneously learn object labels at individual and category levels. The results revealed that adults were able to use…

Measuring University Students' Approaches to Learning Statistics: An Invariance Study

ERIC Educational Resources Information Center

Chiesi, Francesca; Primi, Caterina; Bilgin, Ayse Aysin; Lopez, Maria Virginia; del Carmen Fabrizio, Maria; Gozlu, Sitki; Tuan, Nguyen Minh

2016-01-01

The aim of the current study was to provide evidence that an abbreviated version of the Approaches and Study Skills Inventory for Students (ASSIST) was invariant across different languages and educational contexts in measuring university students' learning approaches to statistics. Data were collected on samples of university students attending…

Inverting an Introductory Statistics Classroom

ERIC Educational Resources Information Center

Kraut, Gertrud L.

2015-01-01

The inverted classroom allows more in-class time for inquiry-based learning and for working through more advanced problem-solving activities than does the traditional lecture class. The skills acquired in this learning environment offer benefits far beyond the statistics classroom. This paper discusses four ways that can make the inverted…

General cognitive principles for learning structure in time and space.

PubMed

Goldstein, Michael H; Waterfall, Heidi R; Lotem, Arnon; Halpern, Joseph Y; Schwade, Jennifer A; Onnis, Luca; Edelman, Shimon

2010-06-01

How are hierarchically structured sequences of objects, events or actions learned from experience and represented in the brain? When several streams of regularities present themselves, which will be learned and which ignored? Can statistical regularities take effect on their own, or are additional factors such as behavioral outcomes expected to influence statistical learning? Answers to these questions are starting to emerge through a convergence of findings from naturalistic observations, behavioral experiments, neurobiological studies, and computational analyses and simulations. We propose that a small set of principles are at work in every situation that involves learning of structure from patterns of experience and outline a general framework that accounts for such learning. (c) 2010 Elsevier Ltd. All rights reserved.

Resonant Cholinergic Dynamics in Cognitive and Motor Decision-Making: Attention, Category Learning, and Choice in Neocortex, Superior Colliculus, and Optic Tectum.

PubMed

Grossberg, Stephen; Palma, Jesse; Versace, Massimiliano

2015-01-01

Freely behaving organisms need to rapidly calibrate their perceptual, cognitive, and motor decisions based on continuously changing environmental conditions. These plastic changes include sharpening or broadening of cognitive and motor attention and learning to match the behavioral demands that are imposed by changing environmental statistics. This article proposes that a shared circuit design for such flexible decision-making is used in specific cognitive and motor circuits, and that both types of circuits use acetylcholine to modulate choice selectivity. Such task-sensitive control is proposed to control thalamocortical choice of the critical features that are cognitively attended and that are incorporated through learning into prototypes of visual recognition categories. A cholinergically-modulated process of vigilance control determines if a recognition category and its attended features are abstract (low vigilance) or concrete (high vigilance). Homologous neural mechanisms of cholinergic modulation are proposed to focus attention and learn a multimodal map within the deeper layers of superior colliculus. This map enables visual, auditory, and planned movement commands to compete for attention, leading to selection of a winning position that controls where the next saccadic eye movement will go. Such map learning may be viewed as a kind of attentive motor category learning. The article hereby explicates a link between attention, learning, and cholinergic modulation during decision making within both cognitive and motor systems. Homologs between the mammalian superior colliculus and the avian optic tectum lead to predictions about how multimodal map learning may occur in the mammalian and avian brain and how such learning may be modulated by acetycholine.

Resonant Cholinergic Dynamics in Cognitive and Motor Decision-Making: Attention, Category Learning, and Choice in Neocortex, Superior Colliculus, and Optic Tectum

PubMed Central

Grossberg, Stephen; Palma, Jesse; Versace, Massimiliano

2016-01-01

Freely behaving organisms need to rapidly calibrate their perceptual, cognitive, and motor decisions based on continuously changing environmental conditions. These plastic changes include sharpening or broadening of cognitive and motor attention and learning to match the behavioral demands that are imposed by changing environmental statistics. This article proposes that a shared circuit design for such flexible decision-making is used in specific cognitive and motor circuits, and that both types of circuits use acetylcholine to modulate choice selectivity. Such task-sensitive control is proposed to control thalamocortical choice of the critical features that are cognitively attended and that are incorporated through learning into prototypes of visual recognition categories. A cholinergically-modulated process of vigilance control determines if a recognition category and its attended features are abstract (low vigilance) or concrete (high vigilance). Homologous neural mechanisms of cholinergic modulation are proposed to focus attention and learn a multimodal map within the deeper layers of superior colliculus. This map enables visual, auditory, and planned movement commands to compete for attention, leading to selection of a winning position that controls where the next saccadic eye movement will go. Such map learning may be viewed as a kind of attentive motor category learning. The article hereby explicates a link between attention, learning, and cholinergic modulation during decision making within both cognitive and motor systems. Homologs between the mammalian superior colliculus and the avian optic tectum lead to predictions about how multimodal map learning may occur in the mammalian and avian brain and how such learning may be modulated by acetycholine. PMID:26834535

Mathematical Representation Ability by Using Project Based Learning on the Topic of Statistics

NASA Astrophysics Data System (ADS)

Widakdo, W. A.

2017-09-01

Seeing the importance of the role of mathematics in everyday life, mastery of the subject areas of mathematics is a must. Representation ability is one of the fundamental ability that used in mathematics to make connection between abstract idea with logical thinking to understanding mathematics. Researcher see the lack of mathematical representation and try to find alternative solution to dolve it by using project based learning. This research use literature study from some books and articles in journals to see the importance of mathematical representation abiliy in mathemtics learning and how project based learning able to increase this mathematical representation ability on the topic of Statistics. The indicators for mathematical representation ability in this research classifies namely visual representation (picture, diagram, graph, or table); symbolize representation (mathematical statement. Mathematical notation, numerical/algebra symbol) and verbal representation (written text). This article explain about why project based learning able to influence student’s mathematical representation by using some theories in cognitive psychology, also showing the example of project based learning that able to use in teaching statistics, one of mathematics topic that very useful to analyze data.

Implicit Learning of Predictive Relationships in Three-element Visual Sequences by Young and Old Adults

PubMed Central

Howard, James H.; Howard, Darlene V.; Dennis, Nancy A.; Kelly, Andrew J.

2008-01-01

Knowledge of sequential relationships enables future events to be anticipated and processed efficiently. Research with the serial reaction time task (SRTT) has shown that sequence learning often occurs implicitly without effort or awareness. Here we report four experiments that use a triplet-learning task (TLT) to investigate sequence learning in young and older adults. In the TLT people respond only to the last target event in a series of discrete, three-event sequences or triplets. Target predictability is manipulated by varying the triplet frequency (joint probability) and/or the statistical relationships (conditional probabilities) among events within the triplets. Results revealed that both groups learned, though older adults showed less learning of both joint and conditional probabilities. Young people used the statistical information in both cues, but older adults relied primarily on information in the second cue alone. We conclude that the TLT complements and extends the SRTT and other tasks by offering flexibility in the kinds of sequential statistical regularities that may be studied as well as by controlling event timing and eliminating motor response sequencing. PMID:18763897

Modulation of spatial attention by goals, statistical learning, and monetary reward.

PubMed

Jiang, Yuhong V; Sha, Li Z; Remington, Roger W

2015-10-01

This study documented the relative strength of task goals, visual statistical learning, and monetary reward in guiding spatial attention. Using a difficult T-among-L search task, we cued spatial attention to one visual quadrant by (i) instructing people to prioritize it (goal-driven attention), (ii) placing the target frequently there (location probability learning), or (iii) associating that quadrant with greater monetary gain (reward-based attention). Results showed that successful goal-driven attention exerted the strongest influence on search RT. Incidental location probability learning yielded a smaller though still robust effect. Incidental reward learning produced negligible guidance for spatial attention. The 95 % confidence intervals of the three effects were largely nonoverlapping. To understand these results, we simulated the role of location repetition priming in probability cuing and reward learning. Repetition priming underestimated the strength of location probability cuing, suggesting that probability cuing involved long-term statistical learning of how to shift attention. Repetition priming provided a reasonable account for the negligible effect of reward on spatial attention. We propose a multiple-systems view of spatial attention that includes task goals, search habit, and priming as primary drivers of top-down attention.

Modulation of spatial attention by goals, statistical learning, and monetary reward

PubMed Central

Sha, Li Z.; Remington, Roger W.

2015-01-01

This study documented the relative strength of task goals, visual statistical learning, and monetary reward in guiding spatial attention. Using a difficult T-among-L search task, we cued spatial attention to one visual quadrant by (i) instructing people to prioritize it (goal-driven attention), (ii) placing the target frequently there (location probability learning), or (iii) associating that quadrant with greater monetary gain (reward-based attention). Results showed that successful goal-driven attention exerted the strongest influence on search RT. Incidental location probability learning yielded a smaller though still robust effect. Incidental reward learning produced negligible guidance for spatial attention. The 95 % confidence intervals of the three effects were largely nonoverlapping. To understand these results, we simulated the role of location repetition priming in probability cuing and reward learning. Repetition priming underestimated the strength of location probability cuing, suggesting that probability cuing involved long-term statistical learning of how to shift attention. Repetition priming provided a reasonable account for the negligible effect of reward on spatial attention. We propose a multiple-systems view of spatial attention that includes task goals, search habit, and priming as primary drivers of top-down attention. PMID:26105657

Aberrant Learning Achievement Detection Based on Person-Fit Statistics in Personalized e-Learning Systems

ERIC Educational Resources Information Center

Liu, Ming-Tsung; Yu, Pao-Ta

2011-01-01

A personalized e-learning service provides learning content to fit learners' individual differences. Learning achievements are influenced by cognitive as well as non-cognitive factors such as mood, motivation, interest, and personal styles. This paper proposes the Learning Caution Indexes (LCI) to detect aberrant learning patterns. The philosophy…

Impact of e-learning on nurses' and student nurses knowledge, skills, and satisfaction: a systematic review and meta-analysis.

PubMed

Lahti, Mari; Hätönen, Heli; Välimäki, Maritta

2014-01-01

To review the impact of e-learning on nurses' and nursing student's knowledge, skills and satisfaction related to e-learning. We conducted a systematic review and meta-analysis of randomized controlled trials (RCT) to assess the impact of e-learning on nurses' and nursing student's knowledge, skills and satisfaction. Electronic databases including MEDLINE (1948-2010), CINAHL (1981-2010), Psychinfo (1967-2010) and Eric (1966-2010) were searched in May 2010 and again in December 2010. All RCT studies evaluating the effectiveness of e-learning and differentiating between traditional learning methods among nurses were included. Data was extracted related to the purpose of the trial, sample, measurements used, index test results and reference standard. An extraction tool developed for Cochrane reviews was used. Methodological quality of eligible trials was assessed. 11 trials were eligible for inclusion in the analysis. We identified 11 randomized controlled trials including a total of 2491 nurses and student nurses'. First, the random effect size for four studies showed some improvement associated with e-learning compared to traditional techniques on knowledge. However, the difference was not statistically significant (p=0.39, MD 0.44, 95% CI -0.57 to 1.46). Second, one study reported a slight impact on e-learning on skills, but the difference was not statistically significant, either (p=0.13, MD 0.03, 95% CI -0.09 to 0.69). And third, no results on nurses or student nurses' satisfaction could be reported as the statistical data from three possible studies were not available. Overall, there was no statistical difference between groups in e-learning and traditional learning relating to nurses' or student nurses' knowledge, skills and satisfaction. E-learning can, however, offer an alternative method of education. In future, more studies following the CONSORT and QUOROM statements are needed to evaluate the effects of these interventions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Dyscalculia, dyslexia, and medical students' needs for learning and using statistics.

PubMed

MacDougall, Margaret

2009-02-07

Much has been written on the learning needs of dyslexic and dyscalculic students in primary and early secondary education. However, it is not clear that the necessary disability support staff and specialist literature are available to ensure that these needs are being adequately met within the context of learning statistics and general quantitative skills in the self-directed learning environments encountered in higher education. This commentary draws attention to dyslexia and dyscalculia as two potentially unrecognized conditions among undergraduate medical students and in turn, highlights key developments from recent literature in the diagnosis of these conditions. With a view to assisting medical educators meet the needs of dyscalculic learners and the more varied needs of dyslexic learners, a comprehensive list of suggestions is provided as to how learning resources can be designed from the outset to be more inclusive. A hitherto neglected area for future research is also identified through a call for a thorough investigation of the meaning of statistical literacy within the context of the undergraduate medical curriculum.

Dyscalculia, Dyslexia, and Medical Students’ Needs for Learning and Using Statistics

PubMed Central

MacDougall, Margaret

2009-01-01

Much has been written on the learning needs of dyslexic and dyscalculic students in primary and early secondary education. However, it is not clear that the necessary disability support staff and specialist literature are available to ensure that these needs are being adequately met within the context of learning statistics and general quantitative skills in the self-directed learning environments encountered in higher education. This commentary draws attention to dyslexia and dyscalculia as two potentially unrecognized conditions among undergraduate medical students and in turn, highlights key developments from recent literature in the diagnosis of these conditions. With a view to assisting medical educators meet the needs of dyscalculic learners and the more varied needs of dyslexic learners, a comprehensive list of suggestions is provided as to how learning resources can be designed from the outset to be more inclusive. A hitherto neglected area for future research is also identified through a call for a thorough investigation of the meaning of statistical literacy within the context of the undergraduate medical curriculum. PMID:20165516

Speech segmentation in aphasia

PubMed Central

Peñaloza, Claudia; Benetello, Annalisa; Tuomiranta, Leena; Heikius, Ida-Maria; Järvinen, Sonja; Majos, Maria Carmen; Cardona, Pedro; Juncadella, Montserrat; Laine, Matti; Martin, Nadine; Rodríguez-Fornells, Antoni

2017-01-01

Background Speech segmentation is one of the initial and mandatory phases of language learning. Although some people with aphasia have shown a preserved ability to learn novel words, their speech segmentation abilities have not been explored. Aims We examined the ability of individuals with chronic aphasia to segment words from running speech via statistical learning. We also explored the relationships between speech segmentation and aphasia severity, and short-term memory capacity. We further examined the role of lesion location in speech segmentation and short-term memory performance. Methods & Procedures The experimental task was first validated with a group of young adults (n = 120). Participants with chronic aphasia (n = 14) were exposed to an artificial language and were evaluated in their ability to segment words using a speech segmentation test. Their performance was contrasted against chance level and compared to that of a group of elderly matched controls (n = 14) using group and case-by-case analyses. Outcomes & Results As a group, participants with aphasia were significantly above chance level in their ability to segment words from the novel language and did not significantly differ from the group of elderly controls. Speech segmentation ability in the aphasic participants was not associated with aphasia severity although it significantly correlated with word pointing span, a measure of verbal short-term memory. Case-by-case analyses identified four individuals with aphasia who performed above chance level on the speech segmentation task, all with predominantly posterior lesions and mild fluent aphasia. Their short-term memory capacity was also better preserved than in the rest of the group. Conclusions Our findings indicate that speech segmentation via statistical learning can remain functional in people with chronic aphasia and suggest that this initial language learning mechanism is associated with the functionality of the verbal short-term memory system and the integrity of the left inferior frontal region. PMID:28824218

Aging and the statistical learning of grammatical form classes.

PubMed

Schwab, Jessica F; Schuler, Kathryn D; Stillman, Chelsea M; Newport, Elissa L; Howard, James H; Howard, Darlene V

2016-08-01

Language learners must place unfamiliar words into categories, often with few explicit indicators about when and how that word can be used grammatically. Reeder, Newport, and Aslin (2013) showed that college students can learn grammatical form classes from an artificial language by relying solely on distributional information (i.e., contextual cues in the input). Here, 2 experiments revealed that healthy older adults also show such statistical learning, though they are poorer than young at distinguishing grammatical from ungrammatical strings. This finding expands knowledge of which aspects of learning vary with aging, with potential implications for second language learning in late adulthood. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Statistical Reasoning Ability, Self-Efficacy, and Value Beliefs in a University Statistics Course

ERIC Educational Resources Information Center

Olani, A.; Hoekstra, R.; Harskamp, E.; van der Werf, G.

2011-01-01

Introduction: The study investigated the degree to which students' statistical reasoning abilities, statistics self-efficacy, and perceived value of statistics improved during a reform based introductory statistics course. The study also examined whether the changes in these learning outcomes differed with respect to the students' mathematical…

Is GAISE Evident? College Students' Perceptions of Statistics Classes as "Almost Not Math"

ERIC Educational Resources Information Center

Hedges, Sarai; Harkness, Shelly Sheats

2017-01-01

The connection between mathematics and statistics is an important aspect in understanding college students' learning of statistics because studies have shown relationships among mathematics attitudes and performance and statistics attitudes. Statistics attitudes, in turn, are related to performance in statistics courses. Little research has been…

How much to trust the senses: Likelihood learning

PubMed Central

Sato, Yoshiyuki; Kording, Konrad P.

2014-01-01

Our brain often needs to estimate unknown variables from imperfect information. Our knowledge about the statistical distributions of quantities in our environment (called priors) and currently available information from sensory inputs (called likelihood) are the basis of all Bayesian models of perception and action. While we know that priors are learned, most studies of prior-likelihood integration simply assume that subjects know about the likelihood. However, as the quality of sensory inputs change over time, we also need to learn about new likelihoods. Here, we show that human subjects readily learn the distribution of visual cues (likelihood function) in a way that can be predicted by models of statistically optimal learning. Using a likelihood that depended on color context, we found that a learned likelihood generalized to new priors. Thus, we conclude that subjects learn about likelihood. PMID:25398975

Beyond existence and aiming outside the laboratory: estimating frequency-dependent and pay-off-biased social learning strategies.

PubMed

McElreath, Richard; Bell, Adrian V; Efferson, Charles; Lubell, Mark; Richerson, Peter J; Waring, Timothy

2008-11-12

The existence of social learning has been confirmed in diverse taxa, from apes to guppies. In order to advance our understanding of the consequences of social transmission and evolution of behaviour, however, we require statistical tools that can distinguish among diverse social learning strategies. In this paper, we advance two main ideas. First, social learning is diverse, in the sense that individuals can take advantage of different kinds of information and combine them in different ways. Examining learning strategies for different information conditions illuminates the more detailed design of social learning. We construct and analyse an evolutionary model of diverse social learning heuristics, in order to generate predictions and illustrate the impact of design differences on an organism's fitness. Second, in order to eventually escape the laboratory and apply social learning models to natural behaviour, we require statistical methods that do not depend upon tight experimental control. Therefore, we examine strategic social learning in an experimental setting in which the social information itself is endogenous to the experimental group, as it is in natural settings. We develop statistical models for distinguishing among different strategic uses of social information. The experimental data strongly suggest that most participants employ a hierarchical strategy that uses both average observed pay-offs of options as well as frequency information, the same model predicted by our evolutionary analysis to dominate a wide range of conditions.

Pioglitazone improves the ability of learning and memory via activating ERK1/2 signaling pathway in the hippocampus of T2DM rats.

PubMed

Gao, F; Zang, L; Wu, D Y; Li, Y J; Zhang, Q; Wang, H B; Tian, G L; Mu, Y M

2017-06-09

To explore the correlation between effect of PIO (pioglitazone, PIO) on learning as well as memory and ERK1/2 (extracellular signal regulated kinase 1/2, ERK1/2) pathway in T2DM (type 2 diabetes mellitus, T2DM) rats, further to elucidate the potential mechanism of PIO in improvement of learning and memory. 12-week-old male SD rats (number of 10 per group) were randomly divided into control group (CON), T2DM group (DM) and T2DM +PIO group (DM+PG). Rats in DM and DM+PG groups were given high fat diet for 20 weeks, then treated with Streptozotocin (27mg/kg) by intraperitoneal injection at 21week. After 72h, the FBG (fasting blood glucose, FBG) was greater than 7.0mmol/L can considered T2DM rats. DM+PG group was treated with PIO (10 mg·kg -1 ·d -1 ) by gavage daily. After Hyperinsulinemic-Euglycemic Clamp Study and Morris water maze test at 30-week, all of animals were sacrificed. The expressions of RKIP (Raf-1 kinase inhibitor protein, RKIP) and ERK1/2 in hippocampus were detected using Western Blot and real-time PCR. The FBG level: DM group (7.68±0.54mmol/L) was higher than CON group (5.35±0.63mmol/L) and DM+PG group (6.07±0.84mmol/L), the differences were considered statistically significant (P <0.05). Hyperinsulinemic-Euglycemic Clamp Studies: GIR (glucose infusion rate, GIR) of DM group (21.02±5.10 mg·kg -1 ·d -1 ) was less than CON group (27.64±3.87 mg·kg -1 ·d -1 ) and DM+PG group (26.04 ±5.41 mg·kg -1 ·d -1 ), the differences were considered statistically significant (P <0.05). Morris water maze training: The escape latencies and searching platform performance of DM group (24.54±5.02s) decreased significantly compared with CON group (16.73±4.02s) and DM+PG group (18.05±4.12s) (P <0.05). Changes of RKIP, ERK, p-ERK protein relative content in rat hippocampus: Compared with CON groupand DM+PG group, the relative content of RKIP in DM group remarkably increased (P<0.01); ERK protein levels were not considered statistically significant among the three groups (P>0.05); The relative content of p-ERK1/2 protein in CON group and DM+PG group rats dorsal were higher than those in group DM, the difference was considered statistically significant (P<0.01). Changes in hippocampus of rat RKIP and ERK gene relative content: Compared with CON group and DM+PG group, levels of RKIP mRNA in DM group were significantly increased (P<0.01); ERK mRNA levels were not considered statistically significant among the three groups (P>0.05). Activation of ERK1/2 signal transduction pathway via reducing RKIP in the hippocampus may be one of the mechanisms of PIO to improve the learning and memory of the T2DM rats. Copyright © 2017 Elsevier B.V. All rights reserved.

A hypothesis on a role of oxytocin in the social mechanisms of speech and vocal learning.

PubMed

Theofanopoulou, Constantina; Boeckx, Cedric; Jarvis, Erich D

2017-08-30

Language acquisition in humans and song learning in songbirds naturally happen as a social learning experience, providing an excellent opportunity to reveal social motivation and reward mechanisms that boost sensorimotor learning. Our knowledge about the molecules and circuits that control these social mechanisms for vocal learning and language is limited. Here we propose a hypothesis of a role for oxytocin (OT) in the social motivation and evolution of vocal learning and language. Building upon existing evidence, we suggest specific neural pathways and mechanisms through which OT might modulate vocal learning circuits in specific developmental stages. © 2017 The Authors.

A hypothesis on a role of oxytocin in the social mechanisms of speech and vocal learning

PubMed Central

Jarvis, Erich D.

2017-01-01

Language acquisition in humans and song learning in songbirds naturally happen as a social learning experience, providing an excellent opportunity to reveal social motivation and reward mechanisms that boost sensorimotor learning. Our knowledge about the molecules and circuits that control these social mechanisms for vocal learning and language is limited. Here we propose a hypothesis of a role for oxytocin (OT) in the social motivation and evolution of vocal learning and language. Building upon existing evidence, we suggest specific neural pathways and mechanisms through which OT might modulate vocal learning circuits in specific developmental stages. PMID:28835557

Employer Learning and the Signaling Value of Education. National Longitudinal Surveys Discussion Paper.

ERIC Educational Resources Information Center

Altonji, Joseph G.; Pierret, Charles R.

A statistical analysis was performed to test the hypothesis that, if profit-maximizing firms have limited information about the general productivity of new workers, they may choose to use easily observable characteristics such as years of education to discriminate statistically among workers. Information about employer learning was obtained by…

A Web-Based Learning Tool Improves Student Performance in Statistics: A Randomized Masked Trial

ERIC Educational Resources Information Center

Gonzalez, Jose A.; Jover, Lluis; Cobo, Erik; Munoz, Pilar

2010-01-01

Background: e-status is a web-based tool able to generate different statistical exercises and to provide immediate feedback to students' answers. Although the use of Information and Communication Technologies (ICTs) is becoming widespread in undergraduate education, there are few experimental studies evaluating its effects on learning. Method: All…

Learning across Languages: Bilingual Experience Supports Dual Language Statistical Word Segmentation

ERIC Educational Resources Information Center

Antovich, Dylan M.; Graf Estes, Katharine

2018-01-01

Bilingual acquisition presents learning challenges beyond those found in monolingual environments, including the need to segment speech in two languages. Infants may use statistical cues, such as syllable-level transitional probabilities, to segment words from fluent speech. In the present study we assessed monolingual and bilingual 14-month-olds'…

Individual Differences in Statistical Learning Predict Children's Comprehension of Syntax

ERIC Educational Resources Information Center

Kidd, Evan; Arciuli, Joanne

2016-01-01

Variability in children's language acquisition is likely due to a number of cognitive and social variables. The current study investigated whether individual differences in statistical learning (SL), which has been implicated in language acquisition, independently predicted 6- to 8-year-old's comprehension of syntax. Sixty-eight (N = 68)…

Learning Axes and Bridging Tools in a Technology-Based Design for Statistics

ERIC Educational Resources Information Center

Abrahamson, Dor; Wilensky, Uri

2007-01-01

We introduce a design-based research framework, "learning axes and bridging tools," and demonstrate its application in the preparation and study of an implementation of a middle-school experimental computer-based unit on probability and statistics, "ProbLab" (Probability Laboratory, Abrahamson and Wilensky 2002 [Abrahamson, D., & Wilensky, U.…

What Software to Use in the Teaching of Mathematical Subjects?

ERIC Educational Resources Information Center

Berežný, Štefan

2015-01-01

We can consider two basic views, when using mathematical software in the teaching of mathematical subjects. First: How to learn to use specific software for the specific tasks, e. g., software Statistica for the subjects of Applied statistics, probability and mathematical statistics, or financial mathematics. Second: How to learn to use the…

Difficulties in Learning and Teaching Statistics: Teacher Views

ERIC Educational Resources Information Center

Koparan, Timur

2015-01-01

The purpose of this study is to define teacher views about the difficulties in learning and teaching middle school statistics subjects. To serve this aim, a number of interviews were conducted with 10 middle school maths teachers in 2011-2012 school year in the province of Trabzon. Of the qualitative descriptive research methods, the…

Can Being Scared Cause Tummy Aches? Naive Theories, Ambiguous Evidence, and Preschoolers' Causal Inferences

ERIC Educational Resources Information Center

Schulz, Laura E.; Bonawitz, Elizabeth Baraff; Griffiths, Thomas L.

2007-01-01

Causal learning requires integrating constraints provided by domain-specific theories with domain-general statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate…

Coupling Changing Student Demographics with Evidence-Based Leadership Practices: Leading Hispanic Friendly Learning Organizations

ERIC Educational Resources Information Center

Farmer, Tod Allen

2012-01-01

The study assessed the need for learning organizations to implement evidence-based policies and practices designed to enhance the academic and social success of Hispanic learners. Descriptive statistics and longitudinal data from the National Center for Educational Statistics (NCES) and the National Clearinghouse for English Language Acquisition…

College Students Attitudes toward Learning Process and Outcome of Online Instruction and Distance Learning across Learning Styles

ERIC Educational Resources Information Center

Nguyen, Dat-Dao; Zhang, Yue

2011-01-01

This study uses the Learning-Style Inventory--LSI (Smith & Kolb, 1985) to explore to what extent student attitudes toward learning process and outcome of online instruction and Distance Learning are affected by their cognitive styles and learning behaviors. It finds that there are not much statistically significant differences in perceptions…

Computer-Assisted Instruction in Statistics. Technical Report.

ERIC Educational Resources Information Center

Cooley, William W.

A paper given at a conference on statistical computation discussed teaching statistics with computers. It concluded that computer-assisted instruction is most appropriately employed in the numerical demonstration of statistical concepts, and for statistical laboratory instruction. The student thus learns simultaneously about the use of computers…

Predicting Chemically Induced Duodenal Ulcer and Adrenal Necrosis with Classification Trees

NASA Astrophysics Data System (ADS)

Giampaolo, Casimiro; Gray, Andrew T.; Olshen, Richard A.; Szabo, Sandor

1991-07-01

Binary tree-structured statistical classification algorithms and properties of 56 model alkyl nucleophiles were brought to bear on two problems of experimental pharmacology and toxicology. Each rat of a learning sample of 745 was administered one compound and autopsied to determine the presence of duodenal ulcer or adrenal hemorrhagic necrosis. The cited statistical classification schemes were then applied to these outcomes and 67 features of the compounds to ascertain those characteristics that are associated with biologic activity. For predicting duodenal ulceration, dipole moment, melting point, and solubility in octanol are particularly important, while for predicting adrenal necrosis, important features include the number of sulfhydryl groups and double bonds. These methods may constitute inexpensive but powerful ways to screen untested compounds for possible organ-specific toxicity. Mechanisms for the etiology and pathogenesis of the duodenal and adrenal lesions are suggested, as are additional avenues for drug design.

Automatic stage identification of Drosophila egg chamber based on DAPI images

PubMed Central

Jia, Dongyu; Xu, Qiuping; Xie, Qian; Mio, Washington; Deng, Wu-Min

2016-01-01

The Drosophila egg chamber, whose development is divided into 14 stages, is a well-established model for developmental biology. However, visual stage determination can be a tedious, subjective and time-consuming task prone to errors. Our study presents an objective, reliable and repeatable automated method for quantifying cell features and classifying egg chamber stages based on DAPI images. The proposed approach is composed of two steps: 1) a feature extraction step and 2) a statistical modeling step. The egg chamber features used are egg chamber size, oocyte size, egg chamber ratio and distribution of follicle cells. Methods for determining the on-site of the polytene stage and centripetal migration are also discussed. The statistical model uses linear and ordinal regression to explore the stage-feature relationships and classify egg chamber stages. Combined with machine learning, our method has great potential to enable discovery of hidden developmental mechanisms. PMID:26732176

Experimental statistical signature of many-body quantum interference

NASA Astrophysics Data System (ADS)

Giordani, Taira; Flamini, Fulvio; Pompili, Matteo; Viggianiello, Niko; Spagnolo, Nicolò; Crespi, Andrea; Osellame, Roberto; Wiebe, Nathan; Walschaers, Mattia; Buchleitner, Andreas; Sciarrino, Fabio

2018-03-01

Multi-particle interference is an essential ingredient for fundamental quantum mechanics phenomena and for quantum information processing to provide a computational advantage, as recently emphasized by boson sampling experiments. Hence, developing a reliable and efficient technique to witness its presence is pivotal in achieving the practical implementation of quantum technologies. Here, we experimentally identify genuine many-body quantum interference via a recent efficient protocol, which exploits statistical signatures at the output of a multimode quantum device. We successfully apply the test to validate three-photon experiments in an integrated photonic circuit, providing an extensive analysis on the resources required to perform it. Moreover, drawing upon established techniques of machine learning, we show how such tools help to identify the—a priori unknown—optimal features to witness these signatures. Our results provide evidence on the efficacy and feasibility of the method, paving the way for its adoption in large-scale implementations.

Surprise responses in the human brain demonstrate statistical learning under high concurrent cognitive demand

NASA Astrophysics Data System (ADS)

Garrido, Marta Isabel; Teng, Chee Leong James; Taylor, Jeremy Alexander; Rowe, Elise Genevieve; Mattingley, Jason Brett

2016-06-01

The ability to learn about regularities in the environment and to make predictions about future events is fundamental for adaptive behaviour. We have previously shown that people can implicitly encode statistical regularities and detect violations therein, as reflected in neuronal responses to unpredictable events that carry a unique prediction error signature. In the real world, however, learning about regularities will often occur in the context of competing cognitive demands. Here we asked whether learning of statistical regularities is modulated by concurrent cognitive load. We compared electroencephalographic metrics associated with responses to pure-tone sounds with frequencies sampled from narrow or wide Gaussian distributions. We showed that outliers evoked a larger response than those in the centre of the stimulus distribution (i.e., an effect of surprise) and that this difference was greater for physically identical outliers in the narrow than in the broad distribution. These results demonstrate an early neurophysiological marker of the brain's ability to implicitly encode complex statistical structure in the environment. Moreover, we manipulated concurrent cognitive load by having participants perform a visual working memory task while listening to these streams of sounds. We again observed greater prediction error responses in the narrower distribution under both low and high cognitive load. Furthermore, there was no reliable reduction in prediction error magnitude under high-relative to low-cognitive load. Our findings suggest that statistical learning is not a capacity limited process, and that it proceeds automatically even when cognitive resources are taxed by concurrent demands.

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…

The origins of word learning: Brain responses of 3-month-olds indicate their rapid association of objects and words.

PubMed

Friedrich, Manuela; Friederici, Angela D

2017-03-01

The present study explored the origins of word learning in early infancy. Using event-related potentials (ERP) we monitored the brain activity of 3-month-old infants when they were repeatedly exposed to several initially novel words paired consistently with each the same initially novel objects or inconsistently with different objects. Our results provide strong evidence that these young infants extract statistic regularities in the distribution of the co-occurrences of objects and words extremely quickly. The data suggest that this ability is based on the rapid formation of associations between the neural representations of objects and words, but that the new associations are not retained in long-term memory until the next day. The type of brain response moreover indicates that, unlike in older infants, in 3-month-olds a semantic processing stage is not involved. Their ability to combine words with meaningful information is caused by a primary learning mechanism that enables the formation of proto-words and acts as a precursor for the acquisition of genuine words. © 2015 John Wiley & Sons Ltd.

Rapid Statistical Learning Supporting Word Extraction From Continuous Speech.

PubMed

Batterink, Laura J

2017-07-01

The identification of words in continuous speech, known as speech segmentation, is a critical early step in language acquisition. This process is partially supported by statistical learning, the ability to extract patterns from the environment. Given that speech segmentation represents a potential bottleneck for language acquisition, patterns in speech may be extracted very rapidly, without extensive exposure. This hypothesis was examined by exposing participants to continuous speech streams composed of novel repeating nonsense words. Learning was measured on-line using a reaction time task. After merely one exposure to an embedded novel word, learners demonstrated significant learning effects, as revealed by faster responses to predictable than to unpredictable syllables. These results demonstrate that learners gained sensitivity to the statistical structure of unfamiliar speech on a very rapid timescale. This ability may play an essential role in early stages of language acquisition, allowing learners to rapidly identify word candidates and "break in" to an unfamiliar language.

What predicts successful literacy acquisition in a second language?

PubMed Central

Frost, Ram; Siegelman, Noam; Narkiss, Alona; Afek, Liron

2013-01-01

We examined whether success (or failure) in assimilating the structure of a second language could be predicted by general statistical learning abilities that are non-linguistic in nature. We employed a visual statistical learning (VSL) task, monitoring our participants’ implicit learning of the transitional probabilities of visual shapes. A pretest revealed that performance in the VSL task is not correlated with abilities related to a general G factor or working memory. We found that native speakers of English who picked up the implicit statistical structure embedded in the continuous stream of shapes, on average, better assimilated the Semitic structure of Hebrew words. Our findings thus suggest that languages and their writing systems are characterized by idiosyncratic correlations of form and meaning, and these are picked up in the process of literacy acquisition, as they are picked up in any other type of learning, for the purpose of making sense of the environment. PMID:23698615

Retrieval Capabilities of Hierarchical Networks: From Dyson to Hopfield

NASA Astrophysics Data System (ADS)

Agliari, Elena; Barra, Adriano; Galluzzi, Andrea; Guerra, Francesco; Tantari, Daniele; Tavani, Flavia

2015-01-01

We consider statistical-mechanics models for spin systems built on hierarchical structures, which provide a simple example of non-mean-field framework. We show that the coupling decay with spin distance can give rise to peculiar features and phase diagrams much richer than their mean-field counterpart. In particular, we consider the Dyson model, mimicking ferromagnetism in lattices, and we prove the existence of a number of metastabilities, beyond the ordered state, which become stable in the thermodynamic limit. Such a feature is retained when the hierarchical structure is coupled with the Hebb rule for learning, hence mimicking the modular architecture of neurons, and gives rise to an associative network able to perform single pattern retrieval as well as multiple-pattern retrieval, depending crucially on the external stimuli and on the rate of interaction decay with distance; however, those emergent multitasking features reduce the network capacity with respect to the mean-field counterpart. The analysis is accomplished through statistical mechanics, Markov chain theory, signal-to-noise ratio technique, and numerical simulations in full consistency. Our results shed light on the biological complexity shown by real networks, and suggest future directions for understanding more realistic models.

Perception of Sentence Stress in Speech Correlates With the Temporal Unpredictability of Prosodic Features.

PubMed

Kakouros, Sofoklis; Räsänen, Okko

2016-09-01

Numerous studies have examined the acoustic correlates of sentential stress and its underlying linguistic functionality. However, the mechanism that connects stress cues to the listener's attentional processing has remained unclear. Also, the learnability versus innateness of stress perception has not been widely discussed. In this work, we introduce a novel perspective to the study of sentential stress and put forward the hypothesis that perceived sentence stress in speech is related to the unpredictability of prosodic features, thereby capturing the attention of the listener. As predictability is based on the statistical structure of the speech input, the hypothesis also suggests that stress perception is a result of general statistical learning mechanisms. To study this idea, computational simulations are performed where temporal prosodic trajectories are modeled with an n-gram model. Probabilities of the feature trajectories are subsequently evaluated on a set of novel utterances and compared to human perception of stress. The results show that the low-probability regions of F0 and energy trajectories are strongly correlated with stress perception, giving support to the idea that attention and unpredictability of sensory stimulus are mutually connected. Copyright © 2015 Cognitive Science Society, Inc.

Learning mechanisms to limit medication administration errors.

PubMed

Drach-Zahavy, Anat; Pud, Dorit

2010-04-01

This paper is a report of a study conducted to identify and test the effectiveness of learning mechanisms applied by the nursing staff of hospital wards as a means of limiting medication administration errors. Since the influential report ;To Err Is Human', research has emphasized the role of team learning in reducing medication administration errors. Nevertheless, little is known about the mechanisms underlying team learning. Thirty-two hospital wards were randomly recruited. Data were collected during 2006 in Israel by a multi-method (observations, interviews and administrative data), multi-source (head nurses, bedside nurses) approach. Medication administration error was defined as any deviation from procedures, policies and/or best practices for medication administration, and was identified using semi-structured observations of nurses administering medication. Organizational learning was measured using semi-structured interviews with head nurses, and the previous year's reported medication administration errors were assessed using administrative data. The interview data revealed four learning mechanism patterns employed in an attempt to learn from medication administration errors: integrated, non-integrated, supervisory and patchy learning. Regression analysis results demonstrated that whereas the integrated pattern of learning mechanisms was associated with decreased errors, the non-integrated pattern was associated with increased errors. Supervisory and patchy learning mechanisms were not associated with errors. Superior learning mechanisms are those that represent the whole cycle of team learning, are enacted by nurses who administer medications to patients, and emphasize a system approach to data analysis instead of analysis of individual cases.

Nonlinear Dynamics, Chaotic and Complex Systems

NASA Astrophysics Data System (ADS)

Infeld, E.; Zelazny, R.; Galkowski, A.

2011-04-01

Part I. Dynamic Systems Bifurcation Theory and Chaos: 1. Chaos in random dynamical systems V. M. Gunldach; 2. Controlling chaos using embedded unstable periodic orbits: the problem of optimal periodic orbits B. R. Hunt and E. Ott; 3. Chaotic tracer dynamics in open hydrodynamical flows G. Karolyi, A. Pentek, T. Tel and Z. Toroczkai; 4. Homoclinic chaos L. P. Shilnikov; Part II. Spatially Extended Systems: 5. Hydrodynamics of relativistic probability flows I. Bialynicki-Birula; 6. Waves in ionic reaction-diffusion-migration systems P. Hasal, V. Nevoral, I. Schreiber, H. Sevcikova, D. Snita, and M. Marek; 7. Anomalous scaling in turbulence: a field theoretical approach V. Lvov and I. Procaccia; 8. Abelian sandpile cellular automata M. Markosova; 9. Transport in an incompletely chaotic magnetic field F. Spineanu; Part III. Dynamical Chaos Quantum Physics and Foundations Of Statistical Mechanics: 10. Non-equilibrium statistical mechanics and ergodic theory L. A. Bunimovich; 11. Pseudochaos in statistical physics B. Chirikov; 12. Foundations of non-equilibrium statistical mechanics J. P. Dougherty; 13. Thermomechanical particle simulations W. G. Hoover, H. A. Posch, C. H. Dellago, O. Kum, C. G. Hoover, A. J. De Groot and B. L. Holian; 14. Quantum dynamics on a Markov background and irreversibility B. Pavlov; 15. Time chaos and the laws of nature I. Prigogine and D. J. Driebe; 16. Evolutionary Q and cognitive systems: dynamic entropies and predictability of evolutionary processes W. Ebeling; 17. Spatiotemporal chaos information processing in neural networks H. Szu; 18. Phase transitions and learning in neural networks C. Van den Broeck; 19. Synthesis of chaos A. Vanecek and S. Celikovsky; 20. Computational complexity of continuous problems H. Wozniakowski; Part IV. Complex Systems As An Interface Between Natural Sciences and Environmental Social and Economic Sciences: 21. Stochastic differential geometry in finance studies V. G. Makhankov; Part V. Conference Banquet Speech: Where will the future go? M. J. Feigenbaum.

In-Depth Characterization and Validation of Human Urine Metabolomes Reveal Novel Metabolic Signatures of Lower Urinary Tract Symptoms

NASA Astrophysics Data System (ADS)

Hao, Ling; Greer, Tyler; Page, David; Shi, Yatao; Vezina, Chad M.; Macoska, Jill A.; Marker, Paul C.; Bjorling, Dale E.; Bushman, Wade; Ricke, William A.; Li, Lingjun

2016-08-01

Lower urinary tract symptoms (LUTS) are a range of irritative or obstructive symptoms that commonly afflict aging population. The diagnosis is mostly based on patient-reported symptoms, and current medication often fails to completely eliminate these symptoms. There is a pressing need for objective non-invasive approaches to measure symptoms and understand disease mechanisms. We developed an in-depth workflow combining urine metabolomics analysis and machine learning bioinformatics to characterize metabolic alterations and support objective diagnosis of LUTS. Machine learning feature selection and statistical tests were combined to identify candidate biomarkers, which were statistically validated with leave-one-patient-out cross-validation and absolutely quantified by selected reaction monitoring assay. Receiver operating characteristic analysis showed highly-accurate prediction power of candidate biomarkers to stratify patients into disease or non-diseased categories. The key metabolites and pathways may be possibly correlated with smooth muscle tone changes, increased collagen content, and inflammation, which have been identified as potential contributors to urinary dysfunction in humans and rodents. Periurethral tissue staining revealed a significant increase in collagen content and tissue stiffness in men with LUTS. Together, our study provides the first characterization and validation of LUTS urinary metabolites and pathways to support the future development of a urine-based diagnostic test for LUTS.

Examining the role of fluctuations in the early stages of homogenous polymer crystallization with simulation and statistical learning

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

Welch, Jr., Paul Michael

Here, we propose a relationship between the dynamics in the amorphous and crystalline domains during polymer crystallization: the fluctuations of ordering-rate about a material-specific value in the amorphous phase drive those fluctuations associated with the increase in percent crystallinity. This suggests a differential equation that satisfies the three experimentally observed time regimes for the rate of crystal growth. To test this postulated expression, we applied a suite of statistical learning tools to molecular dynamics simulations to extract the relevant phenomenology. This study shows that the proposed relationship holds in the early time regime. It illustrates the effectiveness of soft computingmore » tools in the analysis of coarse-grained simulations in which patterns exist, but may not easily yield to strict quantitative evaluation. This ability assists us in characterizing the critical early time molecular arrangement during the primary nucleation phase of polymer melt crystallization. In addition to supporting the validity of the proposed kinetics expression, the simulations show that (i) the classical nucleation and growth mechanism is active in the early stages of ordering; (ii) the number of nuclei and their masses grow linearly during this early time regime; and (iii) a fixed inter-nuclei distance is established.« less

Examining the role of fluctuations in the early stages of homogenous polymer crystallization with simulation and statistical learning

DOE PAGES

Welch, Jr., Paul Michael

2017-01-23

Here, we propose a relationship between the dynamics in the amorphous and crystalline domains during polymer crystallization: the fluctuations of ordering-rate about a material-specific value in the amorphous phase drive those fluctuations associated with the increase in percent crystallinity. This suggests a differential equation that satisfies the three experimentally observed time regimes for the rate of crystal growth. To test this postulated expression, we applied a suite of statistical learning tools to molecular dynamics simulations to extract the relevant phenomenology. This study shows that the proposed relationship holds in the early time regime. It illustrates the effectiveness of soft computingmore » tools in the analysis of coarse-grained simulations in which patterns exist, but may not easily yield to strict quantitative evaluation. This ability assists us in characterizing the critical early time molecular arrangement during the primary nucleation phase of polymer melt crystallization. In addition to supporting the validity of the proposed kinetics expression, the simulations show that (i) the classical nucleation and growth mechanism is active in the early stages of ordering; (ii) the number of nuclei and their masses grow linearly during this early time regime; and (iii) a fixed inter-nuclei distance is established.« less

Gift from statistical learning: Visual statistical learning enhances memory for sequence elements and impairs memory for items that disrupt regularities.

PubMed

Otsuka, Sachio; Saiki, Jun

2016-02-01

Prior studies have shown that visual statistical learning (VSL) enhances familiarity (a type of memory) of sequences. How do statistical regularities influence the processing of each triplet element and inserted distractors that disrupt the regularity? Given that increased attention to triplets induced by VSL and inhibition of unattended triplets, we predicted that VSL would promote memory for each triplet constituent, and degrade memory for inserted stimuli. Across the first two experiments, we found that objects from structured sequences were more likely to be remembered than objects from random sequences, and that letters (Experiment 1) or objects (Experiment 2) inserted into structured sequences were less likely to be remembered than those inserted into random sequences. In the subsequent two experiments, we examined an alternative account for our results, whereby the difference in memory for inserted items between structured and random conditions is due to individuation of items within random sequences. Our findings replicated even when control letters (Experiment 3A) or objects (Experiment 3B) were presented before or after, rather than inserted into, random sequences. Our findings suggest that statistical learning enhances memory for each item in a regular set and impairs memory for items that disrupt the regularity. Copyright © 2015 Elsevier B.V. All rights reserved.

Rhythmic grouping biases constrain infant statistical learning

PubMed Central

Hay, Jessica F.; Saffran, Jenny R.

2012-01-01

Linguistic stress and sequential statistical cues to word boundaries interact during speech segmentation in infancy. However, little is known about how the different acoustic components of stress constrain statistical learning. The current studies were designed to investigate whether intensity and duration each function independently as cues to initial prominence (trochaic-based hypothesis) or whether, as predicted by the Iambic-Trochaic Law (ITL), intensity and duration have characteristic and separable effects on rhythmic grouping (ITL-based hypothesis) in a statistical learning task. Infants were familiarized with an artificial language (Experiments 1 & 3) or a tone stream (Experiment 2) in which there was an alternation in either intensity or duration. In addition to potential acoustic cues, the familiarization sequences also contained statistical cues to word boundaries. In speech (Experiment 1) and non-speech (Experiment 2) conditions, 9-month-old infants demonstrated discrimination patterns consistent with an ITL-based hypothesis: intensity signaled initial prominence and duration signaled final prominence. The results of Experiment 3, in which 6.5-month-old infants were familiarized with the speech streams from Experiment 1, suggest that there is a developmental change in infants’ willingness to treat increased duration as a cue to word offsets in fluent speech. Infants’ perceptual systems interact with linguistic experience to constrain how infants learn from their auditory environment. PMID:23730217

Relationship between Graduate Students' Statistics Self-Efficacy, Statistics Anxiety, Attitude toward Statistics, and Social Support

ERIC Educational Resources Information Center

Perepiczka, Michelle; Chandler, Nichelle; Becerra, Michael

2011-01-01

Statistics plays an integral role in graduate programs. However, numerous intra- and interpersonal factors may lead to successful completion of needed coursework in this area. The authors examined the extent of the relationship between self-efficacy to learn statistics and statistics anxiety, attitude towards statistics, and social support of 166…

Does reviewing lead to better learning and decision making? Answers from a randomized stock market experiment.

PubMed

Wessa, Patrick; Holliday, Ian E

2012-01-01

The literature is not univocal about the effects of Peer Review (PR) within the context of constructivist learning. Due to the predominant focus on using PR as an assessment tool, rather than a constructivist learning activity, and because most studies implicitly assume that the benefits of PR are limited to the reviewee, little is known about the effects upon students who are required to review their peers. Much of the theoretical debate in the literature is focused on explaining how and why constructivist learning is beneficial. At the same time these discussions are marked by an underlying presupposition of a causal relationship between reviewing and deep learning. The purpose of the study is to investigate whether the writing of PR feedback causes students to benefit in terms of: perceived utility about statistics, actual use of statistics, better understanding of statistical concepts and associated methods, changed attitudes towards market risks, and outcomes of decisions that were made. We conducted a randomized experiment, assigning students randomly to receive PR or non-PR treatments and used two cohorts with a different time span. The paper discusses the experimental design and all the software components that we used to support the learning process: Reproducible Computing technology which allows students to reproduce or re-use statistical results from peers, Collaborative PR, and an AI-enhanced Stock Market Engine. The results establish that the writing of PR feedback messages causes students to experience benefits in terms of Behavior, Non-Rote Learning, and Attitudes, provided the sequence of PR activities are maintained for a period that is sufficiently long.

Learning coefficient of generalization error in Bayesian estimation and vandermonde matrix-type singularity.

PubMed

Aoyagi, Miki; Nagata, Kenji

2012-06-01

The term algebraic statistics arises from the study of probabilistic models and techniques for statistical inference using methods from algebra and geometry (Sturmfels, 2009 ). The purpose of our study is to consider the generalization error and stochastic complexity in learning theory by using the log-canonical threshold in algebraic geometry. Such thresholds correspond to the main term of the generalization error in Bayesian estimation, which is called a learning coefficient (Watanabe, 2001a , 2001b ). The learning coefficient serves to measure the learning efficiencies in hierarchical learning models. In this letter, we consider learning coefficients for Vandermonde matrix-type singularities, by using a new approach: focusing on the generators of the ideal, which defines singularities. We give tight new bound values of learning coefficients for the Vandermonde matrix-type singularities and the explicit values with certain conditions. By applying our results, we can show the learning coefficients of three-layered neural networks and normal mixture models.

Teaching Statistics in Integration with Psychology

ERIC Educational Resources Information Center

Wiberg, Marie

2009-01-01

The aim was to revise a statistics course in order to get the students motivated to learn statistics and to integrate statistics more throughout a psychology course. Further, we wish to make students become more interested in statistics and to help them see the importance of using statistics in psychology research. To achieve this goal, several…

Treatment of missing data in follow-up studies of randomised controlled trials: A systematic review of the literature.

PubMed

Sullivan, Thomas R; Yelland, Lisa N; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-08-01

After completion of a randomised controlled trial, an extended follow-up period may be initiated to learn about longer term impacts of the intervention. Since extended follow-up studies often involve additional eligibility restrictions and consent processes for participation, and a longer duration of follow-up entails a greater risk of participant attrition, missing data can be a considerable threat in this setting. As a potential source of bias, it is critical that missing data are appropriately handled in the statistical analysis, yet little is known about the treatment of missing data in extended follow-up studies. The aims of this review were to summarise the extent of missing data in extended follow-up studies and the use of statistical approaches to address this potentially serious problem. We performed a systematic literature search in PubMed to identify extended follow-up studies published from January to June 2015. Studies were eligible for inclusion if the original randomised controlled trial results were also published and if the main objective of extended follow-up was to compare the original randomised groups. We recorded information on the extent of missing data and the approach used to treat missing data in the statistical analysis of the primary outcome of the extended follow-up study. Of the 81 studies included in the review, 36 (44%) reported additional eligibility restrictions and 24 (30%) consent processes for entry into extended follow-up. Data were collected at a median of 7 years after randomisation. Excluding 28 studies with a time to event primary outcome, 51/53 studies (96%) reported missing data on the primary outcome. The median percentage of randomised participants with complete data on the primary outcome was just 66% in these studies. The most common statistical approach to address missing data was complete case analysis (51% of studies), while likelihood-based analyses were also well represented (25%). Sensitivity analyses around the missing data mechanism were rarely performed (25% of studies), and when they were, they often involved unrealistic assumptions about the mechanism. Despite missing data being a serious problem in extended follow-up studies, statistical approaches to addressing missing data were often inadequate. We recommend researchers clearly specify all sources of missing data in follow-up studies and use statistical methods that are valid under a plausible assumption about the missing data mechanism. Sensitivity analyses should also be undertaken to assess the robustness of findings to assumptions about the missing data mechanism.

Technical Data for Five Learning Style Instruments with Instructional Applications.

ERIC Educational Resources Information Center

Lemire, David

This manual presents five learning styles instruments and presents data related to validity and reliability and descriptive statistics. The manual also discusses the implications for learning presented by each of these learning models. For purposes of this discussion, "learning style,""cognitive style," and "personal style" are used synonymously.…

Learning Opportunities for Group Learning

ERIC Educational Resources Information Center

Gil, Alfonso J.; Mataveli, Mara

2017-01-01

Purpose: This paper aims to analyse the impact of organizational learning culture and learning facilitators in group learning. Design/methodology/approach: This study was conducted using a survey method applied to a statistically representative sample of employees from Rioja wine companies in Spain. A model was tested using a structural equation…

Prediction during statistical learning, and implications for the implicit/explicit divide

PubMed Central

Dale, Rick; Duran, Nicholas D.; Morehead, J. Ryan

2012-01-01

Accounts of statistical learning, both implicit and explicit, often invoke predictive processes as central to learning, yet practically all experiments employ non-predictive measures during training. We argue that the common theoretical assumption of anticipation and prediction needs clearer, more direct evidence for it during learning. We offer a novel experimental context to explore prediction, and report results from a simple sequential learning task designed to promote predictive behaviors in participants as they responded to a short sequence of simple stimulus events. Predictive tendencies in participants were measured using their computer mouse, the trajectories of which served as a means of tapping into predictive behavior while participants were exposed to very short and simple sequences of events. A total of 143 participants were randomly assigned to stimulus sequences along a continuum of regularity. Analysis of computer-mouse trajectories revealed that (a) participants almost always anticipate events in some manner, (b) participants exhibit two stable patterns of behavior, either reacting to vs. predicting future events, (c) the extent to which participants predict relates to performance on a recall test, and (d) explicit reports of perceiving patterns in the brief sequence correlates with extent of prediction. We end with a discussion of implicit and explicit statistical learning and of the role prediction may play in both kinds of learning. PMID:22723817

Learning (Not) to Talk about Race: Investigating What Doctoral Students Learn about Race Variables and Statistical Modeling

ERIC Educational Resources Information Center

Armijo, Michael; Lundy-Wagner, Valerie; Merrill, Elizabeth

2012-01-01

This paper asks how doctoral students understand the use of race variables in statistical modeling. More specifically, it examines how doctoral students at two universities are trained to define, operationalize, and analyze race variables. The authors interviewed students and instructors in addition to conducting a document analysis of their texts…

Improving Face Verification in Photo Albums by Combining Facial Recognition and Metadata With Cross-Matching

DTIC Science & Technology

2017-12-01

satisfactory performance. We do not use statistical models, and we do not create patterns that require supervised learning. Our methodology is intended...statistical models, and we do not create patterns that require supervised learning. Our methodology is intended for use in personal digital image...THESIS MOTIVATION .........................................................................19 III. METHODOLOGY

Using the Student Research Project to Integrate Macroeconomics and Statistics in an Advanced Cost Accounting Course

ERIC Educational Resources Information Center

Hassan, Mahamood M.; Schwartz, Bill N.

2014-01-01

This paper discusses a student research project that is part of an advanced cost accounting class. The project emphasizes active learning, integrates cost accounting with macroeconomics and statistics by "learning by doing" using real world data. Students analyze sales data for a publicly listed company by focusing on the company's…

The Role of Context in Developing Informal Statistical Inferential Reasoning: A Classroom Study

ERIC Educational Resources Information Center

Pfannkuch, Maxine

2011-01-01

Context is identified as an important factor when considering the learning of informal statistical inferential reasoning, but research in this area is very limited. This small exploratory study in one grade 10 (14 year olds) classroom seeks to learn more about the role context plays in learners' inferential reasoning, where both teacher and…

Statistical Learning Is Not Affected by a Prior Bout of Physical Exercise

ERIC Educational Resources Information Center

Stevens, David J.; Arciuli, Joanne; Anderson, David I.

2016-01-01

This study examined the effect of a prior bout of exercise on implicit cognition. Specifically, we examined whether a prior bout of moderate intensity exercise affected performance on a statistical learning task in healthy adults. A total of 42 participants were allocated to one of three conditions--a control group, a group that exercised for…

The Prospective Mathematics Teachers' Thought Processes and Views about Using Problem-Based Learning in Statistics Education

ERIC Educational Resources Information Center

Canturk-Gunhan, Berna; Bukova-Guzel, Esra; Ozgur, Zekiye

2012-01-01

The purpose of this study is to determine prospective mathematics teachers' views about using problem-based learning (PBL) in statistics teaching and to examine their thought processes. It is a qualitative study conducted with 15 prospective mathematics teachers from a state university in Turkey. The data were collected via participant observation…

A Meta-Analytic Review of Studies of the Effectiveness of Small-Group Learning Methods on Statistics Achievement

ERIC Educational Resources Information Center

Kalaian, Sema A.; Kasim, Rafa M.

2014-01-01

This meta-analytic study focused on the quantitative integration and synthesis of the accumulated pedagogical research in undergraduate statistics education literature. These accumulated research studies compared the academic achievement of students who had been instructed using one of the various forms of small-group learning methods to those who…

Student Achievement in Undergraduate Statistics: The Potential Value of Allowing Failure

ERIC Educational Resources Information Center

Ferrandino, Joseph A.

2016-01-01

This article details what resulted when I re-designed my undergraduate statistics course to allow failure as a learning strategy and focused on achievement rather than performance. A variety of within and between sample t-tests are utilized to determine the impact of unlimited test and quiz opportunities on student learning on both quizzes and…

Effects of Concept Mapping Strategy on Learning Performance in Business and Economics Statistics

ERIC Educational Resources Information Center

Chiou, Chei-Chang

2009-01-01

A concept map (CM) is a hierarchically arranged, graphic representation of the relationships among concepts. Concept mapping (CMING) is the process of constructing a CM. This paper examines whether a CMING strategy can be useful in helping students to improve their learning performance in a business and economics statistics course. A single…

Statistics in Action: The Story of a Successful Service-Learning Project

ERIC Educational Resources Information Center

DeHart, Mary; Ham, Jim

2011-01-01

The purpose of this article is to share the stories of an Introductory Statistics service-learning project in which students from both New Jersey and Michigan design and conduct phone surveys that lead to publication in local newspapers; to discuss the pedagogical benefits and challenges of the project; and to provide information for those who…

Teaching Introductory Business Statistics Using the DCOVA Framework

ERIC Educational Resources Information Center

Levine, David M.; Stephan, David F.

2011-01-01

Introductory business statistics students often receive little guidance on how to apply the methods they learn to further business objectives they may one day face. And those students may fail to see the continuity among the topics taught in an introductory course if they learn those methods outside a context that provides a unifying framework.…

Course Format Effects on Learning Outcomes in an Introductory Statistics Course

ERIC Educational Resources Information Center

Sami, Fary

2011-01-01

The purpose of this study was to determine if course format significantly impacted student learning and course completion rates in an introductory statistics course taught at Harford Community College. In addition to the traditional lecture format, the College offers an online, and a hybrid (blend of traditional and online) version of this class.…

Characterizing Impacts of Online Professional Development on Teachers' Beliefs and Perspectives about Teaching Statistics

ERIC Educational Resources Information Center

Lee, Hollylynne S.; Lovett, Jennifer N.; Mojica, Gemma M.

2017-01-01

With online learning becoming a more viable option for teachers to develop their expertise, our report shares one such effort focused on improving the teaching of statistics. We share design principles and learning opportunities, as well as discuss specific impacts evident in classroom teachers' course activity concerning changes to their beliefs…

Bayesian theories of conditioning in a changing world.

PubMed

Courville, Aaron C; Daw, Nathaniel D; Touretzky, David S

2006-07-01

The recent flowering of Bayesian approaches invites the re-examination of classic issues in behavior, even in areas as venerable as Pavlovian conditioning. A statistical account can offer a new, principled interpretation of behavior, and previous experiments and theories can inform many unexplored aspects of the Bayesian enterprise. Here we consider one such issue: the finding that surprising events provoke animals to learn faster. We suggest that, in a statistical account of conditioning, surprise signals change and therefore uncertainty and the need for new learning. We discuss inference in a world that changes and show how experimental results involving surprise can be interpreted from this perspective, and also how, thus understood, these phenomena help constrain statistical theories of animal and human learning.

Improving students' understanding of quantum mechanics

NASA Astrophysics Data System (ADS)

Zhu, Guangtian

2011-12-01

Learning physics is challenging at all levels. Students' difficulties in the introductory level physics courses have been widely studied and many instructional strategies have been developed to help students learn introductory physics. However, research shows that there is a large diversity in students' preparation and skills in the upper-level physics courses and it is necessary to provide scaffolding support to help students learn advanced physics. This thesis explores issues related to students' common difficulties in learning upper-level undergraduate quantum mechanics and how these difficulties can be reduced by research-based learning tutorials and peer instruction tools. We investigated students' difficulties in learning quantum mechanics by administering written tests and surveys to many classes and conducting individual interviews with a subset of students. Based on these investigations, we developed Quantum Interactive Learning Tutorials (QuILTs) and peer instruction tools to help students build a hierarchical knowledge structure of quantum mechanics through a guided approach. Preliminary assessments indicate that students' understanding of quantum mechanics is improved after using the research-based learning tools in the junior-senior level quantum mechanics courses. We also designed a standardized conceptual survey that can help instructors better probe students' understanding of quantum mechanics concepts in one spatial dimension. The validity and reliability of this quantum mechanics survey is discussed.

A novel data-driven learning method for radar target detection in nonstationary environments

DOE PAGES

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

2016-04-12

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

Language learning, language use and the evolution of linguistic variation

PubMed Central

Perfors, Amy; Fehér, Olga; Samara, Anna; Swoboda, Kate; Wonnacott, Elizabeth

2017-01-01

Linguistic universals arise from the interaction between the processes of language learning and language use. A test case for the relationship between these factors is linguistic variation, which tends to be conditioned on linguistic or sociolinguistic criteria. How can we explain the scarcity of unpredictable variation in natural language, and to what extent is this property of language a straightforward reflection of biases in statistical learning? We review three strands of experimental work exploring these questions, and introduce a Bayesian model of the learning and transmission of linguistic variation along with a closely matched artificial language learning experiment with adult participants. Our results show that while the biases of language learners can potentially play a role in shaping linguistic systems, the relationship between biases of learners and the structure of languages is not straightforward. Weak biases can have strong effects on language structure as they accumulate over repeated transmission. But the opposite can also be true: strong biases can have weak or no effects. Furthermore, the use of language during interaction can reshape linguistic systems. Combining data and insights from studies of learning, transmission and use is therefore essential if we are to understand how biases in statistical learning interact with language transmission and language use to shape the structural properties of language. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872370

Mathematics authentic assessment on statistics learning: the case for student mini projects

NASA Astrophysics Data System (ADS)

Fauziah, D.; Mardiyana; Saputro, D. R. S.

2018-03-01

Mathematics authentic assessment is a form of meaningful measurement of student learning outcomes for the sphere of attitude, skill and knowledge in mathematics. The construction of attitude, skill and knowledge achieved through the fulfilment of tasks which involve active and creative role of the students. One type of authentic assessment is student mini projects, started from planning, data collecting, organizing, processing, analysing and presenting the data. The purpose of this research is to learn the process of using authentic assessments on statistics learning which is conducted by teachers and to discuss specifically the use of mini projects to improving students’ learning in the school of Surakarta. This research is an action research, where the data collected through the results of the assessments rubric of student mini projects. The result of data analysis shows that the average score of rubric of student mini projects result is 82 with 96% classical completeness. This study shows that the application of authentic assessment can improve students’ mathematics learning outcomes. Findings showed that teachers and students participate actively during teaching and learning process, both inside and outside of the school. Student mini projects also provide opportunities to interact with other people in the real context while collecting information and giving presentation to the community. Additionally, students are able to exceed more on the process of statistics learning using authentic assessment.

2.5-year-olds use cross-situational consistency to learn verbs under referential uncertainty.

PubMed

Scott, Rose M; Fisher, Cynthia

2012-02-01

Recent evidence shows that children can use cross-situational statistics to learn new object labels under referential ambiguity (e.g., Smith & Yu, 2008). Such evidence has been interpreted as support for proposals that statistical information about word-referent co-occurrence plays a powerful role in word learning. But object labels represent only a fraction of the vocabulary children acquire, and arguably represent the simplest case of word learning based on observations of world scenes. Here we extended the study of cross-situational word learning to a new segment of the vocabulary, action verbs, to permit a stronger test of the role of statistical information in word learning. In two experiments, on each trial 2.5-year-olds encountered two novel intransitive (e.g., "She's pimming!"; Experiment 1) or transitive verbs (e.g., "She's pimming her toy!"; Experiment 2) while viewing two action events. The consistency with which each verb accompanied each action provided the only source of information about the intended referent of each verb. The 2.5-year-olds used cross-situational consistency in verb learning, but also showed significant limits on their ability to do so as the sentences and scenes became slightly more complex. These findings help to define the role of cross-situational observation in word learning. Copyright © 2011 Elsevier B.V. All rights reserved.

2.5-year-olds use cross-situational consistency to learn verbs under referential uncertainty

PubMed Central

Scott, Rose M.; Fisher, Cynthia

2011-01-01

Recent evidence shows that children can use cross-situational statistics to learn new object labels under referential ambiguity (e.g., Smith & Yu, 2008). Such evidence has been interpreted as support for proposals that statistical information about word-referent co-occurrence plays a powerful role in word learning. But object labels represent only a fraction of the vocabulary children acquire, and arguably represent the simplest case of word learning based on observations of world scenes. Here we extended the study of cross-situational word learning to a new segment of the vocabulary, action verbs, to permit a stronger test of the role of statistical information in word learning. In two experiments, on each trial 2.5-year-olds encountered two novel intransitive (e.g., “She’s pimming!”; Experiment 1) or transitive verbs (e.g., “She’s pimming her toy!”; Experiment 2) while viewing two action events. The consistency with which each verb accompanied each action provided the only source of information about the intended referent of each verb. The 2.5-year-olds used cross-situational consistency in verb learning, but also showed significant limits on their ability to do so as the sentences and scenes became slightly more complex. These findings help to define the role of cross-situational observation in word learning. PMID:22104489

Language learning, language use and the evolution of linguistic variation.

PubMed

Smith, Kenny; Perfors, Amy; Fehér, Olga; Samara, Anna; Swoboda, Kate; Wonnacott, Elizabeth

2017-01-05

Linguistic universals arise from the interaction between the processes of language learning and language use. A test case for the relationship between these factors is linguistic variation, which tends to be conditioned on linguistic or sociolinguistic criteria. How can we explain the scarcity of unpredictable variation in natural language, and to what extent is this property of language a straightforward reflection of biases in statistical learning? We review three strands of experimental work exploring these questions, and introduce a Bayesian model of the learning and transmission of linguistic variation along with a closely matched artificial language learning experiment with adult participants. Our results show that while the biases of language learners can potentially play a role in shaping linguistic systems, the relationship between biases of learners and the structure of languages is not straightforward. Weak biases can have strong effects on language structure as they accumulate over repeated transmission. But the opposite can also be true: strong biases can have weak or no effects. Furthermore, the use of language during interaction can reshape linguistic systems. Combining data and insights from studies of learning, transmission and use is therefore essential if we are to understand how biases in statistical learning interact with language transmission and language use to shape the structural properties of language.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Authors.

Engaging with the Art & Science of Statistics

ERIC Educational Resources Information Center

Peters, Susan A.

2010-01-01

How can statistics clearly be mathematical and yet distinct from mathematics? The answer lies in the reality that statistics is both an art and a science, and both aspects are important for teaching and learning statistics. Statistics is a mathematical science in that it applies mathematical theories and techniques. Mathematics provides the…

Students' Perceptions of Statistics: An Exploration of Attitudes, Conceptualizations, and Content Knowledge of Statistics

ERIC Educational Resources Information Center

Bond, Marjorie E.; Perkins, Susan N.; Ramirez, Caroline

2012-01-01

Although statistics education research has focused on students' learning and conceptual understanding of statistics, researchers have only recently begun investigating students' perceptions of statistics. The term perception describes the overlap between cognitive and non-cognitive factors. In this mixed-methods study, undergraduate students…

The Use of a Reflective Learning Journal in an Introductory Statistics Course

ERIC Educational Resources Information Center

Denton, Ashley Waggoner

2018-01-01

Reflective learning entails a thoughtful learning process through which one not only learns a particular piece of knowledge or skill, but better understands "how" one learned it--knowledge that can then be transferred well beyond the scope of the specific learning experience. This type of thinking empowers learners by making them more…

Explorations in statistics: the log transformation.

PubMed

Curran-Everett, Douglas

2018-06-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This thirteenth installment of Explorations in Statistics explores the log transformation, an established technique that rescales the actual observations from an experiment so that the assumptions of some statistical analysis are better met. A general assumption in statistics is that the variability of some response Y is homogeneous across groups or across some predictor variable X. If the variability-the standard deviation-varies in rough proportion to the mean value of Y, a log transformation can equalize the standard deviations. Moreover, if the actual observations from an experiment conform to a skewed distribution, then a log transformation can make the theoretical distribution of the sample mean more consistent with a normal distribution. This is important: the results of a one-sample t test are meaningful only if the theoretical distribution of the sample mean is roughly normal. If we log-transform our observations, then we want to confirm the transformation was useful. We can do this if we use the Box-Cox method, if we bootstrap the sample mean and the statistic t itself, and if we assess the residual plots from the statistical model of the actual and transformed sample observations.

Addressing economic development goals through innovative teaching of university statistics: a case study of statistical modelling in Nigeria

NASA Astrophysics Data System (ADS)

Oseloka Ezepue, Patrick; Ojo, Adegbola

2012-12-01

A challenging problem in some developing countries such as Nigeria is inadequate training of students in effective problem solving using the core concepts of their disciplines. Related to this is a disconnection between their learning and socio-economic development agenda of a country. These problems are more vivid in statistical education which is dominated by textbook examples and unbalanced assessment 'for' and 'of' learning within traditional curricula. The problems impede the achievement of socio-economic development objectives such as those stated in the Nigerian Vision 2020 blueprint and United Nations Millennium Development Goals. They also impoverish the ability of (statistics) graduates to creatively use their knowledge in relevant business and industry sectors, thereby exacerbating mass graduate unemployment in Nigeria and similar developing countries. This article uses a case study in statistical modelling to discuss the nature of innovations in statistics education vital to producing new kinds of graduates who can link their learning to national economic development goals, create wealth and alleviate poverty through (self) employment. Wider implications of the innovations for repositioning mathematical sciences education globally are explored in this article.