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
Experience and sentence processing: statistical learning and relative clause comprehension.
Wells, Justine B; Christiansen, Morten H; Race, David S; Acheson, Daniel J; MacDonald, Maryellen C
2009-03-01
Many explanations of the difficulties associated with interpreting object relative clauses appeal to the demands that object relatives make on working memory. MacDonald and Christiansen [MacDonald, M. C., & Christiansen, M. H. (2002). Reassessing working memory: Comment on Just and Carpenter (1992) and Waters and Caplan (1996). Psychological Review, 109, 35-54] pointed to variations in reading experience as a source of differences, arguing that the unique word order of object relatives makes their processing more difficult and more sensitive to the effects of previous experience than the processing of subject relatives. This hypothesis was tested in a large-scale study manipulating reading experiences of adults over several weeks. The group receiving relative clause experience increased reading speeds for object relatives more than for subject relatives, whereas a control experience group did not. The reading time data were compared to performance of a computational model given different amounts of experience. The results support claims for experience-based individual differences and an important role for statistical learning in sentence comprehension processes.
Multiple processes in two-dimensional visual statistical learning
Hoshino, Eiichi; Mogi, Ken
2017-01-01
Knowledge about the arrangement of visual elements is an important aspect of perception. This study investigates whether humans learn rules of two-dimensional abstract patterns (exemplars) generated from Reber's artificial grammar. The key question is whether the subjects can implicitly learn them without explicit instructions, and, if so, how they use the acquired knowledge to judge new patterns (probes) in relation to their finite experience of the exemplars. The analysis was conducted using dissimilarities among patterns, which are defined with n-gram probabilities and the Levenshtein distance. The results show that subjects are able to learn rules of two-dimensional visual patterns (exemplars) and make categorical judgment of probes based on knowledge of exemplar-based representation. Our analysis revealed that subjects' judgments of probes were related to the degree of dissimilarities between the probes and exemplars. The result suggests the coexistence of configural and element-based processing in exemplar-based representations. Exemplar-based representation was preferred to prototypical representation through tasks requiring discrimination, recognition and working memory. Relations of the studied judgment processes to the neural basis are discussed. We conclude that knowledge of a finite experience of two-dimensional visual patterns would be crystalized in different levels of relations among visual elements. PMID:28212388
The extraction and integration framework: a two-process account of statistical learning.
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.
On-line individual differences in statistical learning predict language processing.
Misyak, Jennifer B; Christiansen, Morten H; Tomblin, J Bruce
2010-01-01
Considerable individual differences in language ability exist among normally developing children and adults. Whereas past research have attributed such differences to variations in verbal working memory or experience with language, we test the hypothesis that individual differences in statistical learning may be associated with differential language performance. We employ a novel paradigm for studying statistical learning on-line, combining a serial-reaction time task with artificial grammar learning. This task offers insights into both the timecourse of and individual differences in statistical learning. Experiment 1 charts the micro-level trajectory for statistical learning of nonadjacent dependencies and provides an on-line index of individual differences therein. In Experiment 2, these differences are then shown to predict variations in participants' on-line processing of long-distance dependencies involving center-embedded relative clauses. The findings suggest that individual differences in the ability to learn from experience through statistical learning may contribute to variations in linguistic performance.
A statistical property of multiagent learning based on Markov decision process.
Iwata, Kazunori; Ikeda, Kazushi; Sakai, Hideaki
2006-07-01
We exhibit an important property called the asymptotic equipartition property (AEP) on empirical sequences in an ergodic multiagent Markov decision process (MDP). Using the AEP which facilitates the analysis of multiagent learning, we give a statistical property of multiagent learning, such as reinforcement learning (RL), near the end of the learning process. We examine the effect of the conditions among the agents on the achievement of a cooperative policy in three different cases: blind, visible, and communicable. Also, we derive a bound on the speed with which the empirical sequence converges to the best sequence in probability, so that the multiagent learning yields the best cooperative result.
ERIC Educational Resources Information Center
Akram, Muhammad; Siddiqui, Asim Jamal; Yasmeen, Farah
2004-01-01
In order to learn the concept of statistical techniques one needs to run real experiments that generate reliable data. In practice, the data from some well-defined process or system is very costly and time consuming. It is difficult to run real experiments during the teaching period in the university. To overcome these difficulties, statisticians…
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.
NASA Technical Reports Server (NTRS)
Shewhart, Mark
1991-01-01
Statistical Process Control (SPC) charts are one of several tools used in quality control. Other tools include flow charts, histograms, cause and effect diagrams, check sheets, Pareto diagrams, graphs, and scatter diagrams. A control chart is simply a graph which indicates process variation over time. The purpose of drawing a control chart is to detect any changes in the process signalled by abnormal points or patterns on the graph. The Artificial Intelligence Support Center (AISC) of the Acquisition Logistics Division has developed a hybrid machine learning expert system prototype which automates the process of constructing and interpreting control charts.
Implicit Statistical Learning in Language Processing: Word Predictability is the Key
Conway, Christopher M.; Baurnschmidt, Althea; Huang, Sean; Pisoni, David B.
2010-01-01
Fundamental learning abilities related to the implicit encoding of sequential structure have been postulated to underlie language acquisition and processing. However, there is very little direct evidence to date supporting such a link between implicit statistical learning and language. In three experiments using novel methods of assessing implicit learning and language abilities, we show that sensitivity to sequential structure -- as measured by improvements to immediate memory span for structurally-consistent input sequences -- is significantly correlated with the ability to use knowledge of word predictability to aid speech perception under degraded listening conditions. Importantly, the association remained even after controlling for participant performance on other cognitive tasks, including short-term and working memory, intelligence, attention and inhibition, and vocabulary knowledge. Thus, the evidence suggests that implicit learning abilities are essential for acquiring long-term knowledge of the sequential structure of language -- i.e., knowledge of word predictability – and that individual differences on such abilities impact speech perception in everyday situations. These findings provide a new theoretical rationale linking basic learning phenomena to specific aspects of spoken language processing in adults, and may furthermore indicate new fruitful directions for investigating both typical and atypical language development. PMID:19922909
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…
Palmer, Shekeila D; Mattys, Sven L
2016-12-01
The purpose of this study was to examine the extent to which working memory resources are recruited during statistical learning (SL). Participants were asked to identify novel words in an artificial speech stream where the transitional probabilities between syllables provided the only segmentation cue. Experiments 1 and 2 demonstrated that segmentation performance improved when the speech rate was slowed down, suggesting that SL is supported by some form of active processing or maintenance mechanism that operates more effectively under slower presentation rates. In Experiment 3 we investigated the nature of this mechanism by asking participants to perform a two-back task while listening to the speech stream. Half of the participants performed a two-back rhyme task designed to engage phonological processing, whereas the other half performed a comparable two-back task on un-nameable visual shapes. It was hypothesized that if SL is dependent only upon domain-specific processes (i.e., phonological rehearsal), the rhyme task should impair speech segmentation performance more than the shape task. However, the two loads were equally disruptive to learning, as they both eradicated the benefit provided by the slow rate. These results suggest that SL is supported by working-memory processes that rely on domain-general resources.
Cooperative Learning in Statistics.
ERIC Educational Resources Information Center
Keeler, Carolyn M.; And Others
1994-01-01
Formal use of cooperative learning techniques proved effective in improving student performance and retention in a freshman level statistics course. Lectures interspersed with group activities proved effective in increasing conceptual understanding and overall class performance. (11 references) (Author)
Statistics for Learning Genetics
NASA Astrophysics Data System (ADS)
Charles, Abigail Sheena
This study investigated the knowledge and skills that biology students may need to help them understand statistics/mathematics as it applies to genetics. The data are based on analyses of current representative genetics texts, practicing genetics professors' perspectives, and more directly, students' perceptions of, and performance in, doing statistically-based genetics problems. This issue is at the emerging edge of modern college-level genetics instruction, and this study attempts to identify key theoretical components for creating a specialized biological statistics curriculum. The goal of this curriculum will be to prepare biology students with the skills for assimilating quantitatively-based genetic processes, increasingly at the forefront of modern genetics. To fulfill this, two college level classes at two universities were surveyed. One university was located in the northeastern US and the other in the West Indies. There was a sample size of 42 students and a supplementary interview was administered to a select 9 students. Interviews were also administered to professors in the field in order to gain insight into the teaching of statistics in genetics. Key findings indicated that students had very little to no background in statistics (55%). Although students did perform well on exams with 60% of the population receiving an A or B grade, 77% of them did not offer good explanations on a probability question associated with the normal distribution provided in the survey. The scope and presentation of the applicable statistics/mathematics in some of the most used textbooks in genetics teaching, as well as genetics syllabi used by instructors do not help the issue. It was found that the text books, often times, either did not give effective explanations for students, or completely left out certain topics. The omission of certain statistical/mathematical oriented topics was seen to be also true with the genetics syllabi reviewed for this study. Nonetheless
Analysis of Self-Regulated Learning Processing Using Statistical Models for Count Data
ERIC Educational Resources Information Center
Greene, Jeffrey Alan; Costa, Lara-Jeane; Dellinger, Kristin
2011-01-01
Researchers often use measures of the frequency of self-regulated learning (SRL; Zimmerman, "American Educational Research Journal," 45(1), 166-183, 2000) processing as a predictor of learning gains. These frequency data, which are really counts of SRL processing events, are often non-normally distributed, and the accurate analysis of these data…
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…
2008-03-31
Superresolution ”, Invited paper, The Computer Journal, April 2007; doi: 10.1093/comjnl/bxm007 4 8. M. Elad, P. Milanfar, R. Rubinstein, “Analysis versus...Elad, and P. Milanfar, “Video-to-Video Dynamic Superresolution for Grayscale and Color Sequences ”, EURASIP Journal of Applied Signal Processing...Special Issue on Superresolution Imaging, Volume 2006, Article ID 61859, Pages 1-15. 12. M. Shahram, and P. Milanfar, “Statistical and Information
Exclusion Constraints Facilitate Statistical Word Learning
ERIC Educational Resources Information Center
Yoshida, Katherine; Rhemtulla, Mijke; Vouloumanos, Athena
2012-01-01
The roles of linguistic, cognitive, and social-pragmatic processes in word learning are well established. If statistical mechanisms also contribute to word learning, they must interact with these processes; however, there exists little evidence for such mechanistic synergy. Adults use co-occurrence statistics to encode speech-object pairings with…
Dissociable behavioural outcomes of visual statistical learning.
Bays, Brett C; Turk-Browne, Nicholas B; Seitz, Aaron R
Statistical learning refers to the extraction of probabilistic relationships between stimuli and is increasingly used as a method to understand learning processes. However, numerous cognitive processes are sensitive to the statistical relationships between stimuli and any one measure of learning may conflate these processes; to date little research has focused on differentiating these processes. To understand how multiple processes underlie statistical learning, here we compared, within the same study, operational measures of learning from different tasks that may be differentially sensitive to these processes. In Experiment 1, participants were visually exposed to temporal regularities embedded in a stream of shapes. Their task was to periodically detect whether a shape, whose contrast was staircased to a threshold level, was present or absent. Afterwards, they completed a search task, where statistically predictable shapes were found more quickly. We used the search task to label shape pairs as "learned" or "non-learned", and then used these labels to analyse the detection task. We found a dissociation between learning on the search task and the detection task where only non-learned pairs showed learning effects in the detection task. This finding was replicated in further experiments with recognition memory (Experiment 2) and associative learning tasks (Experiment 3). Taken together, these findings are consistent with the view that statistical learning may comprise a family of processes that can produce dissociable effects on different aspects of behaviour.
Chetail, Fabienne
2017-06-01
Individuals rapidly become sensitive to recurrent patterns present in the environment and this occurs in many situations. However, evidence of a role for statistical learning of orthographic regularities in reading is mixed, and its role has peripheral status in current theories of visual word recognition. Additionally, exactly which regularities readers learn to be sensitive to is still unclear. To address these two issues, three experiments were conducted with artificial scripts. In Experiments 1a and 1b, participants were exposed to a flow of artificial words (five characters) for a few minutes, with either two or four bigrams occurring very frequently. In Experiment 2, exposure took place over several days while participants had to learn the orthographic and phonological forms of new words entailing or not frequent bigrams. Sensitivity to these regularities was then tested in a wordlikeness task. Finally, participants performed a letter detection task, with letters being either of high frequency or not in the exposure phase. The results of the wordlikeness task showed that after only a few minutes, readers become sensitive to the positional frequency of letter clusters and to bigram frequency beyond single letter frequency. Moreover, this new knowledge influenced the performance in the letter detection task, with high-frequency letters being detected more rapidly than low-frequency ones. We discuss the implications of such results for models of orthographic encoding and reading.
Learning harmony: the role of serial statistics.
Jonaitis, Erin McMullen; Saffran, Jenny R
2009-07-01
How do listeners learn about the statistical regularities underlying musical harmony? In traditional Western music, certain chords predict the occurrence of other chords: Given a particular chord, not all chords are equally likely to follow. In Experiments 1 and 2, we investigated whether adults make use of statistical information when learning new musical structures. Listeners were exposed to a novel musical system containing phrases generated using an artificial grammar. This new system contained statistical structure quite different from Western tonal music. Our results suggest that learners take advantage of the statistical patterning of chords to acquire new musical structures, similar to learning processes previously observed for language learning.
Dissociable behavioural outcomes of visual statistical learning
Turk-Browne, Nicholas B.; Seitz, Aaron R.
2016-01-01
Statistical learning refers to the extraction of probabilistic relationships between stimuli and is increasingly used as a method to understand learning processes. However, numerous cognitive processes are sensitive to the statistical relationships between stimuli and any one measure of learning may conflate these processes; to date little research has focused on differentiating these processes. To understand how multiple processes underlie statistical learning, here we compared, within the same study, operational measures of learning from different tasks that may be differentially sensitive to these processes. In Experiment 1, participants were visually exposed to temporal regularities embedded in a stream of shapes. Their task was to periodically detect whether a shape, whose contrast was staircased to a threshold level, was present or absent. Afterwards, they completed a search task, where statistically predictable shapes were found more quickly. We used the search task to label shape pairs as “learned” or “non-learned”, and then used these labels to analyse the detection task. We found a dissociation between learning on the search task and the detection task where only non-learned pairs showed learning effects in the detection task. This finding was replicated in further experiments with recognition memory (Experiment 2) and associative learning tasks (Experiment 3). Taken together, these findings are consistent with the view that statistical learning may comprise a family of processes that can produce dissociable effects on different aspects of behaviour. PMID:27478399
François, Clément; Schön, Daniele
2014-02-01
There is increasing evidence that humans and other nonhuman mammals are sensitive to the statistical structure of auditory input. Indeed, neural sensitivity to statistical regularities seems to be a fundamental biological property underlying auditory learning. In the case of speech, statistical regularities play a crucial role in the acquisition of several linguistic features, from phonotactic to more complex rules such as morphosyntactic rules. Interestingly, a similar sensitivity has been shown with non-speech streams: sequences of sounds changing in frequency or timbre can be segmented on the sole basis of conditional probabilities between adjacent sounds. We recently ran a set of cross-sectional and longitudinal experiments showing that merging music and speech information in song facilitates stream segmentation and, further, that musical practice enhances sensitivity to statistical regularities in speech at both neural and behavioral levels. Based on recent findings showing the involvement of a fronto-temporal network in speech segmentation, we defend the idea that enhanced auditory learning observed in musicians originates via at least three distinct pathways: enhanced low-level auditory processing, enhanced phono-articulatory mapping via the left Inferior Frontal Gyrus and Pre-Motor cortex and increased functional connectivity within the audio-motor network. Finally, we discuss how these data predict a beneficial use of music for optimizing speech acquisition in both normal and impaired populations.
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…
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…
Statistics for Learning Genetics
ERIC Educational Resources Information Center
Charles, Abigail Sheena
2012-01-01
This study investigated the knowledge and skills that biology students may need to help them understand statistics/mathematics as it applies to genetics. The data are based on analyses of current representative genetics texts, practicing genetics professors' perspectives, and more directly, students' perceptions of, and performance in, doing…
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…
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…
Teaching Statistics through Learning Projects
ERIC Educational Resources Information Center
Moreira da Silva, Mauren Porciúncula; Pinto, Suzi Samá
2014-01-01
This paper aims to reflect on the teaching of statistics through student research, in the form of projects carried out by students on self-selected topics. The paper reports on a study carried out with two undergraduate classes using a methodology of teaching that we call "learning projects." Monitoring the development of the various…
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…
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…
Role of attention and perceptual grouping in visual statistical learning.
Baker, Chris I; Olson, Carl R; Behrmann, Marlene
2004-07-01
Statistical learning has been widely proposed as a mechanism by which observers learn to decompose complex sensory scenes. To determine how robust statistical learning is, we investigated the impact of attention and perceptual grouping on statistical learning of visual shapes. Observers were presented with stimuli containing two shapes that were either connected by a bar or unconnected. When observers were required to attend to both locations at which shapes were presented, the degree of statistical learning was unaffected by whether the shapes were connected or not. However, when observers were required to attend to just one of the shapes' locations, statistical learning was observed only when the shapes were connected. These results demonstrate that visual statistical learning is not just a passive process. It can be modulated by both attention and connectedness, and in natural scenes these factors may constrain the role of stimulus statistics in learning.
Impaired Statistical Learning in Developmental Dyslexia
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
Statistical learning across development: flexible yet constrained.
Krogh, Lauren; Vlach, Haley A; Johnson, Scott P
2012-01-01
Much research in the past two decades has documented infants' and adults' ability to extract statistical regularities from auditory input. Importantly, recent research has extended these findings to the visual domain, demonstrating learners' sensitivity to statistical patterns within visual arrays and sequences of shapes. In this review we discuss both auditory and visual statistical learning to elucidate both the generality of and constraints on statistical learning. The review first outlines the major findings of the statistical learning literature with infants, followed by discussion of statistical learning across domains, modalities, and development. The second part of this review considers constraints on statistical learning. The discussion focuses on two categories of constraint: constraints on the types of input over which statistical learning operates and constraints based on the state of the learner. The review concludes with a discussion of possible mechanisms underlying statistical learning.
Statistical learning and selective inference
Taylor, Jonathan; Tibshirani, Robert J.
2015-01-01
We describe the problem of “selective inference.” This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have “cherry-picked”—searched for the strongest associations—means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis. PMID:26100887
Statistical learning and selective inference.
Taylor, Jonathan; Tibshirani, Robert J
2015-06-23
We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.
Early language acquisition: Statistical learning and social learning
NASA Astrophysics Data System (ADS)
Kuhl, Patricia K.
2003-10-01
Infants are sensitive to the statistical patterns in language input, and exposure to them alters phonetic perception. Our recent data indicate that first-time exposure to a foreign language at 9 months of age results in learning after only 5 h, suggesting a process that is fairly automatic, given natural language input. At the same time, it appears that early phonetic learning from natural language may be constrained by the need for social interaction. Our work demonstrates that infants learn phonetically when exposed to a live, but not a pre-recorded, speaker. This talk will focus on statistical learning in a social context and develop the thesis that this combination provides an ideal situation for the acquisition of a natural language.
Visual Statistical Learning in the Newborn Infant
ERIC Educational Resources Information Center
Bulf, Hermann; Johnson, Scott P.; Valenza, Eloisa
2011-01-01
Statistical learning--implicit learning of statistical regularities within sensory input--is a way of acquiring structure within continuous sensory environments. Statistics computation, initially shown to be involved in word segmentation, has been demonstrated to be a general mechanism that operates across domains, across time and space, and…
Transforming Elementary Statistics To Enhance Student Learning.
ERIC Educational Resources Information Center
Lane, Jill L.; Aleksic, Maja
Undergraduate students often leave statistics courses not fully understanding how to apply statistical concepts (M. Bonsangue, 1994). In order to enhance student learning and improve the understanding and application of statistical concepts, an elementary statistics course was transformed from a lecture-based course into one that integrates…
iMinerva: a mathematical model of distributional statistical learning.
Thiessen, Erik D; Pavlik, Philip I
2013-03-01
Statistical learning refers to the ability to identify structure in the input based on its statistical properties. For many linguistic structures, the relevant statistical features are distributional: They are related to the frequency and variability of exemplars in the input. These distributional regularities have been suggested to play a role in many different aspects of language learning, including phonetic categories, using phonemic distinctions in word learning, and discovering non-adjacent relations. On the surface, these different aspects share few commonalities. Despite this, we demonstrate that the same computational framework can account for learning in all of these tasks. These results support two conclusions. The first is that much, and perhaps all, of distributional statistical learning can be explained by the same underlying set of processes. The second is that some aspects of language can be learned due to domain-general characteristics of memory.
Representational Versatility in Learning Statistics
ERIC Educational Resources Information Center
Graham, Alan T.; Thomas, Michael O. J.
2005-01-01
Statistical data can be represented in a number of qualitatively different ways, the choice depending on the following three conditions: the concepts to be investigated; the nature of the data; and the purpose for which they were collected. This paper begins by setting out frameworks that describe the nature of statistical thinking in schools, and…
Functional differences between statistical learning with and without explicit training
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
Statistical Inference at Work: Statistical Process Control as an Example
ERIC Educational Resources Information Center
Bakker, Arthur; Kent, Phillip; Derry, Jan; Noss, Richard; Hoyles, Celia
2008-01-01
To characterise statistical inference in the workplace this paper compares a prototypical type of statistical inference at work, statistical process control (SPC), with a type of statistical inference that is better known in educational settings, hypothesis testing. Although there are some similarities between the reasoning structure involved in…
How implicit is visual statistical learning?
Bertels, Julie; Franco, Ana; Destrebecqz, Arnaud
2012-09-01
In visual statistical learning, participants learn the statistical regularities present in a sequence of visual shapes. A recent study (Kim, Seitz, Feenstra, & Shams, 2009) suggests that visual statistical learning occurs implicitly, as it is not accompanied by conscious awareness of these regularities. However, that interpretation of the data depends on 2 unwarranted assumptions concerning the nature and sensitivity of the tasks used to measure learning. In a replication of this study, we used a 4-choice completion task as a direct measure of learning, in addition to an indirect measure consisting of a rapid serial visual presentation task. Moreover, binary confidence judgments were recorded after each completion trial. This way, we measured systematically the extent to which sequence knowledge was available to consciousness. Supporting the notion that the role of unconscious knowledge was overestimated in Kim et al.'s study, our results reveal that participants' performance cannot be exclusively accounted for by implicit knowledge.
Is statistical learning constrained by lower level perceptual organization?
Emberson, Lauren L; Liu, Ran; Zevin, Jason D
2013-07-01
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 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.
Rhythmic Grouping Biases Constrain Infant Statistical Learning
ERIC Educational Resources Information Center
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…
Screencast Tutorials Enhance Student Learning of Statistics
ERIC Educational Resources Information Center
Lloyd, Steven A.; Robertson, Chuck L.
2012-01-01
Although the use of computer-assisted instruction has rapidly increased, there is little empirical research evaluating these technologies, specifically within the context of teaching statistics. The authors assessed the effect of screencast tutorials on learning outcomes, including statistical knowledge, application, and interpretation. Students…
Two Learning Activities for a Large Introductory Statistics Class
ERIC Educational Resources Information Center
Zacharopoulou, Hrissoula
2006-01-01
In a very large Introductory Statistics class, i.e. in a class of more than 300 students, instructors may hesitate to apply active learning techniques, discouraged by the volume of extra work. In this paper two such activities are presented that evoke student involvement in the learning process. The first is group peer teaching and the second is…
Is Statistical Learning Constrained by Lower Level Perceptual Organization?
ERIC Educational Resources Information Center
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…
Statistical Process Control for KSC Processing
NASA Technical Reports Server (NTRS)
Ford, Roger G.; Delgado, Hector; Tilley, Randy
1996-01-01
The 1996 Summer Faculty Fellowship Program and Kennedy Space Center (KSC) served as the basis for a research effort into statistical process control for KSC processing. The effort entailed several tasks and goals. The first was to develop a customized statistical process control (SPC) course for the Safety and Mission Assurance Trends Analysis Group. The actual teaching of this course took place over several weeks. In addition, an Internet version of the same course complete with animation and video excerpts from the course when it was taught at KSC was developed. The application of SPC to shuttle processing took up the rest of the summer research project. This effort entailed the evaluation of SPC use at KSC, both present and potential, due to the change in roles for NASA and the Single Flight Operations Contractor (SFOC). Individual consulting on SPC use was accomplished as well as an evaluation of SPC software for KSC use in the future. A final accomplishment of the orientation of the author to NASA changes, terminology, data format, and new NASA task definitions will allow future consultation when the needs arise.
ERIC Educational Resources Information Center
Mirman, Daniel; Estes, Katharine Graf; Magnuson, James S.
2010-01-01
Statistical learning mechanisms play an important role in theories of language acquisition and processing. Recurrent neural network models have provided important insights into how these mechanisms might operate. We examined whether such networks capture two key findings in human statistical learning. In Simulation 1, a simple recurrent network…
Visual statistical learning of shape sequences: an ERP study.
Abla, Dilshat; Okanoya, Kazuo
2009-06-01
Behavioral experiments have found that infants and adults learn statistically defined patterns presented in auditory and visual input sequences in the same manner regardless of whether the input was linguistic (syllables) or nonlinguistic (tones and shapes). In order do determine the time course and neural processes involved in online word segmentation and statistical learning of visual sequence, we recorded event-related potentials (ERPs) while participants were exposed to continuous sequences with elements organized into shape-words randomly connected to each other. After viewing three 6.6min sessions of sequences, the participants performed a behavioral choice test. The participants were divided into two groups (high and low learners) based on their behavioral performance. The overall mean performance was 72.2%, indicating that the shape sequence was segmented and that the participants learned the shape-triplets statistically. Grand-averaged ERPs showed that triplet-onset (the initial shapes of shape-words) elicited larger N400 amplitudes than did middle and final shapes embedded in continuous streams during the early learning sessions of high learners, but no triplet-onset effect was found among low learners. The results suggested that the N400 effect indicated online segmentation of the visual sequence and the degree of statistical learning. Our results also imply that statistical learning represents a common learning device.
Can statistical learning bootstrap the integers?
Rips, Lance J; Asmuth, Jennifer; Bloomfield, Amber
2013-09-01
This paper examines Piantadosi, Tenenbaum, and Goodman's (2012) model for how children learn the relation between number words ("one" through "ten") and cardinalities (sizes of sets with one through ten elements). This model shows how statistical learning can induce this relation, reorganizing its procedures as it does so in roughly the way children do. We question, however, Piantadosi et al.'s claim that the model performs "Quinian bootstrapping," in the sense of Carey (2009). Unlike bootstrapping, the concept it learns is not discontinuous with the concepts it starts with. Instead, the model learns by recombining its primitives into hypotheses and confirming them statistically. As such, it accords better with earlier claims (Fodor, 1975, 1981) that learning does not increase expressive power. We also question the relevance of the simulation for children's learning. The model starts with a preselected set of15 primitives, and the procedure it learns differs from children's method. Finally, the partial knowledge of the positive integers that the model attains is consistent with an infinite number of nonstandard meanings-for example, that the integers stop after ten or loop from ten back to one.
The Statistical Determinants of the Speed of Motor Learning
He, Kang; Liang, You; Abdollahi, Farnaz; Fisher Bittmann, Moria; Kording, Konrad; Wei, Kunlin
2016-01-01
It has recently been suggested that movement variability directly increases the speed of motor learning. Here we use computational modeling of motor adaptation to show that variability can have a broad range of effects on learning, both negative and positive. Experimentally, we also find contributing and decelerating effects. Lastly, through a meta-analysis of published papers, we verify that across a wide range of experiments, movement variability has no statistical relation with learning rate. While motor learning is a complex process that can be modeled, further research is needed to understand the relative importance of the involved factors. PMID:27606808
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)…
ERIC Educational Resources Information Center
Criss, Ellen
2008-01-01
Teacher-educator and researcher Daniel L. Kohut suggests in "Musical Performance: Learning Theory and Pedagogy" that there are many problems that result from the way music teachers often teach. Most teachers focus on the process, not the goal. The Natural Learning Process that Kohut advocates is the same process that young children use when they…
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…
Statistical learning of novel graphotactic constraints in children and adults.
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.
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…
Learning algorithms for perceptrons from statistical physics
NASA Astrophysics Data System (ADS)
Gordon, Mirta B.; Peretto, Pierre; Berchier, Dominique
1993-02-01
Learning algorithms for perceptrons are deduced from statistical mechanics. Thermodynamical quantities are used as cost functions which may be extremalized by gradient dynamics to find the synaptic efficacies that store the learning set of patterns. The learning rules so obtained are classified in two categories, following the statistics used to derive the cost functions, namely, Boltzmann statistics, and Fermi statistics. In the limits of zero or infinite temperatures some of the rules behave like already known algorithms, but new strategies for learning are obtained at finite temperatures, which minimize the number of errors on the training set. Nous déduisons des algorithmes d'apprentissage pour des perceptrons à partir de considérations de mécanique statistique. Des quantités thermodynamiques sont considérées comme des fonctions de coût, dont on obtient, par une dynamique de gradient, les efficacités synaptiques qui apprennent l'ensemble d'apprentissage. Les règles ainsi obtenues sont classées en deux catégories suivant les statistiques, de Boltzmann ou de Fermi, utilisées pour dériver les fonctions de coût. Dans les limites de températures nulle ou infinie, la plupart des règles trouvées tendent vers les algorithmes connus, mais à température finie on trouve des stratégies nouvelles, qui minimisent le nombre d'erreurs dans l'ensemble d'apprentissage.
Context influences conscious appraisal of cross situational statistical learning.
Poepsel, Timothy J; Weiss, Daniel J
2014-01-01
Previous research in cross-situational statistical learning has established that people can track statistical information across streams in order to map nonce words to their referent objects (Yu and Smith, 2007). Under some circumstances, learners are able to acquire multiple mappings for a single object (e.g., Yurovsky and Yu, 2008). Here we explore whether having a contextual cue associated with a new mapping may facilitate this process, or the conscious awareness of learning. Using a cross-situational statistical learning paradigm, in which learners could form both 1:1 and 2:1 word-object mappings over two phases of learning, we collected confidence ratings during familiarization and provided a retrospective test to gage learning. In Condition 1, there were no contextual cues to indicate a change in mappings (baseline). Conditions 2 and 3 added contextual cues (a change in speaker voice or explicit instructions, respectively) to the second familiarization phase to determine their effects on the trajectory of learning. While contextual cues did not facilitate acquisition of 2:1 mappings as assessed by retrospective measures, confidence ratings for these mappings were significantly higher in contextual cue conditions compared to the baseline condition with no cues. These results suggest that contextual cues corresponding to changes in the input may influence the conscious awareness of learning.
Which statistics should tropical biologists learn?
Loaiza Velásquez, Natalia; González Lutz, María Isabel; Monge-Nájera, Julián
2011-09-01
Tropical biologists study the richest and most endangered biodiversity in the planet, and in these times of climate change and mega-extinctions, the need for efficient, good quality research is more pressing than in the past. However, the statistical component in research published by tropical authors sometimes suffers from poor quality in data collection; mediocre or bad experimental design and a rigid and outdated view of data analysis. To suggest improvements in their statistical education, we listed all the statistical tests and other quantitative analyses used in two leading tropical journals, the Revista de Biología Tropical and Biotropica, during a year. The 12 most frequent tests in the articles were: Analysis of Variance (ANOVA), Chi-Square Test, Student's T Test, Linear Regression, Pearson's Correlation Coefficient, Mann-Whitney U Test, Kruskal-Wallis Test, Shannon's Diversity Index, Tukey's Test, Cluster Analysis, Spearman's Rank Correlation Test and Principal Component Analysis. We conclude that statistical education for tropical biologists must abandon the old syllabus based on the mathematical side of statistics and concentrate on the correct selection of these and other procedures and tests, on their biological interpretation and on the use of reliable and friendly freeware. We think that their time will be better spent understanding and protecting tropical ecosystems than trying to learn the mathematical foundations of statistics: in most cases, a well designed one-semester course should be enough for their basic requirements.
Mature students learning statistics: The activity theory perspective
NASA Astrophysics Data System (ADS)
Gordon, Sue
1993-09-01
The concept of approach "stresses relationships between intention, process and outcome within a specified context as described by an individual" (Schmeck, 1988, p. 10). This paper explores the approaches to learning of a group of mature students from the theoretical perspective of activity theory in order to gain an insight into some of the ways statistics is learned. In this framework, learning, regarded as goal-directed behaviour, is analysed by exploring the socio-historical factors relating to students' self regulation of their cognitive activities. The material is derived from questionnaires and interviews with five students, and focuses on the students' own interpretations of the contexts affecting their approaches.
Process chemistry {ampersand} statistics quality assurance plan
Meznarich, H.K.
1996-08-01
This document provides quality assurance guidelines and quality control requirements for Process Chemistry and Statistics. This document is designed on the basis of Hanford Analytical Services Quality Assurance Plan (HASQAP) technical guidelines and is used for governing process chemistry activities.
Statistical learning of action: the role of conditional probability.
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.
Implicit Statistical Learning and Language Skills in Bilingual Children
ERIC Educational Resources Information Center
Yim, Dongsun; Rudoy, John
2013-01-01
Purpose: Implicit statistical learning in 2 nonlinguistic domains (visual and auditory) was used to investigate (a) whether linguistic experience influences the underlying learning mechanism and (b) whether there are modality constraints in predicting implicit statistical learning with age and language skills. Method: Implicit statistical learning…
Distance Learning Fiscal and Statistical Report, 2000-2001.
ERIC Educational Resources Information Center
Peltz, Steve
This Distance Learning Fiscal and Statistical Report is an annual publication designed to document statistical and financial aspects of the Distance Learning Program at West Valley College (Saratoga, California). In addition to presenting comparative distance learning course statistics for the last 15 years, this report presents a thorough review…
Statistical mechanics of learning from examples
NASA Astrophysics Data System (ADS)
Seung, H. S.; Sompolinsky, H.; Tishby, N.
1992-04-01
Learning from examples in feedforward neural networks is studied within a statistical-mechanical framework. Training is assumed to be stochastic, leading to a Gibbs distribution of networks characterized by a temperature parameter T. Learning of realizable rules as well as of unrealizable rules is considered. In the latter case, the target rule cannot be perfectly realized by a network of the given architecture. Two useful approximate theories of learning from examples are studied: the high-temperature limit and the annealed approximation. Exact treatment of the quenched disorder generated by the random sampling of the examples leads to the use of the replica theory. Of primary interest is the generalization curve, namely, the average generalization error ɛg versus the number of examples P used for training. The theory implies that, for a reduction in ɛg that remains finite in the large-N limit, P should generally scale as αN, where N is the number of independently adjustable weights in the network. We show that for smooth networks, i.e., those with continuously varying weights and smooth transfer functions, the generalization curve asymptotically obeys an inverse power law. In contrast, for nonsmooth networks other behaviors can appear, depending on the nature of the nonlinearities as well as the realizability of the rule. In particular, a discontinuous learning transition from a state of poor to a state of perfect generalization can occur in nonsmooth networks learning realizable rules. We illustrate both gradual and continuous learning with a detailed analytical and numerical study of several single-layer perceptron models. Comparing with the exact replica theory of perceptron learning, we find that for realizable rules the high-temperature and annealed theories provide very good approximations to the generalization performance. Assuming this to hold for multilayer networks as well, we propose a classification of possible asymptotic forms of learning curves
Learning Processes and Learning Outcomes
1992-06-01
some have been shown to interact with other learner traits (i.e., learning styles like impulsivity) to impact learning. Let’s now consider in more detail...the second conative factor--learning style. Learning styles , in the broadest sense, refer to "general behavioral dispositions that characterize...manipulated and thus are more transitory in nature, learning styles are comparatively more stable. However, style does imply a choice by the learner as
Memory constraints on infants' cross-situational statistical learning.
Vlach, Haley A; Johnson, Scott P
2013-06-01
Infants are able to map linguistic labels to referents in the world by tracking co-occurrence probabilities across learning events, a behavior often termed cross-situational statistical learning. This study builds upon existing research by examining infants' developing ability to aggregate and retrieve word-referent pairings over time. 16- and 20-month-old infants (N=32) were presented with a cross-situational statistical learning task in which half of the object-label pairings were presented in immediate succession (massed) and half were distributed across time (interleaved). Results revealed striking developmental differences in word mapping performance; infants in both age groups were able to learn pairings presented in immediate succession, but only 20-month-old infants were able to correctly infer pairings distributed over time. This work reveals significant constraints on infants' ability to aggregate and retrieve object-label pairings across time and challenges theories of cross-situational statistical learning that rest on retrieval processes as successful and automatic.
Dimension-based statistical learning of vowels.
Liu, Ran; Holt, Lori L
2015-12-01
Speech perception depends on long-term representations that reflect regularities of the native language. However, listeners rapidly adapt when speech acoustics deviate from these regularities due to talker idiosyncrasies such as foreign accents and dialects. To better understand these dual aspects of speech perception, we probe native English listeners' baseline perceptual weighting of 2 acoustic dimensions (spectral quality and vowel duration) toward vowel categorization and examine how they subsequently adapt to an "artificial accent" that deviates from English norms in the correlation between the 2 dimensions. At baseline, listeners rely relatively more on spectral quality than vowel duration to signal vowel category, but duration nonetheless contributes. Upon encountering an "artificial accent" in which the spectral-duration correlation is perturbed relative to English language norms, listeners rapidly down-weight reliance on duration. Listeners exhibit this type of short-term statistical learning even in the context of nonwords, confirming that lexical information is not necessary to this form of adaptive plasticity in speech perception. Moreover, learning generalizes to both novel lexical contexts and acoustically distinct altered voices. These findings are discussed in the context of a mechanistic proposal for how supervised learning may contribute to this type of adaptive plasticity in speech perception.
DIMENSION-BASED STATISTICAL LEARNING OF VOWELS
Liu, Ran; Holt, Lori L.
2015-01-01
Speech perception depends on long-term representations that reflect regularities of the native language. However, listeners rapidly adapt when speech acoustics deviate from these regularities due to talker idiosyncrasies such as foreign accents and dialects. To better understand these dual aspects of speech perception, we probe native English listeners’ baseline perceptual weighting of two acoustic dimensions (spectral quality and vowel duration) towards vowel categorization and examine how they subsequently adapt to an “artificial accent” that deviates from English norms in the correlation between the two dimensions. At baseline, listeners rely relatively more on spectral quality than vowel duration to signal vowel category, but duration nonetheless contributes. Upon encountering an “artificial accent” in which the spectral-duration correlation is perturbed relative to English language norms, listeners rapidly down-weight reliance on duration. Listeners exhibit this type of short-term statistical learning even in the context of nonwords, confirming that lexical information is not necessary to this form of adaptive plasticity in speech perception. Moreover, learning generalizes to both novel lexical contexts and acoustically-distinct altered voices. These findings are discussed in the context of a mechanistic proposal for how supervised learning may contribute to this type of adaptive plasticity in speech perception. PMID:26280268
Statistical Learning as a Key to Cracking Chinese Orthographic Codes
ERIC Educational Resources Information Center
He, Xinjie; Tong, Xiuli
2017-01-01
This study examines statistical learning as a mechanism for Chinese orthographic learning among children in Grades 3-5. Using an artificial orthography, children were repeatedly exposed to positional, phonetic, and semantic regularities of radicals. Children showed statistical learning of all three regularities. Regularities' levels of consistency…
Predicting radiotherapy outcomes using statistical learning techniques
NASA Astrophysics Data System (ADS)
El Naqa, Issam; Bradley, Jeffrey D.; Lindsay, Patricia E.; Hope, Andrew J.; Deasy, Joseph O.
2009-09-01
Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model
Investigating implicit statistical learning mechanisms through contextual cueing.
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.
Concurrent movement impairs incidental but not intentional statistical learning.
Stevens, David J; Arciuli, Joanne; Anderson, David I
2015-07-01
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 learn these regularities. Experiment 1 demonstrated that while the control group were able to learn the statistical regularities, the resistance-free cycling group and the exercise group did not demonstrate learning. This is in contrast with the findings of Experiment 2, where all three groups demonstrated significant levels of learning. The results suggest that the movement demands, rather than the physiological stress, interfered with statistical learning. We suggest movement activates the striatum, which is not only responsible for motor control but also plays a role in incidental learning.
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 statistical learning to explore these questions. Participants viewed statistically structured versus unstructured sequences of shapes while performing a task unrelated to the structure. Robust neural responses to statistical structure were observed, and these responses were notable in four ways: First, responses to structure were observed in the striatum and medial temporal lobe, suggesting that statistical learning may be related to other forms of associative learning and relational memory. Second, statistical regularities yielded greater activation in category-specific visual regions (object-selective lateral occipital cortex and word-selective ventral occipito-temporal cortex), demonstrating that these regions are sensitive to information distributed in time. Third, evidence of learning emerged early during familiarization, showing that statistical learning can operate very quickly and with little exposure. Finally, neural signatures of learning were dissociable from subsequent explicit familiarity, suggesting that learning can occur in the absence of awareness. Overall, our findings help elucidate the underlying nature of statistical learning. PMID:18823241
Predicting radiotherapy outcomes using statistical learning techniques.
El Naqa, Issam; Bradley, Jeffrey D; Lindsay, Patricia E; Hope, Andrew J; Deasy, Joseph O
2009-09-21
Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model
Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials.
Potter, Christine E; Wang, Tianlin; Saffran, Jenny R
2016-12-18
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.
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…
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.
Applied Behavior Analysis and Statistical Process Control?
ERIC Educational Resources Information Center
Hopkins, B. L.
1995-01-01
Incorporating statistical process control (SPC) methods into applied behavior analysis is discussed. It is claimed that SPC methods would likely reduce applied behavior analysts' intimate contacts with problems and would likely yield poor treatment and research decisions. Cases and data presented by Pfadt and Wheeler (1995) are cited as examples.…
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…
Analogical Processes in Learning
1980-09-15
CLASSICATMN OF ThIS PA5UrSfie Dule gIt.,.d ANALOGICAL PROCESSES IN LEARNING David E . Rumelhart & Donald A. Norman Program in Cognitive Science Center...for Human Information Processing University of California, San Diego La Jolla, California 92093 Copyright Q) 1980 David E . Rumelhart & Donald A. Norman...4) SEARS 7 E Ilamnp C5P Tota $.600 Aprovd.L. Figure 1. Stimuli for the two conditions of D’Andrade’s reasoning * experiment. The left panel shows the
Stable Measures and Processes in Statistical Physics.
1984-11-01
IN STATISTICAL PHYSICS 0 by Aleksander Weron and Karina Weron Tecnicl Rpor #8Th November 1984) 0 0 85 01 16 099 0 L-SYCLASS1IEA.EDTISAG SfCUAI;V...Stochastic Processes" Department of Statistics J______ University of North Carolina Chapel Hill, NC 27514 f n -- and , -" ::i 7Codes ot : ’r ; Karina Weron...2 p.-t ,i7i;7, ~Department of Physics and Astronomy Louisiana State University Baton Rouge, LA 70803 Abstract. It is shown how a-stable distributions
Statistical Learning of Phonetic Categories: Insights from a Computational Approach
ERIC Educational Resources Information Center
McMurray, Bob; Aslin, Richard N.; Toscano, Joseph C.
2009-01-01
Recent evidence (Maye, Werker & Gerken, 2002) suggests that statistical learning may be an important mechanism for the acquisition of phonetic categories in the infant's native language. We examined the sufficiency of this hypothesis and its implications for development by implementing a statistical learning mechanism in a computational model…
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…
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…
Koelsch, Stefan; Busch, Tobias; Jentschke, Sebastian; Rohrmeier, Martin
2016-01-01
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. PMID:26830652
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.
Vocal learning is constrained by the statistics of sensorimotor experience.
Sober, Samuel J; Brainard, Michael S
2012-12-18
The brain uses sensory feedback to correct behavioral errors. Larger errors by definition require greater corrections, and many models of learning assume that larger sensory feedback errors drive larger motor changes. However, an alternative perspective is that larger errors drive learning less effectively because such errors fall outside the range of errors normally experienced and are therefore unlikely to reflect accurate feedback. This is especially crucial in vocal control because auditory feedback can be contaminated by environmental noise or sensory processing errors. A successful control strategy must therefore rely on feedback to correct errors while disregarding aberrant auditory signals that would lead to maladaptive vocal corrections. We hypothesized that these constraints result in compensation that is greatest for smaller imposed errors and least for larger errors. To test this hypothesis, we manipulated the pitch of auditory feedback in singing Bengalese finches. We found that learning driven by larger sensory errors was both slower than that resulting from smaller errors and showed less complete compensation for the imposed error. Additionally, we found that a simple principle could account for these data: the amount of compensation was proportional to the overlap between the baseline distribution of pitch production and the distribution experienced during the shift. Correspondingly, the fraction of compensation approached zero when pitch was shifted outside of the song's baseline pitch distribution. Our data demonstrate that sensory errors drive learning best when they fall within the range of production variability, suggesting that learning is constrained by the statistics of sensorimotor experience.
Statistical learning leads to persistent memory: Evidence for one-year consolidation.
Kóbor, Andrea; Janacsek, Karolina; Takács, Ádám; Nemeth, Dezso
2017-04-10
Statistical learning is a robust mechanism of the brain that enables the extraction of environmental patterns, which is crucial in perceptual and cognitive domains. However, the dynamical change of processes underlying long-term statistical memory formation has not been tested in an appropriately controlled design. Here we show that a memory trace acquired by statistical learning is resistant to inference as well as to forgetting after one year. Participants performed a statistical learning task and were retested one year later without further practice. The acquired statistical knowledge was resistant to interference, since after one year, participants showed similar memory performance on the previously practiced statistical structure after being tested with a new statistical structure. These results could be key to understand the stability of long-term statistical knowledge.
Learning across Senses: Cross-Modal Effects in Multisensory Statistical Learning
ERIC Educational Resources Information Center
Mitchel, Aaron D.; Weiss, Daniel J.
2011-01-01
It is currently unknown whether statistical learning is supported by modality-general or modality-specific mechanisms. One issue within this debate concerns the independence of learning in one modality from learning in other modalities. In the present study, the authors examined the extent to which statistical learning across modalities is…
Toward Understanding the Role of Technological Tools in Statistical Learning.
ERIC Educational Resources Information Center
Ben-Zvi, Dani
2000-01-01
Begins with some context setting on new views of statistics and statistical education reflected in the introduction of exploratory data analysis (EDA) into the statistics curriculum. Introduces a detailed example of an EDA learning activity in the middle school that makes use of the power of the spreadsheet to mediate students' construction of…
Thermodynamically reversible processes in statistical physics
NASA Astrophysics Data System (ADS)
Norton, John D.
2017-02-01
Equilibrium states are used as limit states to define thermodynamically reversible processes. When these processes are understood in terms of statistical physics, these limit states can change with time due to thermal fluctuations. For macroscopic systems, the changes are insignificant on ordinary time scales and what little change there is can be suppressed by macroscopically negligible, entropy-creating dissipation. For systems of molecular sizes, the changes are large on short time scales. They can only sometimes be suppressed with significant entropy-creating dissipation, and this entropy creation is unavoidable if any process is to proceed to completion. As a result, at molecular scales, thermodynamically reversible processes are impossible in principle. Unlike the macroscopic case, they cannot be realized even approximately when we account for all sources of dissipation, and argumentation invoking them on molecular scales can lead to spurious conclusions.
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…
2015-06-23
AFRL-OSR-VA-TR-2015-0143 DYNAMIC INFORMATION NETWORKS: GEOMETRY, TOPOLOGY, AND STATISTICAL LEARNING FOR THE ARTICULATION OF STRUCTURE Daniel Rockmore...and machine learning . 15. SUBJECT TERMS Information networks, machine learning , link prediction, hyperbolic geometry, multiscale networks, complex...ideas from linear algebra, markov processes, diffusion networks, differential geometry, and machine learning
Analyzing a Mature Software Inspection Process Using Statistical Process Control (SPC)
NASA Technical Reports Server (NTRS)
Barnard, Julie; Carleton, Anita; Stamper, Darrell E. (Technical Monitor)
1999-01-01
This paper presents a cooperative effort where the Software Engineering Institute and the Space Shuttle Onboard Software Project could experiment applying Statistical Process Control (SPC) analysis to inspection activities. The topics include: 1) SPC Collaboration Overview; 2) SPC Collaboration Approach and Results; and 3) Lessons Learned.
Statistical process control for radiotherapy quality assurance.
Pawlicki, Todd; Whitaker, Matthew; Boyer, Arthur L
2005-09-01
Every quality assurance process uncovers random and systematic errors. These errors typically consist of many small random errors and a very few number of large errors that dominate the result. Quality assurance practices in radiotherapy do not adequately differentiate between these two sources of error. The ability to separate these types of errors would allow the dominant source(s) of error to be efficiently detected and addressed. In this work, statistical process control is applied to quality assurance in radiotherapy for the purpose of setting action thresholds that differentiate between random and systematic errors. The theoretical development and implementation of process behavior charts are described. We report on a pilot project is which these techniques are applied to daily output and flatness/symmetry quality assurance for a 10 MV photon beam in our department. This clinical case was followed over 52 days. As part of our investigation, we found that action thresholds set using process behavior charts were able to identify systematic changes in our daily quality assurance process. This is in contrast to action thresholds set using the standard deviation, which did not identify the same systematic changes in the process. The process behavior thresholds calculated from a subset of the data detected a 2% change in the process whereas with a standard deviation calculation, no change was detected. Medical physicists must make decisions on quality assurance data as it is acquired. Process behavior charts help decide when to take action and when to acquire more data before making a change in the process.
Changing viewer perspectives reveals constraints to implicit visual statistical learning
Jiang, Yuhong V.; Swallow, Khena M.
2014-01-01
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. PMID:25294640
Experience and grammatical agreement: statistical learning shapes number agreement production.
Haskell, Todd R; Thornton, Robert; Macdonald, Maryellen C
2010-02-01
A robust result in research on the production of grammatical agreement is that speakers are more likely to produce an erroneous verb with phrases such as the key to the cabinets, with a singular noun followed by a plural one, than with phrases such as the keys to the cabinet, where a plural noun is followed by a singular. These asymmetries are thought to reflect core language production processes. Previous accounts have attributed error patterns to a syntactic number feature present on plurals but not singulars. An alternative approach is presented in which a process similar to structural priming contributes to the error asymmetry via speakers' past experiences with related agreement constructions. A corpus analysis and two agreement production studies test this account. The results suggest that agreement production is shaped by statistical learning from past language experience. Implications for accounts of agreement are discussed.
Experience and grammatical agreement: Statistical learning shapes number agreement production
Haskell, Todd R.; Thornton, Robert; MacDonald, Maryellen C.
2009-01-01
A robust result in research on the production of grammatical agreement is that speakers are more likely to produce an erroneous verb with phrases such as the key to the cabinets, with a singular noun followed by a plural one, than with phrases such as the keys to the cabinet, where a plural noun is followed by a singular. These asymmetries are thought to reflect core language production processes. Previous accounts have attributed error patterns to a syntactic number feature present on plurals but not singulars. An alternative approach is presented in which a process similar to structural priming contributes to the error asymmetry via speakers' past experiences with related agreement constructions. A corpus analysis and two agreement production studies test this account. The results suggest that agreement production is shaped by statistical learning from past language experience. Implications for accounts of agreement are discussed. PMID:19942213
Problem Based Learning and the scientific process
NASA Astrophysics Data System (ADS)
Schuchardt, Daniel Shaner
This research project was developed to inspire students to constructively use problem based learning and the scientific process to learn middle school science content. The student population in this study consisted of male and female seventh grade students. Students were presented with authentic problems that are connected to physical and chemical properties of matter. The intent of the study was to have students use the scientific process of looking at existing knowledge, generating learning issues or questions about the problems, and then developing a course of action to research and design experiments to model resolutions to the authentic problems. It was expected that students would improve their ability to actively engage with others in a problem solving process to achieve a deeper understanding of Michigan's 7th Grade Level Content Expectations, the Next Generation Science Standards, and a scientific process. Problem based learning was statistically effective in students' learning of the scientific process. Students statistically showed improvement on pre to posttest scores. The teaching method of Problem Based Learning was effective for seventh grade science students at Dowagiac Middle School.
Right Hemisphere Dominance in Visual Statistical Learning
ERIC Educational Resources Information Center
Roser, Matthew E.; Fiser, Jozsef; Aslin, Richard N.; Gazzaniga, Michael S.
2011-01-01
Several studies report a right hemisphere advantage for visuospatial integration and a left hemisphere advantage for inferring conceptual knowledge from patterns of covariation. The present study examined hemispheric asymmetry in the implicit learning of new visual feature combinations. A split-brain patient and normal control participants viewed…
Teaching, Learning and Assessing Statistical Problem Solving
ERIC Educational Resources Information Center
Marriott, John; Davies, Neville; Gibson, Liz
2009-01-01
In this paper we report the results from a major UK government-funded project, started in 2005, to review statistics and handling data within the school mathematics curriculum for students up to age 16. As a result of a survey of teachers we developed new teaching materials that explicitly use a problem-solving approach for the teaching and…
Probability & Statistics: Modular Learning Exercises. Student Edition
ERIC Educational Resources Information Center
Actuarial Foundation, 2012
2012-01-01
The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The materials are centered on the fictional town of Happy Shores, a coastal community which is at risk for hurricanes. Actuaries at an insurance company figure out the risks and…
Probability & Statistics: Modular Learning Exercises. Teacher Edition
ERIC Educational Resources Information Center
Actuarial Foundation, 2012
2012-01-01
The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The modules also introduce students to real world math concepts and problems that property and casualty actuaries come across in their work. They are designed to be used by teachers and…
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…
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…
Infants Learn about Objects from Statistics and People
ERIC Educational Resources Information Center
Wu, Rachel; Gopnik, Alison; Richardson, Daniel C.; Kirkham, Natasha Z.
2011-01-01
In laboratory experiments, infants are sensitive to patterns of visual features that co-occur (e.g., Fiser & Aslin, 2002). Once infants learn the statistical regularities, however, what do they do with that knowledge? Moreover, which patterns do infants learn in the cluttered world outside of the laboratory? Across 4 experiments, we show that…
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…
Memory Constraints on Infants' Cross-Situational Statistical Learning
ERIC Educational Resources Information Center
Vlach, Haley A.; Johnson, Scott P.
2013-01-01
Infants are able to map linguistic labels to referents in the world by tracking co-occurrence probabilities across learning events, a behavior often termed "cross-situational statistical learning." This study builds upon existing research by examining infants' developing ability to aggregate and retrieve word-referent pairings over time. 16- and…
Distant Melodies: Statistical Learning of Nonadjacent Dependencies in Tone Sequences
ERIC Educational Resources Information Center
Creel, Sarah C.; Newport, Elissa L.; Aslin, Richard N.
2004-01-01
Human listeners can keep track of statistical regularities among temporally adjacent elements in both speech and musical streams. However, for speech streams, when statistical regularities occur among nonadjacent elements, only certain types of patterns are acquired. Here, using musical tone sequences, the authors investigate nonadjacent learning.…
Isolated Words Enhance Statistical Language Learning in Infancy
ERIC Educational Resources Information Center
Lew-Williams, Casey; Pelucchi, Bruna; Saffran, Jenny R.
2011-01-01
Infants are adept at tracking statistical regularities to identify word boundaries in pause-free speech. However, researchers have questioned the relevance of statistical learning mechanisms to language acquisition, since previous studies have used simplified artificial languages that ignore the variability of real language input. The experiments…
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…
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.
Continuum Statistics of the Airy2 Process
NASA Astrophysics Data System (ADS)
Corwin, Ivan; Quastel, Jeremy; Remenik, Daniel
2013-01-01
We develop an exact determinantal formula for the probability that the Airy_2 process is bounded by a function g on a finite interval. As an application, we provide a direct proof that {sup({A}2(x)-x^2)} is distributed as a GOE random variable. Both the continuum formula and the GOE result have applications in the study of the end point of an unconstrained directed polymer in a disordered environment. We explain Johansson's (Commun. Math. Phys. 242(1-2):277-329, 2003) observation that the GOE result follows from this polymer interpretation and exact results within that field. In a companion paper (Moreno Flores et al. in Commun. Math. Phys. 2012) these continuum statistics are used to compute the distribution of the endpoint of directed polymers.
Probability and Statistics in Astronomical Machine Learning and Data Minin
NASA Astrophysics Data System (ADS)
Scargle, Jeffrey
2012-03-01
Statistical issues peculiar to astronomy have implications for machine learning and data mining. It should be obvious that statistics lies at the heart of machine learning and data mining. Further it should be no surprise that the passive observational nature of astronomy, the concomitant lack of sampling control, and the uniqueness of its realm (the whole universe!) lead to some special statistical issues and problems. As described in the Introduction to this volume, data analysis technology is largely keeping up with major advances in astrophysics and cosmology, even driving many of them. And I realize that there are many scientists with good statistical knowledge and instincts, especially in the modern era I like to call the Age of Digital Astronomy. Nevertheless, old impediments still lurk, and the aim of this chapter is to elucidate some of them. Many experiences with smart people doing not-so-smart things (cf. the anecdotes collected in the Appendix here) have convinced me that the cautions given here need to be emphasized. Consider these four points: 1. Data analysis often involves searches of many cases, for example, outcomes of a repeated experiment, for a feature of the data. 2. The feature comprising the goal of such searches may not be defined unambiguously until the search is carried out, or perhaps vaguely even then. 3. The human visual system is very good at recognizing patterns in noisy contexts. 4. People are much easier to convince of something they want to believe, or already believe, as opposed to unpleasant or surprising facts. One can argue that all four are good things during the initial, exploratory phases of most data analysis. They represent the curiosity and creativity of the scientific process, especially during the exploration of data collections from new observational programs such as all-sky surveys in wavelengths not accessed before or sets of images of a planetary surface not yet explored. On the other hand, confirmatory scientific
Basic Learning Processes in Childhood.
ERIC Educational Resources Information Center
Reese, Hayne W.
This book is an introduction to the psychological study of basic learning processes in children. Written for students who are not majors in psychology and who do not have much familiarity with the technical vocabulary of psychology, it has two themes: even the most basic kinds of learning are included by cognitive processes or mental activities;…
Visual Modelling of Learning Processes
ERIC Educational Resources Information Center
Copperman, Elana; Beeri, Catriel; Ben-Zvi, Nava
2007-01-01
This paper introduces various visual models for the analysis and description of learning processes. The models analyse learning on two levels: the dynamic level (as a process over time) and the functional level. Two types of model for dynamic modelling are proposed: the session trace, which documents a specific learner in a particular learning…
New insights into statistical learning and chunk learning in implicit sequence acquisition.
Du, Yue; Clark, Jane E
2016-11-03
Implicit sequence learning is ubiquitous in our daily life. However, it is unclear whether the initial acquisition of sequences results from learning to chunk items (i.e., chunk learning) or learning the underlying statistical regularities (i.e., statistical learning). By grouping responses with or without a distinct chunk or statistical structure into segments and comparing these responses, previous studies have demonstrated both chunk and statistical learning. However, few studies have considered the response sequence as a whole and examined the temporal dependency of the entire sequence, where the temporal dependencies could disclose the internal representations of chunk and statistical learning. Participants performed a serial reaction time (SRT) task under different stimulus interval conditions. We found that sequence learning reflected by reaction time (RT) rather than motor improvements represented by movement time (MT). The temporal dependency of RT and MT revealed that both RT and MT displayed recursive patterns caused by biomechanical effects of response locations and foot transitions. Chunking was noticeable only in the presence of the recurring RT or MT but vanished after the recursive component was removed, implying that chunk formation may result from biomechanical constraints rather than learning itself. In addition, we observed notable first-order autocorrelations in RT. This trial-to-trial association enhanced as learning progressed regardless of stimulus intervals, reflecting the internal cognitive representation of the first-order stimulus contingencies. Our results suggest that initial acquisition of implicit sequences may arise from first-order statistical learning rather than chunk learning.
No Apparent Influence of Reward upon Visual Statistical Learning
Rogers, Leeland L.; Friedman, Kyle G.; Vickery, Timothy J.
2016-01-01
Humans are capable of detecting and exploiting a variety of environmental regularities, including stimulus-stimulus contingencies (e.g., visual statistical learning) and stimulus-reward contingencies. However, the relationship between these two types of learning is poorly understood. In two experiments, we sought evidence that the occurrence of rewarding events enhances or impairs visual statistical learning. Across all of our attempts to find such evidence, we employed a training stage during which we grouped shapes into triplets and presented triplets one shape at a time in an undifferentiated stream. Participants subsequently performed a surprise recognition task in which they were tested on their knowledge of the underlying structure of the triplets. Unbeknownst to participants, triplets were also assigned no-, low-, or high-reward status. In Experiments 1A and 1B, participants viewed shape streams while low and high rewards were “randomly” given, presented as low- and high-pitched tones played through headphones. Rewards were always given on the third shape of a triplet (Experiment 1A) or the first shape of a triplet (Experiment 1B), and high- and low-reward sounds were always consistently paired with the same triplets. Experiment 2 was similar to Experiment 1, except that participants were required to learn value associations of a subset of shapes before viewing the shape stream. Across all experiments, we observed significant visual statistical learning effects, but the strength of learning did not differ amongst no-, low-, or high-reward conditions for any of the experiments. Thus, our experiments failed to find any influence of rewards on statistical learning, implying that visual statistical learning may be unaffected by the occurrence of reward. The system that detects basic stimulus-stimulus regularities may operate independently of the system that detects reward contingencies. PMID:27853441
No Apparent Influence of Reward upon Visual Statistical Learning.
Rogers, Leeland L; Friedman, Kyle G; Vickery, Timothy J
2016-01-01
Humans are capable of detecting and exploiting a variety of environmental regularities, including stimulus-stimulus contingencies (e.g., visual statistical learning) and stimulus-reward contingencies. However, the relationship between these two types of learning is poorly understood. In two experiments, we sought evidence that the occurrence of rewarding events enhances or impairs visual statistical learning. Across all of our attempts to find such evidence, we employed a training stage during which we grouped shapes into triplets and presented triplets one shape at a time in an undifferentiated stream. Participants subsequently performed a surprise recognition task in which they were tested on their knowledge of the underlying structure of the triplets. Unbeknownst to participants, triplets were also assigned no-, low-, or high-reward status. In Experiments 1A and 1B, participants viewed shape streams while low and high rewards were "randomly" given, presented as low- and high-pitched tones played through headphones. Rewards were always given on the third shape of a triplet (Experiment 1A) or the first shape of a triplet (Experiment 1B), and high- and low-reward sounds were always consistently paired with the same triplets. Experiment 2 was similar to Experiment 1, except that participants were required to learn value associations of a subset of shapes before viewing the shape stream. Across all experiments, we observed significant visual statistical learning effects, but the strength of learning did not differ amongst no-, low-, or high-reward conditions for any of the experiments. Thus, our experiments failed to find any influence of rewards on statistical learning, implying that visual statistical learning may be unaffected by the occurrence of reward. The system that detects basic stimulus-stimulus regularities may operate independently of the system that detects reward contingencies.
Statistical Mechanics of Node-Perturbation Learning for Nonlinear Perceptron
NASA Astrophysics Data System (ADS)
Hara, Kazuyuki; Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato
2013-05-01
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. Node-perturbation learning with M linear perceptrons has previously been analyzed using the methods of statistical mechanics. It was shown that cross-talk noise, which originates from the error of the other outputs, increases the generalization error as the number of outputs increases. On the other hand, a nonlinear perceptron has several advantages over a linear perceptron, such as the ability to use nonlinear outputs, learnability, storage capacity, and so forth. However, node-perturbation for a nonlinear perceptron has yet to be analyzed theoretically. In this paper, we derive a learning rule of node-perturbation learning for a nonlinear perceptron within the framework of REINFORCE learning and analyze the learning behavior by using statistical mechanical methods. From the results, we found that the signal and cross-talk terms of the order parameter Q have different forms for a nonlinear perceptron. Moreover, the increase in the generalization error with increasing number of outputs is less than for a linear perceptron.
Statistical physics of media processes: Mediaphysics
NASA Astrophysics Data System (ADS)
Kuznetsov, Dmitri V.; Mandel, Igor
2007-04-01
The processes of mass communications in complicated social or sociobiological systems such as marketing, economics, politics, animal populations, etc. as a subject for the special scientific subbranch-“mediaphysics”-are considered in its relation with sociophysics. A new statistical physics approach to analyze these phenomena is proposed. A keystone of the approach is an analysis of population distribution between two or many alternatives: brands, political affiliations, or opinions. Relative distances between a state of a “person's mind” and the alternatives are measures of propensity to buy (to affiliate, or to have a certain opinion). The distribution of population by those relative distances is time dependent and affected by external (economic, social, marketing, natural) and internal (influential propagation of opinions, “word of mouth”, etc.) factors, considered as fields. Specifically, the interaction and opinion-influence field can be generalized to incorporate important elements of Ising-spin-based sociophysical models and kinetic-equation ones. The distributions were described by a Schrödinger-type equation in terms of Green's functions. The developed approach has been applied to a real mass-media efficiency problem for a large company and generally demonstrated very good results despite low initial correlations of factors and the target variable.
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.
Parameter Identifiability in Statistical Machine Learning: A Review.
Ran, Zhi-Yong; Hu, Bao-Gang
2017-02-09
This review examines the relevance of parameter identifiability for statistical models used in machine learning. In addition to defining main concepts, we address several issues of identifiability closely related to machine learning, showing the advantages and disadvantages of state-of-the-art research and demonstrating recent progress. First, we review criteria for determining the parameter structure of models from the literature. This has three related issues: parameter identifiability, parameter redundancy, and reparameterization. Second, we review the deep influence of identifiability on various aspects of machine learning from theoretical and application viewpoints. In addition to illustrating the utility and influence of identifiability, we emphasize the interplay among identifiability theory, machine learning, mathematical statistics, information theory, optimization theory, information geometry, Riemann geometry, symbolic computation, Bayesian inference, algebraic geometry, and others. Finally, we present a new perspective together with the associated challenges.
Statistical learning: a powerful mechanism that operates by mere exposure.
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.
Concept Learning through Image Processing.
ERIC Educational Resources Information Center
Cifuentes, Lauren; Yi-Chuan, Jane Hsieh
This study explored computer-based image processing as a study strategy for middle school students' science concept learning. Specifically, the research examined the effects of computer graphics generation on science concept learning and the impact of using computer graphics to show interrelationships among concepts during study time. The 87…
Learning as a Generative Process
ERIC Educational Resources Information Center
Wittrock, M. C.
2010-01-01
A cognitive model of human learning with understanding is introduced. Empirical research supporting the model, which is called the generative model, is summarized. The model is used to suggest a way to integrate some of the research in cognitive development, human learning, human abilities, information processing, and aptitude-treatment…
Infant Directed Speech Enhances Statistical Learning in Newborn Infants: An ERP Study.
Bosseler, Alexis N; 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.
Infant Directed Speech Enhances Statistical Learning in Newborn Infants: An ERP Study
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
Learning across senses: cross-modal effects in multisensory statistical learning.
Mitchel, Aaron D; Weiss, Daniel J
2011-09-01
It is currently unknown whether statistical learning is supported by modality-general or modality-specific mechanisms. One issue within this debate concerns the independence of learning in one modality from learning in other modalities. In the present study, the authors examined the extent to which statistical learning across modalities is independent by simultaneously presenting learners with auditory and visual streams. After establishing baseline rates of learning for each stream independently, they systematically varied the amount of audiovisual correspondence across 3 experiments. They found that learners were able to segment both streams successfully only when the boundaries of the audio and visual triplets were in alignment. This pattern of results suggests that learners are able to extract multiple statistical regularities across modalities provided that there is some degree of cross-modal coherence. They discuss the implications of their results in light of recent claims that multisensory statistical learning is guided by modality-independent mechanisms.
Statistical mechanics of learning with soft margin classifiers.
Risau-Gusman, S; Gordon, M B
2001-09-01
We study the typical learning properties of the recently introduced soft margin classifiers (SMCs), learning realizable and unrealizable tasks, with the tools of statistical mechanics. We derive analytically the behavior of the learning curves in the regime of very large training sets. We obtain exponential and power laws for the decay of the generalization error towards the asymptotic value, depending on the task and on general characteristics of the distribution of stabilities of the patterns to be learned. The optimal learning curves of the SMCs, which give the minimal generalization error, are obtained by tuning the coefficient controlling the trade-off between the error and the regularization terms in the cost function. If the task is realizable by the SMC, the optimal performance is better than that of a hard margin support vector machine and is very close to that of a Bayesian classifier.
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…
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)…
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…
High-Dimensional Statistical Learning: Roots, Justifications, and Potential Machineries
Zollanvari, Amin
2015-01-01
High-dimensional data generally refer to data in which the number of variables is larger than the sample size. Analyzing such datasets poses great challenges for classical statistical learning because the finite-sample performance of methods developed within classical statistical learning does not live up to classical asymptotic premises in which the sample size unboundedly grows for a fixed dimensionality of observations. Much work has been done in developing mathematical–statistical techniques for analyzing high-dimensional data. Despite remarkable progress in this field, many practitioners still utilize classical methods for analyzing such datasets. This state of affairs can be attributed, in part, to a lack of knowledge and, in part, to the ready-to-use computational and statistical software packages that are well developed for classical techniques. Moreover, many scientists working in a specific field of high-dimensional statistical learning are either not aware of other existing machineries in the field or are not willing to try them out. The primary goal in this work is to bring together various machineries of high-dimensional analysis, give an overview of the important results, and present the operating conditions upon which they are grounded. When appropriate, readers are referred to relevant review articles for more information on a specific subject. PMID:27081307
Human Motion Retrieval Based on Statistical Learning and Bayesian Fusion
Xiao, Qinkun; Song, Ren
2016-01-01
A novel motion retrieval approach based on statistical learning and Bayesian fusion is presented. The approach includes two primary stages. (1) In the learning stage, fuzzy clustering is utilized firstly to get the representative frames of motions, and the gesture features of the motions are extracted to build a motion feature database. Based on the motion feature database and statistical learning, the probability distribution function of different motion classes is obtained. (2) In the motion retrieval stage, the query motion feature is extracted firstly according to stage (1). Similarity measurements are then conducted employing a novel method that combines category-based motion similarity distances with similarity distances based on canonical correlation analysis. The two motion distances are fused using Bayesian estimation, and the retrieval results are ranked according to the fused values. The effectiveness of the proposed method is verified experimentally. PMID:27732673
Statistical learning of temporal community structure in the hippocampus.
Schapiro, Anna C; Turk-Browne, Nicholas B; Norman, Kenneth A; Botvinick, Matthew M
2016-01-01
The hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between adjacent items in a sequence. We test whether the hippocampus can learn higher-order structure using sequences that have no variance in transition probability and instead exhibit temporal community structure. We find that the hippocampus is indeed sensitive to this form of structure, as revealed by its representations, activity dynamics, and connectivity with other regions. These findings suggest that the hippocampus is a sophisticated learner of environmental regularities, able to uncover higher-order structure that requires sensitivity to overlapping associations.
Specificity of Dimension-Based Statistical Learning in Word Recognition
Idemaru, Kaori; Holt, Lori L.
2014-01-01
Speech perception flexibly adapts to short-term regularities of ambient speech input. Recent research demonstrates that the function of an acoustic dimension for speech categorization at a given time is relative to its relationship to the evolving distribution of dimensional regularity across time, and not simply to a fixed value along the dimension. Two experiments examine the nature of this dimension-based statistical learning in online word recognition, testing generalization of learning across phonetic categories. While engaged in a word recognition task guided by perceptually unambiguous voice-onset time (VOT) acoustics signaling stop voicing in either bilabial rhymes, beer and pier, or alveolar rhymes, deer and tear, listeners were exposed incidentally to an artificial “accent” deviating from English norms in its correlation of the pitch onset of the following vowel (F0) with VOT (Experiment 1). Exposure to the change in the correlation of F0 with VOT led listeners to down-weight reliance on F0 in voicing categorization, indicating dimension-based statistical learning. This learning was observed only for the “accented” contrast varying in its F0/VOT relationship during exposure; learning did not generalize to the other place of articulation. Another group of listeners experienced competing F0/VOT correlations across place of articulation such that the global correlation for voicing was stable, but locally correlations across voicing pairs were opposing (e.g., “accented” beer and pier, “canonical” deer and tear, Experiment 2). Listeners showed dimension-based learning only for the accented pair, not the canonical pair, indicating that they are able to track separate acoustic statistics across place of articulation, that is, for /b-p/ and /d-t/. This suggests that dimension-based learning does not operate obligatorily at the phonological level of stop voicing. PMID:24364708
Hsu, Hsinjen J.; Tomblin, J. Bruce; Christiansen, Morten H.
2014-01-01
Being able to track dependencies between syntactic elements separated by other constituents is crucial for language acquisition and processing (e.g., in subject-noun/verb agreement). Although long assumed to require language-specific machinery, research on statistical learning has suggested that domain-general mechanisms may support the acquisition of non-adjacent dependencies. In this study, we investigated whether individuals with specific language impairment (SLI)—who have problems with long-distance dependencies in language—also have problems with statistical learning of non-adjacent relations. The results confirmed this hypothesis, indicating that statistical learning may subserve the acquisition and processing of long-distance dependencies in natural language. PMID:24639661
Hold My Calls: An Activity for Introducing the Statistical Process
ERIC Educational Resources Information Center
Abel, Todd; Poling, Lisa
2015-01-01
Working with practicing teachers, this article demonstrates, through the facilitation of a statistical activity, how to introduce and investigate the unique qualities of the statistical process including: formulate a question, collect data, analyze data, and interpret data.
Retirement as a Learning Process
ERIC Educational Resources Information Center
Hodkinson, Phil; Ford, Geoff; Hodkinson, Heather; Hawthorn, Ruth
2008-01-01
This article draws upon a major qualitative empirical research investigation in Great Britain to explore the relationships between retirement and learning. Though retirement is frequently viewed as an event leading to a life stage, our data show that it can perhaps be best understood as a lengthy process. This process begins well before actual…
Living and learning food processing
Technology Transfer Automated Retrieval System (TEKTRAN)
This year’s annual event promises to be both exciting and educational for those who wish to learn more about food processing. This column will provide a brief overview of the multitude of scientific sessions that reveal new research related to food processing. In addition to the symposia previewed h...
The Necessity of the Medial Temporal Lobe for Statistical Learning
Schapiro, Anna C.; Gregory, Emma; Landau, Barbara; McCloskey, Michael; Turk-Browne, Nicholas B.
2014-01-01
The sensory input that we experience is highly patterned, and we are experts at detecting these regularities. Although the extraction of such regularities, or statistical learning (SL), is typically viewed as a cortical process, recent studies have implicated the medial temporal lobe (MTL), including the hippocampus. These studies have employed fMRI, leaving open the possibility that the MTL is involved but not necessary for SL. Here, we examined this issue in a case study of LSJ, a patient with complete bilateral hippocampal loss and broader MTL damage. In Experiments 1 and 2, LSJ and matched control participants were passively exposed to a continuous sequence of shapes, syllables, scenes, or tones containing temporal regularities in the co-occurrence of items. In a subsequent test phase, the control groups exhibited reliable SL in all conditions, successfully discriminating regularities from recombinations of the same items into novel foil sequences. LSJ, however, exhibited no SL, failing to discriminate regularities from foils. Experiment 3 ruled out more general explanations for this failure, such as inattention during exposure or difficulty following test instructions, by showing that LSJ could discriminate which individual items had been exposed. These findings provide converging support for the importance of the MTL in extracting temporal regularities. PMID:24456393
Gómez, Rebecca L
2017-01-05
Statistical structure abounds in language. Human infants show a striking capacity for using statistical learning (SL) to extract regularities in their linguistic environments, a process thought to bootstrap their knowledge of language. Critically, studies of SL test infants in the minutes immediately following familiarization, but long-term retention unfolds over hours and days, with almost no work investigating retention of SL. This creates a critical gap in the literature given that we know little about how single or multiple SL experiences translate into permanent knowledge. Furthermore, different memory systems with vastly different encoding and retention profiles emerge at different points in development, with the underlying memory system dictating the fidelity of the memory trace hours later. I describe the scant literature on retention of SL, the learning and retention properties of memory systems as they apply to SL, and the development of these memory systems. I propose that different memory systems support retention of SL in infant and adult learners, suggesting an explanation for the slow pace of natural language acquisition in infancy. I discuss the implications of developing memory systems for SL and suggest that we exercise caution in extrapolating from adult to infant properties of SL.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.
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…
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…
New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
Zhao, Ying-Qi; Zeng, Donglin; Laber, Eric B.; Kosorok, Michael R.
2014-01-01
Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized treatment selection into an either sequential or simultaneous classification problem, and can thus be applied by modifying existing machine learning techniques. The proposed methods are based on directly maximizing over all DTRs a nonparametric estimator of the expected long-term outcome; this is fundamentally different than regression-based methods, for example Q-learning, which indirectly attempt such maximization and rely heavily on the correctness of postulated regression models. We prove that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules. Simulation results suggest the proposed methods produce superior DTRs compared with Q-learning especially in small samples. We illustrate the methods using data from a clinical trial for smoking cessation. PMID:26236062
Learning temporal statistics for sensory predictions in mild cognitive impairment.
Di Bernardi Luft, Caroline; Baker, Rosalind; Bentham, Peter; Kourtzi, Zoe
2015-08-01
Training is known to improve performance in a variety of perceptual and cognitive skills. However, there is accumulating evidence that mere exposure (i.e. without supervised training) to regularities (i.e. patterns that co-occur in the environment) facilitates our ability to learn contingencies that allow us to interpret the current scene and make predictions about future events. Recent neuroimaging studies have implicated fronto-striatal and medial temporal lobe brain regions in the learning of spatial and temporal statistics. Here, we ask whether patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) that are characterized by hippocampal dysfunction are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards orientated gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. However, our fMRI results demonstrate that MCI-AD patients recruit an alternate circuit to hippocampus to succeed in learning of predictive structures. In particular, we observed stronger learning-dependent activations for structured sequences in frontal, subcortical and cerebellar regions for patients compared to age-matched controls. Thus, our findings suggest a cortico-striatal-cerebellar network that may mediate the ability for predictive learning despite hippocampal dysfunction in MCI-AD.
Side effects of being blue: influence of sad mood on visual statistical learning.
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.
Structure Learning and Statistical Estimation in Distribution Networks - Part II
Deka, Deepjyoti; Backhaus, Scott N.; Chertkov, Michael
2015-02-13
Limited placement of real-time monitoring devices in the distribution grid, recent trends notwithstanding, has prevented the easy implementation of demand-response and other smart grid applications. Part I of this paper discusses the problem of learning the operational structure of the grid from nodal voltage measurements. In this work (Part II), the learning of the operational radial structure is coupled with the problem of estimating nodal consumption statistics and inferring the line parameters in the grid. Based on a Linear-Coupled(LC) approximation of AC power flows equations, polynomial time algorithms are designed to identify the structure and estimate nodal load characteristics and/or line parameters in the grid using the available nodal voltage measurements. Then the structure learning algorithm is extended to cases with missing data, where available observations are limited to a fraction of the grid nodes. The efficacy of the presented algorithms are demonstrated through simulations on several distribution test cases.
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…
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…
Physics-based statistical learning approach to mesoscopic model selection
NASA Astrophysics Data System (ADS)
Taverniers, Søren; Haut, Terry S.; Barros, Kipton; Alexander, Francis J.; Lookman, Turab
2015-11-01
In materials science and many other research areas, models are frequently inferred without considering their generalization to unseen data. We apply statistical learning using cross-validation to obtain an optimally predictive coarse-grained description of a two-dimensional kinetic nearest-neighbor Ising model with Glauber dynamics (GD) based on the stochastic Ginzburg-Landau equation (sGLE). The latter is learned from GD "training" data using a log-likelihood analysis, and its predictive ability for various complexities of the model is tested on GD "test" data independent of the data used to train the model on. Using two different error metrics, we perform a detailed analysis of the error between magnetization time trajectories simulated using the learned sGLE coarse-grained description and those obtained using the GD model. We show that both for equilibrium and out-of-equilibrium GD training trajectories, the standard phenomenological description using a quartic free energy does not always yield the most predictive coarse-grained model. Moreover, increasing the amount of training data can shift the optimal model complexity to higher values. Our results are promising in that they pave the way for the use of statistical learning as a general tool for materials modeling and discovery.
Application of Statistical Learning Theory to Plankton Image Analysis
2006-06-01
X. Wang. Learning to decode cognitive states from brain. Machine Learning, 57:145-175, 2004. [118] A. Mohan, C. Papageorgiou, and T. Poggio. Example...an automated image analyzing system. Journal of Plankton Research, 6:637- 645, 1984. [136] R. Rosenblatt. Principles of Neurodynamics . Spartan Books...New York, 1962. [137] D. E. Rumelhart and J. L. McClelland. Parallel Distributed Processing: Ex- ploration in the Microstructure of Cognition . MIT
Cooperative Learning: Refining the Process.
ERIC Educational Resources Information Center
Schultz, James L.
1990-01-01
Teachers must give adequate attention to teaching social skills and monitoring for total team involvement if they are to introduce cooperative learning successfully. Interpersonal skills are more important than positive interdependence, face-to-face interaction, individual accountability, or group processing skills. Includes five references. (MLH)
Statistical mechanics and visual signal processing
NASA Astrophysics Data System (ADS)
Potters, Marc; Bialek, William
1994-11-01
We show how to use the language of statistical field theory to address and solve problems in which one must estimate some aspect of the environnent from the data in an array of sensors. In the field theory formulation the optimal estimator can be written as an expectation value in an ensemble where the input data act as external field. Problems at low signal-to-noise ratio can be solved in perturbation theory, while high signal-to-noise ratios are treated with a saddle-point approximation. These ideas are illustrated in detail by an example of visual motion estimation which is chosen to model a problem solved by the fly's brain. The optimal estimator bas a rich structure, adapting to various parameters of the environnent such as the mean-square contrast and the corrélation time of contrast fluctuations. This structure is in qualitative accord with existing measurements on motion sensitive neurons in the fly's brain, and the adaptive properties of the optimal estimator may help resolve conficts among different interpretations of these data. Finally we propose some crucial direct tests of the adaptive behavior. Nous montrons comment employer le langage de la théorie statistique des champs pour poser et résoudre des problèmes où l'on doit estimer une caractéristique de l'environnement à l'aide de données provenant d'un ensemble de détecteurs. Dans ce formalisme, l'estimateur optimal peut être écrit comme la valeur moyenne d'un opérateur, l'ensemble des données d'entrée agissant comme un champ externe. Les problèmes à faible rapport signal-bruit sont résolus par la théorie des perturbations. La méthode du col est employée pour ceux à haut rapport signal-bruit. Ces idées sont illustrées en détails sur un modèle d'estimation visuelle du mouvement basé sur un problème résolu par la mouche. L'estimateur optimal a une structure très riche, s'adaptant à divers paramètres de l'environnement tels la variance du contraste et le temps de corr
Phonetic diversity, statistical learning, and acquisition of phonology.
Pierrehumbert, Janet B
2003-01-01
In learning to perceive and produce speech, children master complex language-specific patterns. Daunting language-specific variation is found both in the segmental domain and in the domain of prosody and intonation. This article reviews the challenges posed by results in phonetic typology and sociolinguistics for the theory of language acquisition. It argues that categories are initiated bottom-up from statistical modes in use of the phonetic space, and sketches how exemplar theory can be used to model the updating of categories once they are initiated. It also argues that bottom-up initiation of categories is successful thanks to the perception-production loop operating in the speech community. The behavior of this loop means that the superficial statistical properties of speech available to the infant indirectly reflect the contrastiveness and discriminability of categories in the adult grammar. The article also argues that the developing system is refined using internal feedback from type statistics over the lexicon, once the lexicon is well-developed. The application of type statistics to a system initiated with surface statistics does not cause a fundamental reorganization of the system. Instead, it exploits confluences across levels of representation which characterize human language and make bootstrapping possible.
Statistical and optimal learning with applications in business analytics
NASA Astrophysics Data System (ADS)
Han, Bin
Statistical learning is widely used in business analytics to discover structure or exploit patterns from historical data, and build models that capture relationships between an outcome of interest and a set of variables. Optimal learning on the other hand, solves the operational side of the problem, by iterating between decision making and data acquisition/learning. All too often the two problems go hand-in-hand, which exhibit a feedback loop between statistics and optimization. We apply this statistical/optimal learning concept on a context of fundraising marketing campaign problem arising in many non-profit organizations. Many such organizations use direct-mail marketing to cultivate one-time donors and convert them into recurring contributors. Cultivated donors generate much more revenue than new donors, but also lapse with time, making it important to steadily draw in new cultivations. The direct-mail budget is limited, but better-designed mailings can improve success rates without increasing costs. We first apply statistical learning to analyze the effectiveness of several design approaches used in practice, based on a massive dataset covering 8.6 million direct-mail communications with donors to the American Red Cross during 2009-2011. We find evidence that mailed appeals are more effective when they emphasize disaster preparedness and training efforts over post-disaster cleanup. Including small cards that affirm donors' identity as Red Cross supporters is an effective strategy, while including gift items such as address labels is not. Finally, very recent acquisitions are more likely to respond to appeals that ask them to contribute an amount similar to their most recent donation, but this approach has an adverse effect on donors with a longer history. We show via simulation that a simple design strategy based on these insights has potential to improve success rates from 5.4% to 8.1%. Given these findings, when new scenario arises, however, new data need to
Chen, Chi-Hsin; Yu, Chen
2016-09-12
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.
The acquisition of allophonic rules: statistical learning with linguistic constraints.
Peperkamp, Sharon; Le Calvez, Rozenn; Nadal, Jean-Pierre; Dupoux, Emmanuel
2006-10-01
Phonological rules relate surface phonetic word forms to abstract underlying forms that are stored in the lexicon. Infants must thus acquire these rules in order to infer the abstract representation of words. We implement a statistical learning algorithm for the acquisition of one type of rule, namely allophony, which introduces context-sensitive phonetic variants of phonemes. This algorithm is based on the observation that different realizations of a single phoneme typically do not appear in the same contexts (ideally, they have complementary distributions). In particular, it measures the discrepancies in context probabilities for each pair of phonetic segments. In Experiment 1, we test the algorithm's performances on a pseudo-language and show that it is robust to statistical noise due to sampling and coding errors, and to non-systematic rule application. In Experiment 2, we show that a natural corpus of semiphonetically transcribed child-directed speech in French presents a very large number of near-complementary distributions that do not correspond to existing allophonic rules. These spurious allophonic rules can be eliminated by a linguistically motivated filtering mechanism based on a phonetic representation of segments. We discuss the role of a priori linguistic knowledge in the statistical learning of phonology.
Dynamic cortical involvement in implicit anticipation during statistical learning.
Altamura, Mario; Carver, Frederick W; Elvevåg, Brita; Weinberger, Daniel R; Coppola, Richard
2014-01-13
The prediction of future events is fundamental in a large number of critical neurobehavioral contexts including implicit motor learning. This learning process relies on the probabilities with which events occur, and is a dynamic phenomenon. The aim of present study was to investigate the development of anticipatory processes during implicit learning. A decision making task was employed in which the frequency of trial types was manipulated such that one trial type was disproportionately prevalent as compared to the remaining three trial types. A 275 channel whole-head magnetoencephalography (MEG) system was used to investigate the spatiotemporal distribution of event-related desynchronization (ERD) and synchronization (ERS). The results revealed that oscillations within the alpha (10-12 Hz) and beta (14-30 Hz) frequencies were associated with anticipatory processes in distinct networks in the course of learning. During early phases of learning the contralateral motor cortex, the anterior cingulate, the caudate and the inferior frontal gyrus showed ERDs within beta and alpha frequencies, putatively reflecting preparation of next motor response. As the task progressed, alpha ERSs in occipitotemporal regions and putamen likely reflect perceptual anticipation of the forthcoming stimuli.
Arciuli, Joanne; Simpson, Ian C
2011-05-01
It is possible that statistical learning (SL) plays a role in almost every mental activity. Indeed, research on SL has grown rapidly over recent decades in an effort to better understand perception and cognition. Yet, there remain gaps in our understanding of how SL operates, in particular with regard to its (im)mutability. Here, we investigated whether participant-related variables (such as age) and task-related variables (such as speed of stimulus presentation) affect visual statistical learning (VSL) in typically developing children. We tested 183 participants ranging in age from 5 to 12 years and compared three speeds of presentation (using stimulus durations of 800, 400 and 200 msecs). A multiple regression analysis revealed significant effects of both age and speed of presentation - after attention during familiarization and gender had been taken into consideration. VSL followed a developmental trajectory whereby learning increased with age. The amount of learning increased with longer presentation times (as shown by Turk-Browne, Jungé & Scholl, 2005, in their study of adults). There was no significant interaction between the two variables. These findings assist in elucidating the nature of statistical learning itself. While statistical learning can be observed in very young children and at remarkably fast presentation times, participant- and task-related variables do impact upon this type of learning. The findings reported here may serve to enhance our understanding of individual differences in the cognitive and perceptual processes that are thought to rely, at least in part, on SL (e.g. language processing and object recognition).
Effects of Reflection Prompts on Learning Outcomes and Learning Behaviour in Statistics Education
ERIC Educational Resources Information Center
Stark, Robin; Krause, Ulrike-Marie
2009-01-01
Starting from difficulties that students display when they deal with correlation analysis, an e-learning environment ("Koralle") was developed. The design was inspired by principles of situated and example-based learning. In order to facilitate reflective processes and thus enhance learning outcomes, reflection prompts were integrated into the…
Process Model Construction and Optimization Using Statistical Experimental Design,
1988-04-01
Memo No. 88-442 ~LECTE March 1988 31988 %,.. MvAY 1 98 0) PROCESS MODEL CONSTRUCTION AND OPTIMIZATION USING STATISTICAL EXPERIMENTAL DESIGN Emmanuel...Sachs and George Prueger Abstract A methodology is presented for the construction of process models by the combination of physically based mechanistic...253-8138. .% I " Process Model Construction and Optimization Using Statistical Experimental Design" by Emanuel Sachs Assistant Professor and George
A Role for Chunk Formation in Statistical Learning of Second Language Syntax
ERIC Educational Resources Information Center
Hamrick, Phillip
2014-01-01
Humans are remarkably sensitive to the statistical structure of language. However, different mechanisms have been proposed to account for such statistical sensitivities. The present study compared adult learning of syntax and the ability of two models of statistical learning to simulate human performance: Simple Recurrent Networks, which learn by…
Extended Statistical Learning as an account for slow vocabulary growth.
Stokes, Stephanie F; Kern, Sophie; Dos Santos, Christophe
2012-01-01
Stokes (2010) compared the lexicons of English-speaking late talkers (LT) with those of their typically developing (TD) peers on neighborhood density (ND) and word frequency (WF) characteristics and suggested that LTs employed learning strategies that differed from those of their TD peers. This research sought to explore the cross-linguistic validity of this conclusion. The lexicons (production, not recognition) of 208 French-speaking two-year-old children were coded for ND and WF. Regression revealed that ND and WF together predicted 62% of the variance in vocabulary size, with ND and WF uniquely accounting for 53% and 9% of that variance respectively. Epiphenomenal findings were ruled out by comparison of simulated data sets with the actual data. A generalized Mann-Whitney test showed that children with small vocabularies had significantly higher ND values and significantly lower WF values than children with large vocabularies. An EXTENDED STATISTICAL LEARNING theory is proposed to account for the findings.
Unsupervised statistical learning applied to experimental high-energy physics and related areas
NASA Astrophysics Data System (ADS)
Simas Filho, Eduardo F.; Seixas, José M.
2016-12-01
Unsupervised statistical learning (USL) techniques, such as self-organizing maps (SOMs), principal component analysis (PCA) and independent component analysis explore different statistical properties to efficiently process information from multiple variables. USL algorithms have been successfully applied in experimental high-energy physics (HEP) and related areas for different purposes, such as feature extraction, signal detection, noise reduction, signal-background separation and removal of cross-interference from multiple signal sources in multisensor measurement systems. This paper presents both a review of the theoretical aspects of these signal processing methods and examples of some successful applications in HEP and related areas experiments.
Implicit and explicit statistical learning of tone sequences across spectral shifts.
Daikoku, Tatsuya; Yatomi, Yutaka; Yumoto, Masato
2014-10-01
We investigated how the statistical learning of auditory sequences is reflected in neuromagnetic responses in implicit and explicit learning conditions. Complex tones with fundamental frequencies (F0s) in a five-tone equal temperament were generated by a formant synthesizer. The tones were subsequently ordered with the constraint that the probability of the forthcoming tone was statistically defined (80% for one tone; 5% for the other four) by the latest two successive tones (second-order Markov chains). The tone sequence consisted of 500 tones and 250 successive tones with a relative shift of F0s based on the same Markov transitional matrix. In explicit and implicit learning conditions, neuromagnetic responses to the tone sequence were recorded from fourteen right-handed participants. The temporal profiles of the N1m responses to the tones with higher and lower transitional probabilities were compared. In the explicit learning condition, the N1m responses to tones with higher transitional probability were significantly decreased compared with responses to tones with lower transitional probability in the latter half of the 500-tone sequence. Furthermore, this difference was retained even after the F0s were relatively shifted. In the implicit learning condition, N1m responses to tones with higher transitional probability were significantly decreased only for the 250 tones following the relative shift of F0s. The delayed detection of learning effects across the sound-spectral shift in the implicit condition may imply that learning may progress earlier in explicit learning conditions than in implicit learning conditions. The finding that the learning effects were retained across spectral shifts regardless of the learning modality indicates that relative pitch processing may be an essential ability for humans.
Musicians’ Online Performance during Auditory and Visual Statistical Learning Tasks
Mandikal Vasuki, Pragati R.; Sharma, Mridula; Ibrahim, Ronny K.; Arciuli, Joanne
2017-01-01
Musicians’ brains are considered to be a functional model of neuroplasticity due to the structural and functional changes associated with long-term musical training. In this study, we examined implicit extraction of statistical regularities from a continuous stream of stimuli—statistical learning (SL). We investigated whether long-term musical training is associated with better extraction of statistical cues in an auditory SL (aSL) task and a visual SL (vSL) task—both using the embedded triplet paradigm. Online measures, characterized by event related potentials (ERPs), were recorded during a familiarization phase while participants were exposed to a continuous stream of individually presented pure tones in the aSL task or individually presented cartoon figures in the vSL task. Unbeknown to participants, the stream was composed of triplets. Musicians showed advantages when compared to non-musicians in the online measure (early N1 and N400 triplet onset effects) during the aSL task. However, there were no differences between musicians and non-musicians for the vSL task. Results from the current study show that musical training is associated with enhancements in extraction of statistical cues only in the auditory domain. PMID:28352223
Bogaerts, Louisa; Siegelman, Noam; Frost, Ram
2016-08-01
What determines individuals' efficacy in detecting regularities in visual statistical learning? Our theoretical starting point assumes that the variance in performance of statistical learning (SL) can be split into the variance related to efficiency in encoding representations within a modality and the variance related to the relative computational efficiency of detecting the distributional properties of the encoded representations. Using a novel methodology, we dissociated encoding from higher-order learning factors, by independently manipulating exposure duration and transitional probabilities in a stream of visual shapes. Our results show that the encoding of shapes and the retrieving of their transitional probabilities are not independent and additive processes, but interact to jointly determine SL performance. The theoretical implications of these findings for a mechanistic explanation of SL are discussed.
Developing Students' Thought Processes for Choosing Appropriate Statistical Methods
ERIC Educational Resources Information Center
Murray, James; Knowles, Elizabeth
2014-01-01
Students often struggle to select appropriate statistical tests when investigating research questions. The authors present a lesson study designed to make students' thought processes visible while considering this choice. The authors taught their students a way to organize knowledge about statistical tests and observed its impact in the classroom…
An Automated Statistical Process Control Study of Inline Mixing Using Spectrophotometric Detection
ERIC Educational Resources Information Center
Dickey, Michael D.; Stewart, Michael D.; Willson, C. Grant
2006-01-01
An experiment is described, which is designed for a junior-level chemical engineering "fundamentals of measurements and data analysis" course, where students are introduced to the concept of statistical process control (SPC) through a simple inline mixing experiment. The students learn how to create and analyze control charts in an effort to…
Statistical Argumentation in Project-Based Learning Environments
ERIC Educational Resources Information Center
Hudson, Rick Alan
2010-01-01
Recent educational reform initiatives call for additional attention to be placed on data analysis and statistics in the K-12 school curriculum and an emphasis on mathematical processes such as reasoning and communication in mathematics classrooms. This study examines how small groups of middle grade students engaged during a project-based learning…
Another look at statistical learning theory and regularization.
Cherkassky, Vladimir; Ma, Yunqian
2009-09-01
The paper reviews and highlights distinctions between function-approximation (FA) and VC theory and methodology, mainly within the setting of regression problems and a squared-error loss function, and illustrates empirically the differences between the two when data is sparse and/or input distribution is non-uniform. In FA theory, the goal is to estimate an unknown true dependency (or 'target' function) in regression problems, or posterior probability P(y/x) in classification problems. In VC theory, the goal is to 'imitate' unknown target function, in the sense of minimization of prediction risk or good 'generalization'. That is, the result of VC learning depends on (unknown) input distribution, while that of FA does not. This distinction is important because regularization theory originally introduced under clearly stated FA setting [Tikhonov, N. (1963). On solving ill-posed problem and method of regularization. Doklady Akademii Nauk USSR, 153, 501-504; Tikhonov, N., & V. Y. Arsenin (1977). Solution of ill-posed problems. Washington, DC: W. H. Winston], has been later used under risk-minimization or VC setting. More recently, several authors [Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Advances in Computational Mathematics, 13, 1-50; Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference and prediction. Springer; Poggio, T. and Smale, S., (2003). The mathematics of learning: Dealing with data. Notices of the AMS, 50 (5), 537-544] applied constructive methodology based on regularization framework to learning dependencies from data (under VC-theoretical setting). However, such regularization-based learning is usually presented as a purely constructive methodology (with no clearly stated problem setting). This paper compares FA/regularization and VC/risk minimization methodologies in terms of underlying theoretical assumptions. The control of model
Learning Process Questionnaire Manual. Student Approaches to Learning and Studying.
ERIC Educational Resources Information Center
Biggs, John B.
This manual describes the theory behind the Learning Process Questionnaire (LPQ) used in Australia and defines what the subscale and scale scores mean. The LPQ is a 36-item self-report questionnaire that yields scores on three basic motives for learning and three learning strategies, and on the approaches to learning that are formed by these…
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.
Machine learning, statistical learning and the future of biological research in psychiatry.
Iniesta, R; Stahl, D; McGuffin, P
2016-09-01
Psychiatric research has entered the age of 'Big Data'. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other 'omic' measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may be further complicated by missing data for some subjects and variables that are highly correlated. Statistical learning-based models are a natural extension of classical statistical approaches but provide more effective methods to analyse very large datasets. In addition, the predictive capability of such models promises to be useful in developing decision support systems. That is, methods that can be introduced to clinical settings and guide, for example, diagnosis classification or personalized treatment. In this review, we aim to outline the potential benefits of statistical learning methods in clinical research. We first introduce the concept of Big Data in different environments. We then describe how modern statistical learning models can be used in practice on Big Datasets to extract relevant information. Finally, we discuss the strengths of using statistical learning in psychiatric studies, from both research and practical clinical points of view.
Ready-to-Use Simulation: Demystifying Statistical Process Control
ERIC Educational Resources Information Center
Sumukadas, Narendar; Fairfield-Sonn, James W.; Morgan, Sandra
2005-01-01
Business students are typically introduced to the concept of process management in their introductory course on operations management. A very important learning outcome here is an appreciation that the management of processes is a key to the management of quality. Some of the related concepts are qualitative, such as strategic and behavioral…
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.
Fault Tolerant Statistical Signal Processing Algorithms for Parallel Architectures.
2014-09-26
AD-fi57 393 FAULT TOLERANT STATISTICAL SIGNAL PROCESSING ALGORITHMS i/i FOR PARALLEL ARCH U) JOHNS HOPKINS UNIV BALTIMORE MD DEPT OF ELECTRICAL...COVERED * ’ Fault Tolerant Statistical Signal Processing Technical A l g o r i t h m s f o r P a r a l l e l A r c h i t e c t u r e s a ._ P E R F O R M I...Identify by block number) , Fault Tolerance, Signal Processing, Parallel Architecture 0 20. ABSTRACT (Continue on reveree side It neceseary and identify by
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…
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…
Manufacturing Squares: An Integrative Statistical Process Control Exercise
ERIC Educational Resources Information Center
Coy, Steven P.
2016-01-01
In the exercise, students in a junior-level operations management class are asked to manufacture a simple product. Given product specifications, they must design a production process, create roles and design jobs for each team member, and develop a statistical process control plan that efficiently and effectively controls quality during…
Using Paper Helicopters to Teach Statistical Process Control
ERIC Educational Resources Information Center
Johnson, Danny J.
2011-01-01
This hands-on project uses a paper helicopter to teach students how to distinguish between common and special causes of variability when developing and using statistical process control charts. It allows the student to experience a process that is out-of-control due to imprecise or incomplete product design specifications and to discover how the…
Observing fermionic statistics with photons in arbitrary processes
Matthews, Jonathan C. F.; Poulios, Konstantinos; Meinecke, Jasmin D. A.; Politi, Alberto; Peruzzo, Alberto; Ismail, Nur; Wörhoff, Kerstin; Thompson, Mark G.; O'Brien, Jeremy L.
2013-01-01
Quantum mechanics defines two classes of particles-bosons and fermions-whose exchange statistics fundamentally dictate quantum dynamics. Here we develop a scheme that uses entanglement to directly observe the correlated detection statistics of any number of fermions in any physical process. This approach relies on sending each of the entangled particles through identical copies of the process and by controlling a single phase parameter in the entangled state, the correlated detection statistics can be continuously tuned between bosonic and fermionic statistics. We implement this scheme via two entangled photons shared across the polarisation modes of a single photonic chip to directly mimic the fermion, boson and intermediate behaviour of two-particles undergoing a continuous time quantum walk. The ability to simulate fermions with photons is likely to have applications for verifying boson scattering and for observing particle correlations in analogue simulation using any physical platform that can prepare the entangled state prescribed here. PMID:23531788
Asmar, M K; Yeretzian, J S; Rady, A
2016-04-19
In view of the rapid health transition faced by the country and a highly dominant private sector, the issue of obtaining reliable health statistics is becoming a priority for Lebanon. This paper reviews the process of compiling and disseminating national health statistics from the multitude of public, private and nongovernmental partners in the country. The lessons learned from preparing two editions of the National health statistics report in Lebanon allow identification of some challenges and strengths of the current health information system in Lebanon. The experience emphasizes the need for a close partnership with all stakeholders, an efficient management system, adequate human resources and predefined systems and procedures. The process would benefit from having an interactive website for exchange of data and information among stakeholders and the public. The existence of clear guidelines with consistent definitions and standardized forms would also facilitate the collection and analysis of data.
Structure Learning and Statistical Estimation in Distribution Networks - Part I
Deka, Deepjyoti; Backhaus, Scott N.; Chertkov, Michael
2015-02-13
Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as those related to demand response, outage detection and management, and improved load-monitoring. In this two part paper, inspired by proliferation of the metering technology, we discuss estimation problems in structurally loopy but operationally radial distribution grids from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. In Part I, the objective is to learn the operational layout of the grid. Part II of this paper presents algorithms that estimate load statistics or line parameters in addition to learning the grid structure. Further, Part II discusses the problem of structure estimation for systems with incomplete measurement sets. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time– which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.
Learning Processes in Man, Machine and Society
ERIC Educational Resources Information Center
Malita, Mircea
1977-01-01
Deciphering the learning mechanism which exists in man remains to be solved. This article examines the learning process with respect to association and cybernetics. It is recommended that research should focus on the transdisciplinary processes of learning which could become the next key concept in the science of man. (Author/MA)
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…
Implicit Statistical Learning Is Directly Associated with the Acquisition of Syntax
ERIC Educational Resources Information Center
Kidd, Evan
2012-01-01
This article reports on an individual differences study that investigated the role of implicit statistical learning in the acquisition of syntax in children. One hundred children ages 4 years 5 months through 6 years 11 months completed a test of implicit statistical learning, a test of explicit declarative learning, and standardized tests of…
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…
Saito, Hiroshi; Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato
2011-05-01
Neural networks can learn flexible input-output associations by changing their synaptic weights. The representational performance and learning dynamics of neural networks are intensively studied in several fields. Neural networks face the "credit assignment problem" in situations in which only incomplete performance evaluations are available. The credit assignment problem is that a network should assign credit or blame for its behaviors according to the contribution to the network performance. In reinforcement learning, a scalar evaluation signal is delivered to a network. The two main types of credit assignment problems in reinforcement learning are structural and temporal, that is, which parameters of the network (structural) and which past network activities (temporal) are related to an evaluation signal given from an environment. In this study, we apply statistical mechanical analysis to the learning processes in a simple neural network model to clarify the effects of two kinds of credit assignments and their interactions. Our model is based on node perturbation learning with eligibility trace. Node perturbation is a stochastic gradient learning method that can solve structural credit assignment problems by introducing a perturbation into the system output. The eligibility trace preserves the past network activities with a temporal credit to deal with the delay of an instruction signal. We show that both credit assignment effects mutually interact and the optimal time constant of the eligibility trace varies not only for the evaluation delay but also the network size.
Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels
Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J.
2014-01-01
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378
Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J
2014-01-01
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.
New trends in natural language processing: statistical natural language processing.
Marcus, M
1995-01-01
The field of natural language processing (NLP) has seen a dramatic shift in both research direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. Recently, more and more work is shifting toward hybrid methods that combine new empirical corpus-based methods, including the use of probabilistic and information-theoretic techniques, with traditional symbolic methods. This work is made possible by the recent availability of linguistic databases that add rich linguistic annotation to corpora of natural language text. Already, these methods have led to a dramatic improvement in the performance of a variety of NLP systems with similar improvement likely in the coming years. This paper focuses on these trends, surveying in particular three areas of recent progress: part-of-speech tagging, stochastic parsing, and lexical semantics. PMID:7479725
Leaders for Learning: A Collaborative Learning Process
ERIC Educational Resources Information Center
Dalton, Margaret R.
2004-01-01
The Missouri Professors of Educational Administration (MPEA) initiated the Leaders for Learning project to create technology based instructional materials aligned with the standards of the Interstate School Leadership Licensure Consortium (ISLLC). With funding from the Missouri Department of Elementary and Secondary Education (DESE), faculty from…
Comparing associative, statistical, and inferential reasoning accounts of human contingency learning
Pineño, Oskar; Miller, Ralph R.
2007-01-01
For more than two decades, researchers have contrasted the relative merits of associative and statistical theories as accounts of human contingency learning. This debate, still far from resolution, has led to further refinement of models within each family of theories. More recently, a third theoretical view has joined the debate: the inferential reasoning account. The explanations of these three accounts differ critically in many aspects, such as level of analysis and their emphasis on different steps within the information-processing sequence. Also, each account has important advantages (as well as critical flaws) and emphasizes experimental evidence that poses problems to the others. Some hybrid models of human contingency learning have attempted to reconcile certain features of these accounts, thereby benefiting from some of the unique advantages of different families of accounts. A comparison of these families of accounts will help us appreciate the challenges that research on human contingency learning will face over the coming years. PMID:17366303
Statistical phonetic learning in infants: facilitation and feature generalization.
Maye, Jessica; Weiss, Daniel J; Aslin, Richard N
2008-01-01
Over the course of the first year of life, infants develop from being generalized listeners, capable of discriminating both native and non-native speech contrasts, into specialized listeners whose discrimination patterns closely reflect the phonetic system of the native language(s). Recent work by Maye, Werker and Gerken (2002) has proposed a statistical account for this phenomenon, showing that infants may lose the ability to discriminate some foreign language contrasts on the basis of their sensitivity to the statistical distribution of sounds in the input language. In this paper we examine the process of enhancement in infant speech perception, whereby initially difficult phonetic contrasts become better discriminated when they define two categories that serve a functional role in the native language. In particular, we demonstrate that exposure to a bimodal statistical distribution in 8-month-old infants' phonetic input can lead to increased discrimination of difficult contrasts. In addition, this exposure also facilitates discrimination of an unfamiliar contrast sharing the same phonetic feature as the contrast presented during familiarization, suggesting that infants extract acoustic/phonetic information that is invariant across an abstract featural representation.
Callahan, Charles D; Griffen, David L
2003-08-01
Emergency medicine faces unique challenges in the effort to improve efficiency and effectiveness. Increased patient volumes, decreased emergency department (ED) supply, and an increased emphasis on the ED as a diagnostic center have contributed to poor customer satisfaction and process failures such as diversion/bypass. Statistical process control (SPC) techniques developed in industry offer an empirically based means to understand our work processes and manage by fact. Emphasizing that meaningful quality improvement can occur only when it is exercised by "front-line" providers, this primer presents robust yet accessible SPC concepts and techniques for use in today's ED.
Statistical Process Control Charts for Public Health Monitoring
2014-12-01
process performance, remove existing sources of natural and unnatural variability, and identify any new sources of variability [1]. Control charts are SPC...can be used and refined over time [4]. The causes of any Phase I points outside the established control limits should be investigated. If the cause is...U.S. Army Public Health Command Statistical Process Control Charts for Public Health Monitoring PHR No. S.0023112 General Medical: 500A, Public
Statistical learning of music- and language-like sequences and tolerance for spectral shifts.
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.
Statistical Model for Predicting Roles and Effects in Learning Community
ERIC Educational Resources Information Center
Chang, Chih-Kai; Chen, Gwo-Dong; Wang, Chin-Yeh
2011-01-01
Functional roles may explain the learning performance of groups. Detecting a functional role is critical for promoting group learning performance in computer-supported collaborative learning environments. However, it is not easy for teachers to identify the functional roles played by students in a web-based learning group, or the relationship…
Semantic Learning Modifies Perceptual Face Processing
ERIC Educational Resources Information Center
Heisz, Jennifer J.; Shedden, Judith M.
2009-01-01
Face processing changes when a face is learned with personally relevant information. In a five-day learning paradigm, faces were presented with rich semantic stories that conveyed personal information about the faces. Event-related potentials were recorded before and after learning during a passive viewing task. When faces were novel, we observed…
Words in a sea of sounds: the output of infant statistical learning.
Saffran, J R
2001-09-01
One of the first problems confronting infant language learners is word segmentation: discovering the boundaries between words. Prior research suggests that 8-month-old infants can detect the statistical patterns that serve as a cue to word boundaries. However, the representational structure of the output of this learning process is unknown. This research assessed the extent to which statistical learning generates novel word-like units, rather than probabilistically-related strings of sounds. Eight-month-old infants were familiarized with a continuous stream of nonsense words with no acoustic cues to word boundaries. A post-familiarization test compared the infants' responses to words versus part-words (sequences spanning a word boundary) embedded either in simple English contexts familiar to the infants (e.g. "I like my tibudo"), or in matched nonsense frames (e.g. "zy fike ny tibudo"). Listening preferences were affected by the context (English versus nonsense) in which the items from the familiarization phase were embedded during testing. A second experiment confirmed that infants can discriminate the simple English contexts and the matched nonsense frames used in Experiment 1. The third experiment replicated the results of Experiment 1 by contrasting the English test frames with non-linguistic frames generated from tone sequences. The results support the hypothesis that statistical learning mechanisms generate word-like units with some status relative to the native language.
Dictionary learning based statistical interior reconstruction without a prior knowledge
NASA Astrophysics Data System (ADS)
Shi, Yongyi; Mou, Xuanqin
2016-10-01
Despite the significantly practical utilities of interior tomography, it still suffers from severe degradation of direct current (DC) shift artifact. Existing literature suggest to introducing prior information of object support (OS) constraint or the zeroth order image moment, i.e., the DC value into interior reconstruction to suppress the shift artifact, while the prior information is not always available in practice. Aimed at alleviating the artifacts without prior knowledge, in this paper, we reported an approach on the estimation of the object support which could be employed to estimate the zeroth order image moment, and hence facilitate the DC shift artifacts removal in interior reconstruction. Firstly, by assuming most of the reconstructed object consists of soft tissues that are equivalent to water, we reconstructed a virtual OS that is symmetrical about the interior region of interest (ROI) for the DC estimation. Hence the DC value can be estimated from the virtual reconstruction. Secondly, a statistical iterative reconstruction incorporated with the sparse representation in terms of learned dictionary and the constraint in terms of image DC value was adopted to solve the interior tomography. Experimental results demonstrate that the relative errors of the estimated zeroth order image moment are 4.7% and 7.6%, corresponding to the simulated data of a human thorax and the real data of a sheep lung, respectively. Reconstructed images with the constraint of the estimated DC value exhibit greatly superior image quality to that without DC value constraint.
The Structural Correlates of Statistical Information Processing during Speech Perception
Deschamps, Isabelle; Hasson, Uri; Tremblay, Pascale
2016-01-01
The processing of continuous and complex auditory signals such as speech relies on the ability to use statistical cues (e.g. transitional probabilities). In this study, participants heard short auditory sequences composed either of Italian syllables or bird songs and completed a regularity-rating task. Behaviorally, participants were better at differentiating between levels of regularity in the syllable sequences than in the bird song sequences. Inter-individual differences in sensitivity to regularity for speech stimuli were correlated with variations in surface-based cortical thickness (CT). These correlations were found in several cortical areas including regions previously associated with statistical structure processing (e.g. bilateral superior temporal sulcus, left precentral sulcus and inferior frontal gyrus), as well other regions (e.g. left insula, bilateral superior frontal gyrus/sulcus and supramarginal gyrus). In all regions, this correlation was positive suggesting that thicker cortex is related to higher sensitivity to variations in the statistical structure of auditory sequences. Overall, these results suggest that inter-individual differences in CT within a distributed network of cortical regions involved in statistical structure processing, attention and memory is predictive of the ability to detect structural structure in auditory speech sequences. PMID:26919234
Orienting teaching toward the learning process.
ten Cate, Olle; Snell, Linda; Mann, Karen; Vermunt, Jan
2004-03-01
Based on developments in educational psychology from the late 1980s, the authors present a model of an approach to teaching. Students' learning processes were analyzed to determine teacher functions. The learning-oriented teaching (LOT) model aims at following and guiding the learning process. The main characteristics of the model are (1) the components of learning: cognition (what to learn), affect (why learn), and metacognition (how to learn); and (2) the amount of guidance students need. If education aims at fostering one's ability to function independently in society, an important general objective should be that one learns how to fully and independently regulate his or her own learning; i.e., the ability to pursue one's professional life independently. This implies a transition from external guidance (from the teacher) through shared guidance (by the student together with the teacher) to internal guidance (by the student alone). This transition pertains not only to the cognitive component of learning (content) but also to the affective component (motives) and the metacognitive component (learning strategies). This model reflects a philosophy of internalization of the teacher's functions in a way that allows optimal independent learning after graduation. The model can be shown as a two-dimensional chart of learning components versus levels of guidance. It is further elaborated from learners' and teachers' perspectives. Examples of curriculum structure and teachers' activities are given to illustrate the model. Implications for curriculum development, course development, individual teaching moments, and educational research are discussed.
Quantum statistics of optical parametric processes with squeezed reservoirs
NASA Astrophysics Data System (ADS)
Peřina, Jan; Křepelka, Jaromír
2013-11-01
Quantum statistics including joint photon-number and integrated-intensity probability distributions are derived in time evolution of general optical parametric process involving processes of frequency conversion, parametric amplification and subharmonic generation taking into account losses and noise described by squeezed reservoirs. Using these tools quantum entanglement of modes is considered and the other nonclassical properties of the process under discussion are demonstrated by means of conditional probability distributions and their Fano factors, difference-number probability distributions, quantum oscillations, squeezing of vacuum fluctuations and negative values of the joint and difference wave probability quasidistributions. Nonclassical properties are illustrated for spontaneous process as well as stimulated process by means of chaotic light and squeezed vacuum field. Multimode processes are investigated in the spirit of the Mandel-Rice photocount formula.
U-processes and preference learning.
Li, Hong; Ren, Chuanbao; Li, Luoqing
2014-12-01
Preference learning has caused great attention in machining learning. In this letter we propose a learning framework for pairwise loss based on empirical risk minimization of U-processes via Rademacher complexity. We first establish a uniform version of Bernstein inequality of U-processes of degree 2 via the entropy methods. Then we estimate the bound of the excess risk by using the Bernstein inequality and peeling skills. Finally, we apply the excess risk bound to the pairwise preference and derive the convergence rates of pairwise preference learning algorithms with squared loss and indicator loss by using the empirical risk minimization with respect to U-processes.
An intelligent system for multivariate statistical process monitoring and diagnosis.
Tatara, Eric; Cinar, Ali
2002-04-01
A knowledge-based system (KBS) was designed for automated system identification, process monitoring, and diagnosis of sensor faults. The real-time KBS consists of a supervisory system using G2 KBS development software linked with external statistical modules for system identification and sensor fault diagnosis. The various statistical techniques were prototyped in MATLAB, converted to ANSI C code, and linked with the G2 Standard Interface. The KBS automatically performs all operations of data collection, identification, monitoring, and sensor fault diagnosis with little or no input from the user. Navigation throughout the KBS is via menu buttons on each user-accessible screen. Selected process variables are displayed on charts showing the history of the variables over a period of time. Multivariate statistical tests and contribution plots are also shown graphically. The KBS was evaluated using simulation studies with a polymerization reactor through a nonlinear dynamic model. Both normal operation conditions as well as conditions of process disturbances were observed to evaluate the KBS performance. Specific user-defined disturbances were added to the simulation, and the KBS correctly diagnosed both process and sensor faults when present.
Phonetic Processing When Learning Words
ERIC Educational Resources Information Center
Havy, Mélanie; Bouchon, Camillia; Nazzi, Thierry
2016-01-01
Infants have remarkable abilities to learn several languages. However, phonological acquisition in bilingual infants appears to vary depending on the phonetic similarities or differences of their two native languages. Many studies suggest that learning contrasts with different realizations in the two languages (e.g., the /p/, /t/, /k/ stops have…
Media and the Learning Process.
ERIC Educational Resources Information Center
Gagne, Robert M.
Although the study of learning theory can produce principles applicable to the design of instruction, virtually no instructional materials in existence today have deliberately been prepared on the basis of such principles. Terms used in four different learning theories--motivation, (Neal Miller); stimulus control (Skinner); distinctive conditions…
Effectiveness of eLearning in Statistics: Pictures and Stories
ERIC Educational Resources Information Center
Blackburn, Greg
2015-01-01
The study investigates (1) the effectiveness of using eLearning-embedded stories and pictures in order to improve learning outcomes for students and (2) how universities can adopt innovative approaches to the creation of Problem-Based Learning (PBL) resources and embed them in educational technology for teaching domain-specific content, such as…
Team-Based Learning in a Statistical Literacy Class
ERIC Educational Resources Information Center
St. Clair, Katherine; Chihara, Laura
2012-01-01
Team-based learning (TBL) is a pedagogical strategy that uses groups of students working together in teams to learn course material. The main learning objective in TBL is to provide students the opportunity to "practice" course concepts during class-time. A key feature is multiple-choice quizzes that students take individually and then re-take as…
International Collaborative Learning--The Facilitation Process.
ERIC Educational Resources Information Center
Clear, A. G.
International collaborative learning is becoming more viable through a variety of Internet enabled software products. Group Support Systems appear to offer promise. But it is not well understood how to facilitate the teaching and learning process in electronic environments. If education is to involve an interactive process of collaborative inquiry…
Visual Statistical Learning Based on the Perceptual and Semantic Information of Objects
ERIC Educational Resources Information Center
Otsuka, Sachio; Nishiyama, Megumi; Nakahara, Fumitaka; Kawaguchi, Jun
2013-01-01
Five experiments examined what is learned based on the perceptual and semantic information of objects in visual statistical learning (VSL). In the familiarization phase, participants viewed a sequence of line drawings and detected repetitions of various objects. In a subsequent test phase, they watched 2 test sequences (statistically related…
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…
ERIC Educational Resources Information Center
Carlisle, Ysanne
This learning unit on descriptive statistics is one in the Choice Series, a self-learning development program for supervisors. Purpose stated for the approximately eight-hour-long unit is to enable the supervisor to understand the nature and uses of statistics at work, understand information which is obtained from figures at work, use statistics…
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…
"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…
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…
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…
Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis
Obeid, Rita; Brooks, Patricia J.; Powers, Kasey L.; Gillespie-Lynch, Kristen; Lum, Jarrad A. G.
2016-01-01
Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI) and Autism Spectrum Disorder (ASD). Using meta-analysis, we examined statistical learning in SLI (14 studies, 15 comparisons) and ASD (13 studies, 20 comparisons) to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, and Probabilistic Classification). Individuals with SLI showed deficits in statistical learning relative to age-matched controls. In contrast, statistical learning was intact in individuals with ASD relative to controls. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping social-communicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman and Pierpont, 2005), impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD. PMID:27602006
Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis.
Obeid, Rita; Brooks, Patricia J; Powers, Kasey L; Gillespie-Lynch, Kristen; Lum, Jarrad A G
2016-01-01
Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI) and Autism Spectrum Disorder (ASD). Using meta-analysis, we examined statistical learning in SLI (14 studies, 15 comparisons) and ASD (13 studies, 20 comparisons) to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, and Probabilistic Classification). Individuals with SLI showed deficits in statistical learning relative to age-matched controls. In contrast, statistical learning was intact in individuals with ASD relative to controls. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping social-communicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman and Pierpont, 2005), impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD.
ERIC Educational Resources Information Center
McGinn, Michelle K.
2010-01-01
This paper presents a qualitative case study of statistical practice in a university-based statistical consulting centre. Naturally occurring conversations and activities in the consulting sessions provided opportunities to observe questions, problems, and decisions related to selecting, using, and reporting statistics and statistical techniques…
The use of machine learning and nonlinear statistical tools for ADME prediction.
Sakiyama, Yojiro
2009-02-01
Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future.
Fostering Students' Statistical Literacy through Significant Learning Experience
ERIC Educational Resources Information Center
Krishnan, Saras
2015-01-01
A major objective of statistics education is to develop students' statistical literacy that enables them to be educated users of data in context. Teaching statistics in today's educational settings is not an easy feat because teachers have a huge task in keeping up with the demands of the new generation of learners. The present day students have…
Signal Processing For Chemical Sensing: Statistics or Biological Inspiration
NASA Astrophysics Data System (ADS)
Marco, Santiago
2011-09-01
Current analytical instrumentation and continuous sensing can provide huge amounts of data. Automatic signal processing and information evaluation is needed to overcome drowning in data. Today, statistical techniques are typically used to analyse and extract information from continuous signals. However, it is very interesting to note that biology (insects and vertebrates) has found alternative solutions for chemical sensing and information processing. This is a brief introduction to the developments in the European Project: Bio-ICT NEUROCHEM: Biologically Inspired Computation for Chemical Sensing (grant no. 216916) Fp7 project devoted to biomimetic olfactory systems.
Multivariate statistical monitoring and diagnosis with applications in semiconductor processes
NASA Astrophysics Data System (ADS)
Yue, Hongyu
Modern chemical processes generate a tremendous amount of measurement data that could be used for process monitoring. Deviation of the process measurements from the specifications could indicate an abnormal condition. It is important to have an effective technique to detect, identify and correct the fault. Because of the correlation of the sensor data, multivariate statistical methods are preferred for process monitoring. In the semiconductor manufacturing industry, many processes have been monitored in a univariate and off-line fashion. Due to the increasing complexity and shrinking feature size of integrated circuits, real-time monitoring by analysis of tool measurements is required to detect and classify faults. This dissertation focuses on developing process monitoring techniques and applications in plasma etching and rapid thermal annealing processes. While principal component analysis (PCA) has found wide application in process monitoring, slow and normal changes often occur in real processes, which lead to false alarms for a fixed-model approach. Recursive PCA is proposed for adaptive process monitoring. Two algorithms are developed to update the model efficiently. Although it is relatively easy to detect a fault, fault identification is a more complicated task. A combined index is first proposed for fault detection and identification. It is shown that the identification result is more accurate than other existing methods. The methods are applied in monitoring a rapid thermal annealing process. Plasma etching is one of the most important processes. It is considered one of the yield limiter because of the occurrence of frequent faults. Tight process monitoring is therefore required to detect the process endpoint and faults. This research uses optical emission spectroscopy sensors to collect high-resolution spectra data. PCA is used to analyze the data for the purpose of low-open area endpoint detection and fault detection. New methods are developed for
Theoretical Description of Teaching-Learning Processes: A Multidisciplinary Approach
NASA Astrophysics Data System (ADS)
Bordogna, Clelia M.; Albano, Ezequiel V.
2001-09-01
A multidisciplinary approach based on concepts from sociology, educational psychology, statistical physics, and computational science is developed for the theoretical description of teaching-learning processes that take place in the classroom. The emerging model is consistent with well-established empirical results, such as the higher achievements reached working in collaborative groups and the influence of the structure of the group on the achievements of the individuals. Furthermore, another social learning process that takes place in massive interactions among individuals via the Internet is also investigated.
Theoretical description of teaching-learning processes: a multidisciplinary approach.
Bordogna, C M; Albano, E V
2001-09-10
A multidisciplinary approach based on concepts from sociology, educational psychology, statistical physics, and computational science is developed for the theoretical description of teaching-learning processes that take place in the classroom. The emerging model is consistent with well-established empirical results, such as the higher achievements reached working in collaborative groups and the influence of the structure of the group on the achievements of the individuals. Furthermore, another social learning process that takes place in massive interactions among individuals via the Internet is also investigated.
Functions of the Learning Portfolio in Student Teachers' Learning Process
ERIC Educational Resources Information Center
Mansvelder-Longayroux, Desiree D.; Beijaard, Douwe; Verloop, Nico; Vermunt, Jan D.
2007-01-01
In this study, we aimed to develop a framework that could be used to describe the value of the learning portfolio for the learning process of individual student teachers. Retrospective interviews with 21 student teachers were used, as were their portfolio-evaluation reports on their experiences of working on a portfolio. Seven functions of the…
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…
ERIC Educational Resources Information Center
Wessa, Patrick
2009-01-01
This paper discusses the implementation of a new e-learning environment that supports non-rote learning of exploratory and inductive statistics within the pedagogical paradigm of social constructivism. The e-learning system is based on a new computational framework that allows us to create an electronic research environment where students are…
Statistical Word Learning at Scale: The Baby's View Is Better
ERIC Educational Resources Information Center
Yurovsky, Daniel; Smith, Linda B.; Yu, Chen
2013-01-01
A key question in early word learning is how children cope with the uncertainty in natural naming events. One potential mechanism for uncertainty reduction is cross-situational word learning--tracking word/object co-occurrence statistics across naming events. But empirical and computational analyses of cross-situational learning have made strong…
Older People and Learning--Some Key Statistics. NIACE Briefing Sheet.
ERIC Educational Resources Information Center
National Inst. of Adult Continuing Education, Leicester (England).
This briefing sheet provides a summary of statistics (primarily from United Kingdom and Dutch surveys) that relate to the participation of older people in learning. It provides evidence of current participation, recent trends, types of learning in which older people are involved, future intentions, and correlation between learning in later life…
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
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.
Competitive Processes in Cross-Situational Word Learning
ERIC Educational Resources Information Center
Yurovsky, Daniel; Yu, Chen; Smith, Linda B.
2013-01-01
Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition.…
Noncommutative Lévy Processes for Generalized (Particularly Anyon) Statistics
NASA Astrophysics Data System (ADS)
Bożejko, Marek; Lytvynov, Eugene; Wysoczański, Janusz
2012-07-01
Let {T=R^d} . Let a function {QT^2toC} satisfy {Q(s,t)=overline{Q(t,s)}} and {|Q(s,t)|=1}. A generalized statistics is described by creation operators {partial_t^dagger} and annihilation operators ∂ t , {tin T}, which satisfy the Q-commutation relations: {partial_spartial^dagger_t = Q(s, t)partial^dagger_tpartial_s+δ(s, t)} , {partial_spartial_t = Q(t, s)partial_tpartial_s}, {partial^dagger_spartial^dagger_t = Q(t, s)partial^dagger_tpartial^dagger_s}. From the point of view of physics, the most important case of a generalized statistics is the anyon statistics, for which Q( s, t) is equal to q if s < t, and to {bar q} if s > t. Here {qinC} , | q| = 1. We start the paper with a detailed discussion of a Q-Fock space and operators {(partial_t^dagger,partial_t)_{tin T}} in it, which satisfy the Q-commutation relations. Next, we consider a noncommutative stochastic process (white noise) {ω(t)=partial_t^dagger+partial_t+λpartial_t^daggerpartial_t} , {tin T} . Here {λinR} is a fixed parameter. The case λ = 0 corresponds to a Q-analog of Brownian motion, while λ ≠ 0 corresponds to a (centered) Q-Poisson process. We study Q-Hermite ( Q-Charlier respectively) polynomials of infinitely many noncommutatative variables {(ω(t))_{tin T}} . The main aim of the paper is to explain the notion of independence for a generalized statistics, and to derive corresponding Lévy processes. To this end, we recursively define Q-cumulants of a field {(ξ(t))_{tin T}}. This allows us to define a Q-Lévy process as a field {(ξ(t))_{tin T}} whose values at different points of T are Q-independent and which possesses a stationarity of increments (in a certain sense). We present an explicit construction of a Q-Lévy process, and derive a Nualart-Schoutens-type chaotic decomposition for such a process.
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…
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…
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…
Effectively Managing and Processing Personal Learning Content
NASA Astrophysics Data System (ADS)
Sieber, Stefanie; Henrich, Andreas
Comparing the typical learner only about one or two decades ago and today, an undeniable difference is evident. By now, most of traditional learning has been moved into a continuum of blended learning, as defined by Jones [1]. In this context, especially ways for distributing learning material and contacting each other have completely changed. Learning Management Systems (LMS) are, in the majority of cases, employed to provide a basis for modern learning. Whether it comes to distance learning or not, all required material, including additional information for further reading and tasks controlling the learner’s success, is provided online such that learners can easily access all desired information any time and any place. Communication has been detached from specific points or periods in time as well. This digitalization of the learning process also bears new challenges, which have to be faced by learners as well as lecturers.
Competent statistical programmer: Need of business process outsourcing industry.
Khan, Imran
2014-07-01
Over the last two decades Business Process Outsourcing (BPO) has evolved as much mature practice. India is looked as preferred destination for pharmaceutical outsourcing over a cost arbitrage. Among the biometrics outsourcing, statistical programming and analysis required very niche skill for service delivery. The demand and supply ratios are imbalance due to high churn out rate and less supply of competent programmer. Industry is moving from task delivery to ownership and accountability. The paradigm shift from an outsourcing to consulting is triggering the need for competent statistical programmer. Programmers should be trained in technical, analytical, problem solving, decision making and soft skill as the expectations from the customer are changing from task delivery to accountability of the project. This paper will highlight the common issue SAS programming service industry is facing and skills the programmers need to develop to cope up with these changes.
Competent statistical programmer: Need of business process outsourcing industry
Khan, Imran
2014-01-01
Over the last two decades Business Process Outsourcing (BPO) has evolved as much mature practice. India is looked as preferred destination for pharmaceutical outsourcing over a cost arbitrage. Among the biometrics outsourcing, statistical programming and analysis required very niche skill for service delivery. The demand and supply ratios are imbalance due to high churn out rate and less supply of competent programmer. Industry is moving from task delivery to ownership and accountability. The paradigm shift from an outsourcing to consulting is triggering the need for competent statistical programmer. Programmers should be trained in technical, analytical, problem solving, decision making and soft skill as the expectations from the customer are changing from task delivery to accountability of the project. This paper will highlight the common issue SAS programming service industry is facing and skills the programmers need to develop to cope up with these changes. PMID:24987578
Statistical Learning of Past Participle Inflections in French
ERIC Educational Resources Information Center
Negro, Isabelle; Bonnotte, Isabelle; Lété, Bernard
2014-01-01
The purpose of this research was to understand better how morphemic units are encoded and auto-organised in memory and how they are accessed during writing. We hypothesised that the activation of morphemic units would not depend on rule-based learning during primary school but would be determined by frequency-based learning, which is a process…
Fast Quantum Algorithm for Predicting Descriptive Statistics of Stochastic Processes
NASA Technical Reports Server (NTRS)
Williams Colin P.
1999-01-01
Stochastic processes are used as a modeling tool in several sub-fields of physics, biology, and finance. Analytic understanding of the long term behavior of such processes is only tractable for very simple types of stochastic processes such as Markovian processes. However, in real world applications more complex stochastic processes often arise. In physics, the complicating factor might be nonlinearities; in biology it might be memory effects; and in finance is might be the non-random intentional behavior of participants in a market. In the absence of analytic insight, one is forced to understand these more complex stochastic processes via numerical simulation techniques. In this paper we present a quantum algorithm for performing such simulations. In particular, we show how a quantum algorithm can predict arbitrary descriptive statistics (moments) of N-step stochastic processes in just O(square root of N) time. That is, the quantum complexity is the square root of the classical complexity for performing such simulations. This is a significant speedup in comparison to the current state of the art.
Interdisciplinary Learning: Process and Outcomes.
ERIC Educational Resources Information Center
Ivanitskaya, Lana; Clark, Deborah; Montgomery, George; Primeau, Ronald
2002-01-01
Presents an adaptation of Biggs and Collis' (1982) Structure of the Observed Learning Outcome which illustrates stages of interdisciplinary knowledge integration and explains corresponding patterns of learners' intellectual functioning, from acquisition of single-subject information to transfer of interdisciplinary knowledge to other topics,…
Learning Objects and Process Interoperability
ERIC Educational Resources Information Center
Baker, Keith D.
2006-01-01
There has been considerable emphasis on the availability and reuse of learning content in recent years. Since 2000, the ADL initiative has refined the recommendations contained in the SCORM documents through progressive stages represented in the SCORM 1.0, 1.1, 1.2 and 1.3 documents. Fundamental to SCORM is the notion of the Shareable Content…
Learning Routines in Innovation Processes
ERIC Educational Resources Information Center
Hoeve, Aimee; Nieuwenhuis, Loek F. M.
2006-01-01
Purpose: This paper aims to generate both a theoretical and an empirical basis for a research model that serves in further research as an analytical tool for understanding the complex phenomenon of learning at different levels in a work organisation. The key concept in this model is the routine concept of Nelson and Winter.…
Reconstruction of a Collaborative Mathematical Learning Process
ERIC Educational Resources Information Center
Pijls, Monique; Dekker, Rijkje; Van Hout-Wolters, Bernadette
2007-01-01
The study focused on the interaction between two secondary school students while they were working on computerized mathematical investigation tasks related to probability theory. The aim was to establish how such interaction helped the students to learn from one another, and how it may have hindered their learning process. The assumption was that…
Integrating Web Portfolios into the Learning Process
ERIC Educational Resources Information Center
D'Ambrosio, Jay
2004-01-01
Web portfolios should be integrated in the learning process as they encourage the students to take an active interest in their own learning and highlights the individual students, their interests and their goals, increases student motivation and provides an excellent method for teaching students. Implementation of Web portfolios could be made more…
Basic Visual Processes and Learning Disability.
ERIC Educational Resources Information Center
Leisman, Gerald
Representatives of a variety of disciplines concerned with either clinical or research problems in vision and learning disabilities present reviews and reports of relevant research and clinical approaches. Contributions are organized into four broad sections: basic processes, specific disorders, diagnosis of visually based problems in learning,…
Time statistics of the photoelectron emission process in scintillation counters
NASA Astrophysics Data System (ADS)
Ranucci, Gioacchino
1993-10-01
In this work the statistical time properties of the photoelectron emission process in scintillation counters are evaluated assuming that the total number of emitted photoelectrons is distributed according to a generic random distribution. Under this general assumption, the probability density function of the time of emission of the ith photoelectron is computed; it is also demonstrated that if the number of emitted photoelectrons is Poisson distributed, this probability density function reduces to the expression already published for this particular case. Finally the procedure adopted is extended to give the expressions predicting the performances of organic scintillators for the pulse shape discrimination of particles of different type.
Distance Learning for Teacher Professional Development in Statistics Education
ERIC Educational Resources Information Center
Meletiou-Mavrotheris, Maria; Mavrotheris, Efstathios; Paparistodemou, Efi
2011-01-01
We provide an overview of "EarlyStatistics," an online professional development course in statistics education targeting European elementary and middle school teachers. The course facilitates intercultural collaboration of teachers using contemporary technological and educational tools. An online information base offers access to all of…
Speech Segmentation by Statistical Learning Depends on Attention
ERIC Educational Resources Information Center
Toro, Juan M.; Sinnett, Scott; Soto-Faraco, Salvador
2005-01-01
We addressed the hypothesis that word segmentation based on statistical regularities occurs without the need of attention. Participants were presented with a stream of artificial speech in which the only cue to extract the words was the presence of statistical regularities between syllables. Half of the participants were asked to passively listen…
Dissociable Learning Processes Underlie Human Pain Conditioning.
Zhang, Suyi; Mano, Hiroaki; Ganesh, Gowrishankar; Robbins, Trevor; Seymour, Ben
2016-01-11
Pavlovian conditioning underlies many aspects of pain behavior, including fear and threat detection [1], escape and avoidance learning [2], and endogenous analgesia [3]. Although a central role for the amygdala is well established [4], both human and animal studies implicate other brain regions in learning, notably ventral striatum and cerebellum [5]. It remains unclear whether these regions make different contributions to a single aversive learning process or represent independent learning mechanisms that interact to generate the expression of pain-related behavior. We designed a human parallel aversive conditioning paradigm in which different Pavlovian visual cues probabilistically predicted thermal pain primarily to either the left or right arm and studied the acquisition of conditioned Pavlovian responses using combined physiological recordings and fMRI. Using computational modeling based on reinforcement learning theory, we found that conditioning involves two distinct types of learning process. First, a non-specific "preparatory" system learns aversive facial expressions and autonomic responses such as skin conductance. The associated learning signals-the learned associability and prediction error-were correlated with fMRI brain responses in amygdala-striatal regions, corresponding to the classic aversive (fear) learning circuit. Second, a specific lateralized system learns "consummatory" limb-withdrawal responses, detectable with electromyography of the arm to which pain is predicted. Its related learned associability was correlated with responses in ipsilateral cerebellar cortex, suggesting a novel computational role for the cerebellum in pain. In conclusion, our results show that the overall phenotype of conditioned pain behavior depends on two dissociable reinforcement learning circuits.
Linking sounds to meanings: Infant statistical learning in a natural language
Hay, Jessica F.; Pelucchi, Bruna; Estes, Katharine Graf; Saffran, Jenny R.
2011-01-01
The processes of infant word segmentation and infant word learning have largely been studied separately. However, the ease with which potential word forms are segmented from fluent speech seems likely to influence subsequent mappings between words and their referents. To explore this process, we tested the link between the statistical coherence of sequences presented in fluent speech and infants’ subsequent use of those sequences as labels for novel objects. Notably, the materials were drawn from a natural language unfamiliar to the infants (Italian). The results of three experiments suggest that there is a close relationship between the statistics of the speech stream and subsequent mapping of labels to referents. Mapping was facilitated when the labels contained high transitional probabilities in the forward and/or backward direction (Experiment 1). When no transitional probability information was available (Experiment 2), or when the internal transitional probabilities of the labels were low in both directions (Experiment 3), infants failed to link the labels to their referents. Word learning appears to be strongly influenced by infants’ prior experience with the distribution of sounds that make up words in natural languages. PMID:21762650
ERIC Educational Resources Information Center
Yurdugül, Halil; Menzi Çetin, Nihal
2015-01-01
Problem Statement: Learners can access and participate in online learning environments regardless of time and geographical barriers. This brings up the umbrella concept of learner autonomy that contains self-directed learning, self-regulated learning and the studying process. Motivation and learning strategies are also part of this umbrella…
Emberson, Lauren L; Rubinstein, Dani Y
2016-08-01
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
The role of Gestalt grouping principles in visual statistical learning.
Glicksohn, Arit; Cohen, Asher
2011-04-01
A major issue in visual scene recognition involves the extraction of recurring chunks from a sequence of complex scenes. Previous studies have suggested that this kind of learning is accomplished according to Bayesian principles that constrain the types of extracted chunks. Here we show that perceptual grouping cues are also incorporated in this Bayesian model, providing additional evidence for the possible span of chunks. Experiment 1 replicates previous results showing that observers can learn three-element chunks without learning smaller, two-element chunks embedded within them. Experiment 2 shows that the very same embedded chunks are learned if they are grouped by perceptual cues, suggesting that perceptual grouping cues play an important role in chunk extraction from complex scenes.
Statistical method for detecting structural change in the growth process.
Ninomiya, Yoshiyuki; Yoshimoto, Atsushi
2008-03-01
Due to competition among individual trees and other exogenous factors that change the growth environment, each tree grows following its own growth trend with some structural changes in growth over time. In the present article, a new method is proposed to detect a structural change in the growth process. We formulate the method as a simple statistical test for signal detection without constructing any specific model for the structural change. To evaluate the p-value of the test, the tube method is developed because the regular distribution theory is insufficient. Using two sets of tree diameter growth data sampled from planted forest stands of Cryptomeria japonica in Japan, we conduct an analysis of identifying the effect of thinning on the growth process as a structural change. Our results demonstrate that the proposed method is useful to identify the structural change caused by thinning. We also provide the properties of the method in terms of the size and power of the test.
What can we learn from noise? - Mesoscopic nonequilibrium statistical physics.
Kobayashi, Kensuke
2016-01-01
Mesoscopic systems - small electric circuits working in quantum regime - offer us a unique experimental stage to explorer quantum transport in a tunable and precise way. The purpose of this Review is to show how they can contribute to statistical physics. We introduce the significance of fluctuation, or equivalently noise, as noise measurement enables us to address the fundamental aspects of a physical system. The significance of the fluctuation theorem (FT) in statistical physics is noted. We explain what information can be deduced from the current noise measurement in mesoscopic systems. As an important application of the noise measurement to statistical physics, we describe our experimental work on the current and current noise in an electron interferometer, which is the first experimental test of FT in quantum regime. Our attempt will shed new light in the research field of mesoscopic quantum statistical physics.
Framework for Conducting Empirical Observations of Learning Processes.
ERIC Educational Resources Information Center
Fischer, Hans Ernst; von Aufschnaiter, Stephan
1993-01-01
Reviews four hypotheses about learning: Comenius's transmission-reception theory, information processing theory, Gestalt theory, and Piagetian theory. Uses the categories preunderstanding, conceptual change, and learning processes to classify and assess investigations on learning processes. (PR)
Statistical distributions of earthquake numbers: consequence of branching process
NASA Astrophysics Data System (ADS)
Kagan, Yan Y.
2010-03-01
We discuss various statistical distributions of earthquake numbers. Previously, we derived several discrete distributions to describe earthquake numbers for the branching model of earthquake occurrence: these distributions are the Poisson, geometric, logarithmic and the negative binomial (NBD). The theoretical model is the `birth and immigration' population process. The first three distributions above can be considered special cases of the NBD. In particular, a point branching process along the magnitude (or log seismic moment) axis with independent events (immigrants) explains the magnitude/moment-frequency relation and the NBD of earthquake counts in large time/space windows, as well as the dependence of the NBD parameters on the magnitude threshold (magnitude of an earthquake catalogue completeness). We discuss applying these distributions, especially the NBD, to approximate event numbers in earthquake catalogues. There are many different representations of the NBD. Most can be traced either to the Pascal distribution or to the mixture of the Poisson distribution with the gamma law. We discuss advantages and drawbacks of both representations for statistical analysis of earthquake catalogues. We also consider applying the NBD to earthquake forecasts and describe the limits of the application for the given equations. In contrast to the one-parameter Poisson distribution so widely used to describe earthquake occurrence, the NBD has two parameters. The second parameter can be used to characterize clustering or overdispersion of a process. We determine the parameter values and their uncertainties for several local and global catalogues, and their subdivisions in various time intervals, magnitude thresholds, spatial windows, and tectonic categories. The theoretical model of how the clustering parameter depends on the corner (maximum) magnitude can be used to predict future earthquake number distribution in regions where very large earthquakes have not yet occurred.
Tillmann, Barbara; McAdams, Stephen
2004-09-01
The present study investigated the influence of acoustical characteristics on the implicit learning of statistical regularities (transition probabilities) in sequences of musical timbres. The sequences were constructed in such a way that the acoustical dissimilarities between timbres potentially created segmentations that either supported (S1) or contradicted (S2) the statistical regularities or were neutral (S3). In the learning group, participants first listened to the continuous timbre sequence and then had to distinguish statistical units from new units. In comparison to a control group without the exposition phase, no interaction between sequence type and amount of learning was observed: Performance increased by the same amount for the three sequences. In addition, performance reflected an overall preference for acoustically similar timbre units. The present outcome extends previous data from the domain of implicit learning to complex nonverbal auditory material. It further suggests that listeners become sensitive to statistical regularities despite acoustical characteristics in the material that potentially affect grouping.
Infants' statistical learning: 2- and 5-month-olds' segmentation of continuous visual sequences.
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.
Statistical learning of recurring sound patterns encodes auditory objects in songbird forebrain.
Lu, Kai; Vicario, David S
2014-10-07
Auditory neurophysiology has demonstrated how basic acoustic features are mapped in the brain, but it is still not clear how multiple sound components are integrated over time and recognized as an object. We investigated the role of statistical learning in encoding the sequential features of complex sounds by recording neuronal responses bilaterally in the auditory forebrain of awake songbirds that were passively exposed to long sound streams. These streams contained sequential regularities, and were similar to streams used in human infants to demonstrate statistical learning for speech sounds. For stimulus patterns with contiguous transitions and with nonadjacent elements, single and multiunit responses reflected neuronal discrimination of the familiar patterns from novel patterns. In addition, discrimination of nonadjacent patterns was stronger in the right hemisphere than in the left, and may reflect an effect of top-down modulation that is lateralized. Responses to recurring patterns showed stimulus-specific adaptation, a sparsening of neural activity that may contribute to encoding invariants in the sound stream and that appears to increase coding efficiency for the familiar stimuli across the population of neurons recorded. As auditory information about the world must be received serially over time, recognition of complex auditory objects may depend on this type of mnemonic process to create and differentiate representations of recently heard sounds.
Speech segmentation by statistical learning depends on attention.
Toro, Juan M; Sinnett, Scott; Soto-Faraco, Salvador
2005-09-01
We addressed the hypothesis that word segmentation based on statistical regularities occurs without the need of attention. Participants were presented with a stream of artificial speech in which the only cue to extract the words was the presence of statistical regularities between syllables. Half of the participants were asked to passively listen to the speech stream, while the other half were asked to perform a concurrent task. In Experiment 1, the concurrent task was performed on a separate auditory stream (noises), in Experiment 2 it was performed on a visual stream (pictures), and in Experiment 3 it was performed on pitch changes in the speech stream itself. Invariably, passive listening to the speech stream led to successful word extraction (as measured by a recognition test presented after the exposure phase), whereas diverted attention led to a dramatic impairment in word segmentation performance. These findings demonstrate that when attentional resources are depleted, word segmentation based on statistical regularities is seriously compromised.
2012-01-01
assume that the NSMS can be approximated by a series of expansion functions F m ( ) such that ( ) m F m ( ) m1 M (31) UXO...a receiver coil is the electromotive force given by the negative of the time derivative of the secondary magnetic flux through the coil. Since the...statistical signal processing MM-1572 Final Report Sky Research, Inc. January 2012 52 A support vector machine learns from data: when fed a series
Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques
2010-05-01
Vasin Punyakanok, Dan Roth and Wen-tau Yih (2005). Generalized inference with multiple semantic role labeling systems. In Proceedings of the Ninth...Uncertainty in Artificial Intelligence (UAI- 2005). Edinburgh, Scotland . Luke S. Zettlemoyer and Michael Collins (2007). Online learning of relaxed
Statistical Inference-Based Cache Management for Mobile Learning
ERIC Educational Resources Information Center
Li, Qing; Zhao, Jianmin; Zhu, Xinzhong
2009-01-01
Supporting efficient data access in the mobile learning environment is becoming a hot research problem in recent years, and the problem becomes tougher when the clients are using light-weight mobile devices such as cell phones whose limited storage space prevents the clients from holding a large cache. A practical solution is to store the cache…
Extended Statistical Learning as an Account for Slow Vocabulary Growth
ERIC Educational Resources Information Center
Stokes, Stephanie F.; Kern, Sophie; dos Santos, Christophe
2012-01-01
Stokes (2010) compared the lexicons of English-speaking late talkers (LT) with those of their typically developing (TD) peers on neighborhood density (ND) and word frequency (WF) characteristics and suggested that LTs employed learning strategies that differed from those of their TD peers. This research sought to explore the cross-linguistic…
Comparative Analysis of Kernel Methods for Statistical Shape Learning
2006-01-01
successfully used by the machine learning community for pattern recognition and image denoising [14]. A Gaussian kernel was used by Cremers et al. [8] for...matrix M, where φi ∈ RNd . Using Singular Value Decomposition ( SVD ), the covariance matrix 1nMM T is decomposed as: UΣUT = 1 n MMT (1) where U is a
Statistical mechanics of reward-modulated learning in decision-making networks.
Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato
2012-05-01
The neural substrates of decision making have been intensively studied using experimental and computational approaches. Alternative-choice tasks accompanying reinforcement have often been employed in investigations into decision making. Choice behavior has been empirically found in many experiments to follow Herrnstein's matching law. A number of theoretical studies have been done on explaining the mechanisms responsible for matching behavior. Various learning rules have been proved in these studies to achieve matching behavior as a steady state of learning processes. The models in the studies have consisted of a few parameters. However, a large number of neurons and synapses are expected to participate in decision making in the brain. We investigated learning behavior in simple but large-scale decision-making networks. We considered the covariance learning rule, which has been demonstrated to achieve matching behavior as a steady state (Loewenstein & Seung, 2006 ). We analyzed model behavior in a thermodynamic limit where the number of plastic synapses went to infinity. By means of techniques of the statistical mechanics, we can derive deterministic differential equations in this limit for the order parameters, which allow an exact calculation of the evolution of choice behavior. As a result, we found that matching behavior cannot be a steady state of learning when the fluctuations in input from individual sensory neurons are so large that they affect the net input to value-encoding neurons. This situation naturally arises when the synaptic strength is sufficiently strong and the excitatory input and the inhibitory input to the value-encoding neurons are balanced. The deviation from matching behavior is caused by increasing variance in the input potential due to the diffusion of synaptic efficacies. This effect causes an undermatching phenomenon, which has been often observed in behavioral experiments.
Processes and subdivisions in diogenites, a multivariate statistical analysis
NASA Technical Reports Server (NTRS)
Harriott, T. A.; Hewins, R. H.
1984-01-01
Multivariate statistical techniques used on diogenite orthopyroxene analyses show the relationships that occur within diogenites and the two orthopyroxenite components (class I and II) in the polymict diogenite Garland. Cluster analysis shows that only Peckelsheim is similar to Garland class I (Fe-rich) and the other diogenites resemble Garland class II. The unique diogenite Y 75032 may be related to type I by fractionation. Factor analysis confirms the subdivision and shows that Fe does not correlate with the weakly incompatible elements across the entire pyroxene composition range, indicating that igneous fractionation is not the process controlling total diogenite composition variation. The occurrence of two groups of diogenites is interpreted as the result of sampling or mixing of two main sequences of orthopyroxene cumulates with slightly different compositions.
Global Statistical Learning in a Visual Search Task
ERIC Educational Resources Information Center
Jones, John L.; Kaschak, Michael P.
2012-01-01
Locating a target in a visual search task is facilitated when the target location is repeated on successive trials. Global statistical properties also influence visual search, but have often been confounded with local regularities (i.e., target location repetition). In two experiments, target locations were not repeated for four successive trials,…
Lessons Learned from Statistical Reform Efforts in Other Disciplines
ERIC Educational Resources Information Center
Fidler, Fiona; Cumming, Geoff
2007-01-01
Compelling arguments for reform of statistical practices have been made in many disciplines, in some cases over several decades, but achieving reform has proved difficult. We discuss how reform has progressed--or not progressed--in psychology, medicine, and ecology and describe case studies of attempts by pioneering journal editors to change…
Temporal and Statistical Information in Causal Structure Learning
ERIC Educational Resources Information Center
McCormack, Teresa; Frosch, Caren; Patrick, Fiona; Lagnado, David
2015-01-01
Three experiments examined children's and adults' abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a 3-variable mechanical…
Peer-Assisted Learning in Research Methods and Statistics
ERIC Educational Resources Information Center
Stone, Anna; Meade, Claire; Watling, Rosamond
2012-01-01
Feedback from students on a Level 1 Research Methods and Statistics module, studied as a core part of a BSc Psychology programme, highlighted demand for additional tutorials to help them to understand basic concepts. Students in their final year of study commonly request work experience to enhance their employability. All students on the Level 1…
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…
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…
Students Learn Statistics When They Assume a Statistician's Role.
ERIC Educational Resources Information Center
Sullivan, Mary M.
Traditional elementary statistics instruction for non-majors has focused on computation. Rarely have students had an opportunity to interact with real data sets or to use questioning to drive data analysis, common activities among professional statisticians. Inclusion of data gathering and analysis into whole class and small group activities…
Analysing Change in Learning Strategies over Time: A Comparison of Three Statistical Techniques
ERIC Educational Resources Information Center
Coertjens, Liesje; van Daal, Tine; Donche, Vincent; De Maeyer, Sven; Vanthournout, Gert; Van Petegem, Peter
2013-01-01
Change in learning strategies during higher education is an important topic of research when considering students' approaches to learning. Regarding the statistical techniques used to analyse this change, repeated measures ANOVA is mostly relied upon. Recently, multilevel and multi-indicator latent growth (MILG) analyses have been used as well.…
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…
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…
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…
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…
The Role of Statistical Learning in the Acquisition of Motion Event Construal in a Second Language
ERIC Educational Resources Information Center
Treffers-Daller, Jeanine; Calude, Andreea
2015-01-01
Learning to talk about motion in a second language is very difficult because it involves restructuring deeply entrenched patterns from the first language. In this paper we argue that statistical learning can explain why L2 learners are only partially successful in restructuring their second language grammars. We explore to what extent L2 learners…
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
Links to sources of cancer-related statistics, including the Surveillance, Epidemiology and End Results (SEER) Program, SEER-Medicare datasets, cancer survivor prevalence data, and the Cancer Trends Progress Report.
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…
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.…
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…
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…
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.…
Sex Differences in the Relation between Statistics Anxiety and Cognitive/Learning Strategies
ERIC Educational Resources Information Center
Rodarte-Luna, Bertha; Sherry, Alissa
2008-01-01
Three hundred twenty three students were recruited in order to investigate sex differences on measures of statistics anxiety and learning strategies. Data was analyzed using descriptive discriminant analysis and canonical correlation analysis. Findings indicated that sex differences on these measures were statistically significant, but with small…
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,…
An appeal to undergraduate wildlife programs: send scientists to learn statistics
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.
Wang, Yi; Ma, Xiang; Wen, Ya-Dong; Zou, Quan; Wang, Jun; Tu, Jia-Run; Cai, Wen-Sheng; Shao, Xue-Guang
2013-05-01
Near infrared diffusive reflectance spectroscopy has been applied in on-site or on-line analysis due to its characteristics of fastness, non-destruction and the feasibility for real complex sample analysis. The present work reported a real-time monitoring method for industrial production by using near infrared spectroscopic technique and multivariate statistical process analysis. In the method, the real-time near infrared spectra of the materials are collected on the production line, and then the evaluation of the production process can be achieved by a statistic Hotelling T2 calculated with the established model. In this work, principal component analysis (PCA) is adopted for building the model, and the statistic is calculated by projecting the real-time spectra onto the PCA model. With an application of the method in a practical production, it was demonstrated that a real-time evaluation of the variations in the production can be realized by investigating the changes in the statistic, and the comparison of the products in different batches can be achieved by further statistics of the statistic. Therefore, the proposed method may provide a practical way for quality insurance of production processes.
Statistical learning in songbirds: from self-tutoring to song culture
Fehér, Olga; Ljubičić, Iva; Suzuki, Kenta; Okanoya, Kazuo; Tchernichovski, Ofer
2017-01-01
At the onset of vocal development, both songbirds and humans produce variable vocal babbling with broadly distributed acoustic features. Over development, these vocalizations differentiate into the well-defined, categorical signals that characterize adult vocal behaviour. A broadly distributed signal is ideal for vocal exploration, that is, for matching vocal production to the statistics of the sensory input. The developmental transition to categorical signals is a gradual process during which the vocal output becomes differentiated and stable. But does it require categorical input? We trained juvenile zebra finches with playbacks of their own developing song, produced just a few moments earlier, updated continuously over development. Although the vocalizations of these self-tutored (ST) birds were initially broadly distributed, birds quickly developed categorical signals, as fast as birds that were trained with a categorical, adult song template. By contrast, siblings of those birds that received no training (isolates) developed phonological categories much more slowly and never reached the same level of category differentiation as their ST brothers. Therefore, instead of simply mirroring the statistical properties of their sensory input, songbirds actively transform it into distinct categories. We suggest that the early self-generation of phonological categories facilitates the establishment of vocal culture by making the song easier to transmit at the micro level, while promoting stability of shared vocabulary at the group level over generations. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872371
Statistical Results on Filtering and Epi-convergence for Learning-Based Model Predictive Control
2011-12-17
be described by ordinary differential equations (ODE’s). The second part provides proofs concerning the epi-convergence of different statistical...and statistical identi cation (or learning) of unmodeled dynamics, for dynamical systems that can be described by ordinary di erential equations (ODE’s... differential equations (ODE’s). The second part is found in Section 4 and pro- vides proofs concerning the epi-convergence of different statistical
Role of Symbolic Coding and Rehearsal Processes in Observational Learning
ERIC Educational Resources Information Center
Bandura, Albert; Jeffery, Robert W.
1973-01-01
Results were interpreted supporting a social learning view of observational learning that emphasizes contral processing of response information in the acquisition phase and motor reproduction and incentive processes in the overt enactment of what has been learned. (Author)
Understanding the Advising Learning Process Using Learning Taxonomies
ERIC Educational Resources Information Center
Muehleck, Jeanette K.; Smith, Cathleen L.; Allen, Janine M.
2014-01-01
To better understand the learning that transpires in advising, we used Anderson et al.'s (2001) revision of Bloom's (1956) taxonomy and Krathwohl, Bloom, and Masia's (1964) affective taxonomy to analyze eight student-reported advising outcomes from Smith and Allen (2014). Using the cognitive processes and knowledge domains of Anderson et al.'s…
Unifying K-12 Learning Processes: Integrating Curricula through Learning
ERIC Educational Resources Information Center
Bosse, Michael J.; Fogarty, Elizabeth A.
2011-01-01
This study was designed to examine whether a set of cross-curricular learning processes could be found in the respective K-12 US national standards for math, language arts, foreign language, science, social studies, fine arts, and technology. Using a qualitative research methodology, the standards from the national associations for these content…
Body Learning: Examining the Processes of Skill Learning in Dance
ERIC Educational Resources Information Center
Bailey, Richard; Pickard, Angela
2010-01-01
This paper was stimulated by the authors' attempt to understand the process of skill learning in dance. Its stimulus was a period of fieldwork based at the Royal Ballet School in London, and subsequent discussions with the school's teachers and with academic colleagues about how it was that the young dancers developed their characteristic set of…
A Differential Theory of Learning for Efficient Statistical Pattern Recognition
1993-12-15
paradigms, limited time for learning, and search algorithms that become increasingly slow and subject to halting in local optima as classifier complexity...figure 8.33 is different from that in figure 8.23. Both sequences are given on page 270, although we ask the reader to indulge us by not peeking at the...that in figure 8.28. Both sequences are given on page 270; again, we ask the reader to indulge us by not peeking at the answers for a while. Figure 8.29
Improved Statistical Signal Processing of Nonstationary Random Processes Using Time-Warping
NASA Astrophysics Data System (ADS)
Wisdom, Scott Thomas
A common assumption used in statistical signal processing of nonstationary random signals is that the signals are locally stationary. Using this assumption, data is segmented into short analysis frames, and processing is performed using these short frames. Short frames limit the amount of data available, which in turn limits the performance of statistical estimators. In this thesis, we propose a novel method that promises improved performance for a variety of statistical signal processing algorithms. This method proposes to estimate certain time-varying parameters of nonstationary signals and then use this estimated information to perform a time-warping of the data that compensates for the time-varying parameters. Since the time-warped data is more stationary, longer analysis frames may be used, which improves the performance of statistical estimators. We first examine the spectral statistics of two particular types of nonstationary random processes that are useful for modeling ship propeller noise and voiced speech. We examine the effect of time-varying frequency content on these spectral statistics, and in addition show that the cross-frequency spectral statistics of these signals contain significant additional information that is not usually exploited using a stationary assumption. This information, combined with our proposed method, promises improvements for a wide variety of applications in the future. We then describe and test an implementation of our time-warping method, the fan-chirp transform. We apply our method to two applications, detection of ship noise in a passive sonar application and joint denoising and dereverberation of speech. Our method yields improved results for both applications compared to conventional methods.
GeoSegmenter: A statistically learned Chinese word segmenter for the geoscience domain
NASA Astrophysics Data System (ADS)
Huang, Lan; Du, Youfu; Chen, Gongyang
2015-03-01
Unlike English, the Chinese language has no space between words. Segmenting texts into words, known as the Chinese word segmentation (CWS) problem, thus becomes a fundamental issue for processing Chinese documents and the first step in many text mining applications, including information retrieval, machine translation and knowledge acquisition. However, for the geoscience subject domain, the CWS problem remains unsolved. Although a generic segmenter can be applied to process geoscience documents, they lack the domain specific knowledge and consequently their segmentation accuracy drops dramatically. This motivated us to develop a segmenter specifically for the geoscience subject domain: the GeoSegmenter. We first proposed a generic two-step framework for domain specific CWS. Following this framework, we built GeoSegmenter using conditional random fields, a principled statistical framework for sequence learning. Specifically, GeoSegmenter first identifies general terms by using a generic baseline segmenter. Then it recognises geoscience terms by learning and applying a model that can transform the initial segmentation into the goal segmentation. Empirical experimental results on geoscience documents and benchmark datasets showed that GeoSegmenter could effectively recognise both geoscience terms and general terms.
Awake, Offline Processing during Associative Learning.
Bursley, James K; Nestor, Adrian; Tarr, Michael J; Creswell, J David
2016-01-01
Offline processing has been shown to strengthen memory traces and enhance learning in the absence of conscious rehearsal or awareness. Here we evaluate whether a brief, two-minute offline processing period can boost associative learning and test a memory reactivation account for these offline processing effects. After encoding paired associates, subjects either completed a distractor task for two minutes or were immediately tested for memory of the pairs in a counterbalanced, within-subjects functional magnetic resonance imaging study. Results showed that brief, awake, offline processing improves memory for associate pairs. Moreover, multi-voxel pattern analysis of the neuroimaging data suggested reactivation of encoded memory representations in dorsolateral prefrontal cortex during offline processing. These results signify the first demonstration of awake, active, offline enhancement of associative memory and suggest that such enhancement is accompanied by the offline reactivation of encoded memory representations.
Awake, Offline Processing during Associative Learning
Nestor, Adrian; Tarr, Michael J.; Creswell, J. David
2016-01-01
Offline processing has been shown to strengthen memory traces and enhance learning in the absence of conscious rehearsal or awareness. Here we evaluate whether a brief, two-minute offline processing period can boost associative learning and test a memory reactivation account for these offline processing effects. After encoding paired associates, subjects either completed a distractor task for two minutes or were immediately tested for memory of the pairs in a counterbalanced, within-subjects functional magnetic resonance imaging study. Results showed that brief, awake, offline processing improves memory for associate pairs. Moreover, multi-voxel pattern analysis of the neuroimaging data suggested reactivation of encoded memory representations in dorsolateral prefrontal cortex during offline processing. These results signify the first demonstration of awake, active, offline enhancement of associative memory and suggest that such enhancement is accompanied by the offline reactivation of encoded memory representations. PMID:27119345
Statistical-Mechanical Analysis of Pre-training and Fine Tuning in Deep Learning
NASA Astrophysics Data System (ADS)
Ohzeki, Masayuki
2015-03-01
In this paper, we present a statistical-mechanical analysis of deep learning. We elucidate some of the essential components of deep learning — pre-training by unsupervised learning and fine tuning by supervised learning. We formulate the extraction of features from the training data as a margin criterion in a high-dimensional feature-vector space. The self-organized classifier is then supplied with small amounts of labelled data, as in deep learning. Although we employ a simple single-layer perceptron model, rather than directly analyzing a multi-layer neural network, we find a nontrivial phase transition that is dependent on the number of unlabelled data in the generalization error of the resultant classifier. In this sense, we evaluate the efficacy of the unsupervised learning component of deep learning. The analysis is performed by the replica method, which is a sophisticated tool in statistical mechanics. We validate our result in the manner of deep learning, using a simple iterative algorithm to learn the weight vector on the basis of belief propagation.
Statistical Analysis of CMC Constituent and Processing Data
NASA Technical Reports Server (NTRS)
Fornuff, Jonathan
2004-01-01
observed using statistical analysis software. The ultimate purpose of this study is to determine what variations in material processing can lead to the most critical changes in the materials property. The work I have taken part in this summer explores, in general, the key properties needed In this study SiC/SiC composites of varying architectures, utilizing a boron-nitride (BN)
Using DNS and Statistical Learning to Model Bubbly Channel Flow
NASA Astrophysics Data System (ADS)
Ma, Ming; Lu, Jiacai; Tryggvason, Gretar
2015-11-01
The transient evolution of laminar bubbly flow in a vertical channel is examined by direct numerical simulation (DNS). Nearly spherical bubbles, initially distributed evenly in a fully developed parabolic flow, are driven relatively quickly to the walls, where they increase the drag and reduce the flow rate on a longer time scale. Once the flow rate has been decreased significantly, some of the bubbles move back into the channel interior and the void fraction there approaches the value needed to balance the weight of the mixture and the imposed pressure gradient. A database generated by averaging the DNS results is used to model the closure terms in a simple model of the average flow. Those terms relate the averaged lateral flux of the bubbles, the velocity fluctuations and the averaged surface tension force to the fluid shear, the void fraction and its gradient, as well as the distance to the nearest wall. An aggregated neural network is used for the statistically leaning of unknown closures, and closure relationships are tested by following the evolution of bubbly channel flow with different initial conditions. It is found that the model predictions are in reasonably good agreement with DNS results. Supported by NSF.
ERIC Educational Resources Information Center
Wilhelm, Pascal; Beishuizen, Jos J.
2004-01-01
Asking learners standardized questions during performance of a self-directed inductive learning task might be a useful way to complement think aloud protocol data. However, asking questions might also scaffold the learning process and thus influence the exact processes one wants to study. In the study described in this paper two groups of learners…
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…
ERIC Educational Resources Information Center
Liu, T.-C.; Lin, Y.-C.; Kinshuk
2010-01-01
Simulation-based computer assisted learning (CAL) is recommended to help students understand important statistical concepts, although the current systems are still far from ideal. Simulation-Assisted Learning Statistics (SALS) is a simulation-based CAL that is developed with a learning model that is based on cognitive conflict theory to correct…
An entropic measure for the teaching-learning process
NASA Astrophysics Data System (ADS)
Singh, Vijay A.; Pathak, Praveen; Pandey, Pratyush
2009-10-01
Few phenomena are as complex as the teaching-learning (TL) process. The instruction efficiency and information on the state of knowledge of the student group are some key variables in this process. Guided by Shannon’s definition of information we propose an entropy based performance index ( Sp) for monitoring the teaching-learning process. Our index is based on item response curves (IRCs) which have been recently employed in physics education research. Our proposed index is an explicit function of the ability θ. A preliminary survey indicates that Sp is low (high) for high (low) ability student groups. We propose a simple model to explain this. We have also carried out a number of controlled studies to study the dependence of Sp on student ability, peer instruction and collaborative learning. Our studies indicate that Sp plays a role analogous to entropy in statistical mechanics, with student ability being akin to inverse temperature, peer instruction to an ordering (magnetic) field and collaborative learning to interacting components.
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
Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods
ERIC Educational Resources Information Center
Soroush, Masoud; Weinberger, Charles B.
2010-01-01
This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…
Statistical Signal Processing Methods in Scattering and Imaging
NASA Astrophysics Data System (ADS)
Zambrano Nunez, Maytee
of projective measurements of the field. The projective measurements are implemented using spatial light modulators of the digital micromirror device (DMD) family, followed by a geometrical-optics-based image casting system to capture the data using a single photodetector. The reconstruction process is based on the new field of compressive sensing which allows, thanks to the exploitation of statistical priors such as sparsity, the imaging of the main features of the objects under illumination with much less data than a typical CCD camera. The present system expands the scope of single-detector imaging systems based on compressive sensing from the incoherent light regime, which has been the past focus, to the coherent light regime which is key in many biomedical and Homeland security applications (THz imaging).
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…
Connectionist perspectives on language learning, representation and processing.
Joanisse, Marc F; McClelland, James L
2015-01-01
The field of formal linguistics was founded on the premise that language is mentally represented as a deterministic symbolic grammar. While this approach has captured many important characteristics of the world's languages, it has also led to a tendency to focus theoretical questions on the correct formalization of grammatical rules while also de-emphasizing the role of learning and statistics in language development and processing. In this review we present a different approach to language research that has emerged from the parallel distributed processing or 'connectionist' enterprise. In the connectionist framework, mental operations are studied by simulating learning and processing within networks of artificial neurons. With that in mind, we discuss recent progress in connectionist models of auditory word recognition, reading, morphology, and syntactic processing. We argue that connectionist models can capture many important characteristics of how language is learned, represented, and processed, as well as providing new insights about the source of these behavioral patterns. Just as importantly, the networks naturally capture irregular (non-rule-like) patterns that are common within languages, something that has been difficult to reconcile with rule-based accounts of language without positing separate mechanisms for rules and exceptions.
Equations of States in Statistical Learning for an Unrealizable and Regular Case
NASA Astrophysics Data System (ADS)
Watanabe, Sumio
Many learning machines that have hierarchical structure or hidden variables are now being used in information science, artificial intelligence, and bioinformatics. However, several learning machines used in such fields are not regular but singular statistical models, hence their generalization performance is still left unknown. To overcome these problems, in the previous papers, we proved new equations in statistical learning, by which we can estimate the Bayes generalization loss from the Bayes training loss and the functional variance, on the condition that the true distribution is a singularity contained in a learning machine. In this paper, we prove that the same equations hold even if a true distribution is not contained in a parametric model. Also we prove that, the proposed equations in a regular case are asymptotically equivalent to the Takeuchi information criterion. Therefore, the proposed equations are always applicable without any condition on the unknown true distribution.
ERIC Educational Resources Information Center
Livingstone, D. W.
A study provided extensive statistics and documentation of Canadian adults' work and learning activities. The study included statistics for household labor and community volunteer activities as well as paid employment. Learning activities included both formal course work and informal learning, as well as on-the-job training. Data sources were the…
Investigations of learning processes in infants.
Mayes, L C
1989-12-01
This brief review has covered three major areas: what kinds of information can infants encode in the first 6 to 9 months and what methods are available to measure their responses; the status of memory functions in infancy; and current models of synaptogenesis, a process likely underlying the emerging cognitive processes during infancy. We can conclude that infants are capable of learning even from the first hours after birth and show a rapid increase in their capacity to recall information and use it for further learning. Furthermore, neurobiologic models of development are beginning to define a more detailed picture of crucial phases in the first months for learning processes. There are four notable omissions in the above points and in the material covered in this review. Mentioning these in this summary serves to caution the reader that there is much more to consider, and to point to other active areas of investigation of infants' cognitive capacities. These points also serve as a partial response to the questions asked in the beginning of the paper about how far we have yet to go in our understanding of infants' learning and about what may be the observational blind spots for our generation of infancy investigators. First, we have not discussed what drives learning--why infants seek actively to acquire information and seem motivated to retain that information for later use? Lipsitt points out that much of early infant behavior is based on positive and negative responses to diverse stimuli of varying degrees of attractiveness, and that the "hedonic overtones," or the amount of pleasure, involved in a stimulus situation, certainly contribute to the infant's response. Until recently, studies of social and affective development have been essentially separate from studies of cognition. Currently, the field of "social cognition" symbolizes the link between studies of basic cognitive processes and social development.(ABSTRACT TRUNCATED AT 400 WORDS)
Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning
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
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.
Retrieval dynamics and retention in cross-situational statistical word learning.
Vlach, Haley A; Sandhofer, Catherine M
2014-01-01
Previous research on cross-situational word learning has demonstrated that learners are able to reduce ambiguity in mapping words to referents by tracking co-occurrence probabilities across learning events. In the current experiments, we examined whether learners are able to retain mappings over time. The results revealed that learners are able to retain mappings for up to 1 week later. However, there were interactions between the amount of retention and the different learning conditions. Interestingly, the strongest retention was associated with a learning condition that engendered retrieval dynamics that initially challenged the learner but eventually led to more successful retrieval toward the end of learning. The ease/difficulty of retrieval is a critical process underlying cross-situational word learning and is a powerful example of how learning dynamics affect long-term learning outcomes.
Learning processes underlying avoidance of negative outcomes.
Andreatta, Marta; Michelmann, Sebastian; Pauli, Paul; Hewig, Johannes
2017-04-01
Successful avoidance of a threatening event may negatively reinforce the behavior due to activation of brain structures involved in reward processing. Here, we further investigated the learning-related properties of avoidance using feedback-related negativity (FRN). The FRN is modulated by violations of an intended outcome (prediction error, PE), that is, the bigger the difference between intended and actual outcome, the larger the FRN amplitude is. Twenty-eight participants underwent an operant conditioning paradigm, in which a behavior (button press) allowed them to avoid a painful electric shock. During two learning blocks, participants could avoid an electric shock in 80% of the trials by pressing one button (avoidance button), or by not pressing another button (punishment button). After learning, participants underwent two test blocks, which were identical to the learning ones except that no shocks were delivered. Participants pressed the avoidance button more often than the punishment button. Importantly, response frequency increased throughout the learning blocks but it did not decrease during the test blocks, indicating impaired extinction and/or habit formation. In line with a PE account, FRN amplitude to negative feedback after correct responses (i.e., unexpected punishment) was significantly larger than to positive feedback (i.e., expected omission of punishment), and it increased throughout the blocks. Highly anxious individuals showed equal FRN amplitudes to negative and positive feedback, suggesting impaired discrimination. These results confirm the role of negative reinforcement in motivating behavior and learning, and reveal important differences between high and low anxious individuals in the processing of prediction errors.
A Discussion of the Statistical Investigation Process in the Australian Curriculum
ERIC Educational Resources Information Center
McQuade, Vivienne
2013-01-01
Statistics and statistical literacy can be found in the Learning Areas of Mathematics, Geography, Science, History and the upcoming Business and Economics, as well as in the General Capability of Numeracy and all three Crosscurriculum priorities. The Australian Curriculum affords many exciting and varied entry points for the teaching of…
Methods of learning in statistical education: Design and analysis of a randomized trial
NASA Astrophysics Data System (ADS)
Boyd, Felicity Turner
Background. Recent psychological and technological advances suggest that active learning may enhance understanding and retention of statistical principles. A randomized trial was designed to evaluate the addition of innovative instructional methods within didactic biostatistics courses for public health professionals. Aims. The primary objectives were to evaluate and compare the addition of two active learning methods (cooperative and internet) on students' performance; assess their impact on performance after adjusting for differences in students' learning style; and examine the influence of learning style on trial participation. Methods. Consenting students enrolled in a graduate introductory biostatistics course were randomized to cooperative learning, internet learning, or control after completing a pretest survey. The cooperative learning group participated in eight small group active learning sessions on key statistical concepts, while the internet learning group accessed interactive mini-applications on the same concepts. Controls received no intervention. Students completed evaluations after each session and a post-test survey. Study outcome was performance quantified by examination scores. Intervention effects were analyzed by generalized linear models using intent-to-treat analysis and marginal structural models accounting for reported participation. Results. Of 376 enrolled students, 265 (70%) consented to randomization; 69, 100, and 96 students were randomized to the cooperative, internet, and control groups, respectively. Intent-to-treat analysis showed no differences between study groups; however, 51% of students in the intervention groups had dropped out after the second session. After accounting for reported participation, expected examination scores were 2.6 points higher (of 100 points) after completing one cooperative learning session (95% CI: 0.3, 4.9) and 2.4 points higher after one internet learning session (95% CI: 0.0, 4.7), versus
Gao, Xinbo; Gao, Fei; Tao, Dacheng; Li, Xuelong
2013-12-01
Universal blind image quality assessment (IQA) metrics that can work for various distortions are of great importance for image processing systems, because neither ground truths are available nor the distortion types are aware all the time in practice. Existing state-of-the-art universal blind IQA algorithms are developed based on natural scene statistics (NSS). Although NSS-based metrics obtained promising performance, they have some limitations: 1) they use either the Gaussian scale mixture model or generalized Gaussian density to predict the nonGaussian marginal distribution of wavelet, Gabor, or discrete cosine transform coefficients. The prediction error makes the extracted features unable to reflect the change in nonGaussianity (NG) accurately. The existing algorithms use the joint statistical model and structural similarity to model the local dependency (LD). Although this LD essentially encodes the information redundancy in natural images, these models do not use information divergence to measure the LD. Although the exponential decay characteristic (EDC) represents the property of natural images that large/small wavelet coefficient magnitudes tend to be persistent across scales, which is highly correlated with image degradations, it has not been applied to the universal blind IQA metrics; and 2) all the universal blind IQA metrics use the same similarity measure for different features for learning the universal blind IQA metrics, though these features have different properties. To address the aforementioned problems, we propose to construct new universal blind quality indicators using all the three types of NSS, i.e., the NG, LD, and EDC, and incorporating the heterogeneous property of multiple kernel learning (MKL). By analyzing how different distortions affect these statistical properties, we present two universal blind quality assessment models, NSS global scheme and NSS two-step scheme. In the proposed metrics: 1) we exploit the NG of natural images
Experience and Sentence Processing: Statistical Learning and Relative Clause Comprehension
ERIC Educational Resources Information Center
Wells, Justine B.; Christiansen, Morten H.; Race, David S.; Acheson, Daniel J.; MacDonald, Maryellen C.
2009-01-01
Many explanations of the difficulties associated with interpreting object relative clauses appeal to the demands that object relatives make on working memory. MacDonald and Christiansen [MacDonald, M. C., & Christiansen, M. H. (2002). "Reassessing working memory: Comment on Just and Carpenter (1992) and Waters and Caplan (1996)." "Psychological…
ERIC Educational Resources Information Center
Tillmann, Barbara; McAdams, Stephen
2004-01-01
The present study investigated the influence of acoustical characteristics on the implicit learning of statistical regularities (transition probabilities) in sequences of musical timbres. The sequences were constructed in such a way that the acoustical dissimilarities between timbres potentially created segmentations that either supported (S1) or…
The Impact of Animation Interactivity on Novices' Learning of Introductory Statistics
ERIC Educational Resources Information Center
Wang, Pei-Yu; Vaughn, Brandon K.; Liu, Min
2011-01-01
This study examined the impact of animation interactivity on novices' learning of introductory statistics. The interactive animation program used in this study was created with Adobe Flash following Mayer's multimedia design principles as well as Kristof and Satran's interactivity theory. This study was guided by three main questions: 1) Is there…
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…
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.…
Parent and Student Perceptions of the Learning Environment at School. Statistics in Brief.
ERIC Educational Resources Information Center
Chandler, Kathryn; And Others
Findings of a study that examined parent and student perceptions of school learning environments are presented in this document. The data are from the 1993 National Household Education Survey (NHES:93), a national survey conducted by Westat for the National Center for Education Statistics. This report is based on data from telephone interviews…
How To Improve Statistical Thinking: Choose the Task Representation Wisely and Learn by Doing.
ERIC Educational Resources Information Center
Sedlmeier, Peter
2000-01-01
Proposes a new training approach to teach effective statistical thinking and describes two studies where people were trained to solve tasks involving conjunctive and conditional probabilities using a frequency grid. Discusses frequency format versus probability format, and describes results that suggest the importance of learning by doing.…
Technology Transfer Automated Retrieval System (TEKTRAN)
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...
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.
Statistical Learning Is Related to Reading Ability in Children and Adults
ERIC Educational Resources Information Center
Arciuli, Joanne; Simpson, Ian C.
2012-01-01
There is little empirical evidence showing a direct link between a capacity for statistical learning (SL) and proficiency with natural language. Moreover, discussion of the role of SL in language acquisition has seldom focused on literacy development. Our study addressed these issues by investigating the relationship between SL and reading ability…
ERIC Educational Resources Information Center
Arciuli, Joanne; Simpson, Ian C.
2011-01-01
It is possible that statistical learning (SL) plays a role in almost every mental activity. Indeed, research on SL has grown rapidly over recent decades in an effort to better understand perception and cognition. Yet, there remain gaps in our understanding of how SL operates, in particular with regard to its (im)mutability. Here, we investigated…
Learning of spatial statistics in nonhuman primates: contextual cueing in baboons (Papio papio).
Goujon, Annabelle; Fagot, Joel
2013-06-15
A growing number of theories of cognition suggest that many of our behaviors result from the ability to implicitly extract and use statistical redundancies present in complex environments. In an attempt to develop an animal model of statistical learning mechanisms in humans, the current study investigated spatial contextual cueing (CC) in nonhuman primates. Twenty-five baboons (Papio papio) were trained to search for a target (T) embedded within configurations of distrators (L) that were either predictive or non-predictive of the target location. Baboons exhibited an early CC effect, which remained intact after a 6-week delay and stable across extensive training of 20,000 trials. These results demonstrate the baboons' ability to learn spatial contingencies, as well as the robustness of CC as a cognitive phenomenon across species. Nevertheless, in both the youngest and oldest baboons, CC required many more trials to emerge than in baboons of intermediate age. As a whole, these results reveal strong similarities between CC in humans and baboons, suggesting similar statistical learning mechanisms in these two species. Therefore, baboons provide a valid model to investigate how statistical learning mechanisms develop and/or age during the life span, as well as how these mechanisms are implemented in neural networks, and how they have evolved throughout the phylogeny.
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…
Students' Learning Strategies: Statistical Types and Their Relationship with Computer Literacy
ERIC Educational Resources Information Center
Saparniene, Diana
2006-01-01
Purpose: The purpose of this article is to identify and describe existing students' statistical types by their learning strategies and to show the connection with factual computer literacy. Methodology: The empirical-experimental part of the present study is based on a series of diagnostic studies of 1004 surveyed Lithuanian students. The article…
Using an online web magazine to motivate the learning of statistics
NASA Astrophysics Data System (ADS)
Din, Nor Anis Hayati Mohd; Ismail, Zaleha; Sulaiman, Norhafizah
2015-02-01
Students have difficulties to appreciate the role of mathematics in their daily life. Failure to understand how mathematics is relevant in life might hinder learning. An online web magazine entitled Dunia Matematik has been published to share mathematics that beyond the classrooms. This paper discusses students' rating of the articles which portray statistics in real life that made available at one section of the web magazine. Students' motivate on were assesed based on four components of ARCS model: Attention, Relevance, Confidence and Satisfaction. The thirty-seven form four students who participated in the study were given one hour to review on some selected articles on statistics that have been published in Dunia Matematik. At the end of the one hour session the participants were required to respond to 20 survey items. Findings showed that the students rated highest for Confidence while Attention was rated lowest. Meanwhile, the overall rating for the articles on Statistics of the online web magazine Dunia Matematik was rated considerably high. The study shows that form four students viewed the articles on applications of statistics published in Dunia Matematik has the potential to motivate the learning of statistics. It is hoped that students take advantage to what is offered by this local web magazine to attain learning and experience beyond classrooms.
ERIC Educational Resources Information Center
Lovett, Marsha; Meyer, Oded; Thille, Candace
2008-01-01
The Open Learning Initiative (OLI) is an open educational resources project at Carnegie Mellon University that began in 2002 with a grant from The William and Flora Hewlett Foundation. OLI creates web-based courses that are designed so that students can learn effectively without an instructor. In addition, the courses are often used by instructors…
CD process control through machine learning
NASA Astrophysics Data System (ADS)
Utzny, Clemens
2016-10-01
For the specific requirements of the 14nm and 20nm site applications a new CD map approach was developed at the AMTC. This approach relies on a well established machine learning technique called recursive partitioning. Recursive partitioning is a powerful technique which creates a decision tree by successively testing whether the quantity of interest can be explained by one of the supplied covariates. The test performed is generally a statistical test with a pre-supplied significance level. Once the test indicates significant association between the variable of interest and a covariate a split performed at a threshold value which minimizes the variation within the newly attained groups. This partitioning is recurred until either no significant association can be detected or the resulting sub group size falls below a pre-supplied level.
Statistical process control of cocrystallization processes: A comparison between OPLS and PLS.
Silva, Ana F T; Sarraguça, Mafalda Cruz; Ribeiro, Paulo R; Santos, Adenilson O; De Beer, Thomas; Lopes, João Almeida
2017-03-30
Orthogonal partial least squares regression (OPLS) is being increasingly adopted as an alternative to partial least squares (PLS) regression due to the better generalization that can be achieved. Particularly in multivariate batch statistical process control (BSPC), the use of OPLS for estimating nominal trajectories is advantageous. In OPLS, the nominal process trajectories are expected to be captured in a single predictive principal component while uncorrelated variations are filtered out to orthogonal principal components. In theory, OPLS will yield a better estimation of the Hotelling's T(2) statistic and corresponding control limits thus lowering the number of false positives and false negatives when assessing the process disturbances. Although OPLS advantages have been demonstrated in the context of regression, its use on BSPC was seldom reported. This study proposes an OPLS-based approach for BSPC of a cocrystallization process between hydrochlorothiazide and p-aminobenzoic acid monitored on-line with near infrared spectroscopy and compares the fault detection performance with the same approach based on PLS. A series of cocrystallization batches with imposed disturbances were used to test the ability to detect abnormal situations by OPLS and PLS-based BSPC methods. Results demonstrated that OPLS was generally superior in terms of sensibility and specificity in most situations. In some abnormal batches, it was found that the imposed disturbances were only detected with OPLS.
Modulation of spatial attention by goals, statistical learning, and monetary reward
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
Modulation of spatial attention by goals, statistical learning, and monetary reward.
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.
Statistics, models and learning in BCM theory of a natural visual environment
NASA Astrophysics Data System (ADS)
Lee, Ann Be-Su
Recently, there has been a great deal of interest in the statistics of natural images from both biological and computational perspectives. From the biological side, it is widely believed that our visual system is adapted to deal efficiently with natural stimuli. Part of this adaptation is experience-dependent and occurs due to some general mechanism in the brain for modifying the synapses of a neuron as a function of the inputs to the neuron. A better understanding of natural scene statistics may thus provide insight into the role of the environment in the development of the nervous system. Two basis properties of natural images are: (i) they are extremely non-Gaussian, with highly kurtotic distributions for almost any mean-0 filter response, and (ii) their statistics seem to be largely invariant to a change of scale or coarse-graining. A detailed study of small patches of natural images shows that the state space of such patches is very sparse and highly structured, with most of the high-contrast data concentrated in low-dimensional manifolds and clusters. An important question in vision is whether we can find stochastic models that capture the typical structures of natural images. We argue that many of the observed characteristics of natural images are, at least partly, due to the world being made up of objects in some generalized sense. We develop a simplified visual environment where an image is formed from a set of elementary shapes, whose locations and scale are sampled from a homogeneous Poisson process. These shapes partially occlude one another as they are laid down in layers. The image model, although very simple, seems to capture much of the low-level statistics of naturally occurring scenes. We finally test whether the BCM theory of synaptic plasticity and inputs from this artificial visual environment can account for the observed response properties of cortical cells in the primary visual cortex. The study is an attempt to better understand what
Statistical and signal-processing concepts in surface metrology
Church, E.L.; Takacs, P.Z.
1986-03-01
This paper proposes the use of a simple two-scale model of surface roughness for testing and specifying the topographic figure and finish of synchrotron-radiation mirrors. In this approach the effects of figure and finish are described in terms of their slope distribution and power spectrum, respectively, which are then combined with the system point spread function to produce a composite image. The result can be used to predict mirror performance or to translate design requirements into manufacturing specifications. Pacing problems in this approach are the development of a practical long-trace slope-profiling instrument and realistic statistical models for figure and finish errors.
Big data analysis using modern statistical and machine learning methods in medicine.
Yoo, Changwon; Ramirez, Luis; Liuzzi, Juan
2014-06-01
In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.
Big Data Analysis Using Modern Statistical and Machine Learning Methods in Medicine
Ramirez, Luis; Liuzzi, Juan
2014-01-01
In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease. PMID:24987556
Statistical process control of a Kalman filter model.
Gamse, Sonja; Nobakht-Ersi, Fereydoun; Sharifi, Mohammad A
2014-09-26
For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF) algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations.
ERIC Educational Resources Information Center
Dietze, Stefan; Gugliotta, Alessio; Domingue, John
2009-01-01
Current E-Learning technologies primarily follow a data and metadata-centric paradigm by providing the learner with composite content containing the learning resources and the learning process description, usually based on specific metadata standards such as ADL SCORM or IMS Learning Design. Due to the design-time binding of learning resources,…
Learning Package by Means of the Inductive Teaching with Group Process
ERIC Educational Resources Information Center
Sawangsri, Benchaporn
2016-01-01
This research focuses on the inductive teaching with group process and students' behavior as working in a group. There were four instruments under this study. The descriptive statistics were employed. The findings revealed that, firstly, the effectiveness of LPIDTGP [Learning Package by means of the Inductive Teaching with Group Process] is higher…
An overview of adult-learning processes.
Russell, Sally S
2006-10-01
Part of being an effective instructor involves understanding how adults learn best. Theories of adult education are based on valuing the prior learning and experience of adults. Adult learners have different learning styles which must be assessed prior to initiating any educational session. Health care providers can maximize teaching moments by incorporating specific adult-learning principles and learning styles into their teaching strategies.
State estimation Kalman filter using optical processings Noise statistics known
NASA Technical Reports Server (NTRS)
Jackson, J.; Casasent, D.
1984-01-01
Reference is made to a study by Casasent et al. (1983), which gave a description of a frequency-multiplexed acoustooptic processor and showed how it was capable of performing all the individual operations required in Kalman filtering. The data flow and organization of all required operations however, were not detailed in that study. Consideration is given here to a simpler Kalman filter state estimation problem. Equally spaced time-sampled intervals (k times T sub s, with k the iterative time index) are assumed. It is further assumed that the system noise vector w and the measurement noise vector v are uncorrelated and Gaussian distributed and that the noise statistics (Q and R) and the system model (Phi, Gamma, H) are known. The error covariance matrix P and the extrapolated error covariance matrix M can thus be precomputed and the Kalman gain matrix K sub k can be precomputed and stored for each input time sample.
Toward digital staining using stimulated Raman scattering and statistical machine learning
NASA Astrophysics Data System (ADS)
Tanji, K.; Otsuka, Y.; Satoh, S.; Hashimoto, H.; Ozeki, Y.; Itoh, Kazuyoshi
2014-03-01
Stimulated Raman scattering (SRS) spectral microscopy is a promising imaging method, based on vibrational spectroscopy, which can visualize biological tissues with chemical specificity. SRS spectral microscopy has been used to obtain two-dimensional spectral images of rat liver tissue, three-dimensional images of a vessel in rat liver, and in vivo spectral images of mouse ear skin. Various multivariate analysis techniques, such as principal component analysis and independent component analysis, have been used to obtain spectral images. In this study, we propose a digital staining method. This method uses SRS spectra and statistical machine learning that makes use of prior knowledge of spectral peaks and their two-dimensional distributional patterns corresponding to the composition of tissue samples. The method selects spectral peaks on the basis of Mahalanobis distance, which is defined as the ratio of inter-group variation to intragroup variation. We also make use of higher-order local autocorrelations as feature values for two-dimensional distributional patterns. This combination of techniques allows groups corresponding to different intracellular structures to be clearly discriminated in the multidimensional feature space. We investigate the performance of our method on mouse liver tissue samples and show that the proposed method can digitally stain each intracellular structure such as cell nuclei, cytoplasm, and erythrocytes separately and clearly without time-consuming chemical staining processes. We anticipate that our method could be applied to computer-aided pathological diagnosis.
Ofoghi, Bahadorreza; Zeleznikow, John; Dwyer, Dan; Macmahon, Clare
2013-01-01
This article describes the utilisation of an unsupervised machine learning technique and statistical approaches (e.g., the Kolmogorov-Smirnov test) that assist cycling experts in the crucial decision-making processes for athlete selection, training, and strategic planning in the track cycling Omnium. The Omnium is a multi-event competition that will be included in the summer Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and intuition. They rarely have access to objective data. We analysed both the old five-event (first raced internationally in 2007) and new six-event (first raced internationally in 2011) Omniums and found that the addition of the elimination race component to the Omnium has, contrary to expectations, not favoured track endurance riders. We analysed the Omnium data and also determined the inter-relationships between different individual events as well as between those events and the final standings of riders. In further analysis, we found that there is no maximum ranking (poorest performance) in each individual event that riders can afford whilst still winning a medal. We also found the required times for riders to finish the timed components that are necessary for medal winning. The results of this study consider the scoring system of the Omnium and inform decision-making toward successful participation in future major Omnium competitions.
Statistical Process Control for Evaluating Contract Service at Army Installations
1990-09-01
Approved I OMB No. 0704-0188 Pubic reporting burden tr this colledion of infornation is estimated to average 1 hour per response, including the time...first, removing special causes, if any exist; second, reducing the variability of the process output under stable conditions. In practice, the first step...properly and removed from the process. When all sporadic causes of variability have been removed from the system, the evaluator can truly assess the level
Bilingualism and inhibitory control influence statistical learning of novel word forms.
Bartolotti, James; Marian, Viorica; Schroeder, Scott R; Shook, Anthony
2011-01-01
We examined the influence of bilingual experience and inhibitory control on the ability to learn a novel language. Using a statistical learning paradigm, participants learned words in two novel languages that were based on the International Morse Code. First, participants listened to a continuous stream of words in a Morse code language to test their ability to segment words from continuous speech. Since Morse code does not overlap in form with natural languages, interference from known languages was minimized. Next, participants listened to another Morse code language composed of new words that conflicted with the first Morse code language. Interference in this second language was high due to conflict between languages and due to the presence of two colliding cues (compressed pauses between words and statistical regularities) that competed to define word boundaries. Results suggest that bilingual experience can improve word learning when interference from other languages is low, while inhibitory control ability can improve word learning when interference from other languages is high. We conclude that the ability to extract novel words from continuous speech is a skill that is affected both by linguistic factors, such as bilingual experience, and by cognitive abilities, such as inhibitory control.
Optimization of Plasma Spray Process Using Statistical Methods
NASA Astrophysics Data System (ADS)
Gao, F.; Huang, X.; Liu, R.; Yang, Q.
2012-01-01
The microstructure features of coatings produced by a plasma spray process are affected significantly by the process parameters such as powder size, spray gun nozzle size, total plasma gas flow, ratio of H2 + N2 over total gas flow, and so on. This article presents a study of the effects of these parameters on the microstructure (porosity, formation of crack, unmelted particle and oxide phase) of NiCrAlY coatings deposited by the Mettech Axial III™ System. A Taguchi array is used to design the spraying process parameters. The results of the microstructure evaluation are used to generate regression equations for the prediction of coating microstructure based on process parameters. The results predicted from the regression equations are in good agreement with the experimental results according to a confidence level of 0.95. Among the parameters examined, the powder size and the ratio of H2 + N2 over total gas flow rate are the most significant parameters affecting the occurrence of crack, porosity, unmelted particle and oxide. Within the range of the designed process parameters, lower powder size and higher ratio of H2 + N2 over total gas flow rate lead to less cracks, pores, unmelted particles but more oxides. Nozzle size has marginal influence on oxides which increase with nozzle size. Gas flow rate has no direct influence on any coating feature evaluated with the range of variation.
Statistical Considerations of Data Processing in Giovanni Online Tool
NASA Technical Reports Server (NTRS)
Suhung, Shen; Leptoukh, G.; Acker, J.; Berrick, S.
2005-01-01
The GES DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni) is a web-based interface for the rapid visualization and analysis of gridded data from a number of remote sensing instruments. The GES DISC currently employs several Giovanni instances to analyze various products, such as Ocean-Giovanni for ocean products from SeaWiFS and MODIS-Aqua; TOMS & OM1 Giovanni for atmospheric chemical trace gases from TOMS and OMI, and MOVAS for aerosols from MODIS, etc. (http://giovanni.gsfc.nasa.gov) Foremost among the Giovanni statistical functions is data averaging. Two aspects of this function are addressed here. The first deals with the accuracy of averaging gridded mapped products vs. averaging from the ungridded Level 2 data. Some mapped products contain mean values only; others contain additional statistics, such as number of pixels (NP) for each grid, standard deviation, etc. Since NP varies spatially and temporally, averaging with or without weighting by NP will be different. In this paper, we address differences of various weighting algorithms for some datasets utilized in Giovanni. The second aspect is related to different averaging methods affecting data quality and interpretation for data with non-normal distribution. The present study demonstrates results of different spatial averaging methods using gridded SeaWiFS Level 3 mapped monthly chlorophyll a data. Spatial averages were calculated using three different methods: arithmetic mean (AVG), geometric mean (GEO), and maximum likelihood estimator (MLE). Biogeochemical data, such as chlorophyll a, are usually considered to have a log-normal distribution. The study determined that differences between methods tend to increase with increasing size of a selected coastal area, with no significant differences in most open oceans. The GEO method consistently produces values lower than AVG and MLE. The AVG method produces values larger than MLE in some cases, but smaller in other cases. Further
Statistical analysis with dilatation for development process of human fetuses.
Naito, Kanta; Notsu, Akifumi; Udagawa, Jun; Otani, Hiroki
2017-02-01
This paper is concerned with the development process of human fetuses. Though the development process of human fetuses still includes many unknown issues, it is known that a certain harmonious relationship between the organs can be observed. This knowledge is based on our intuition, but we have no theory which clarifies these harmonized developments. The paper aims to give a mathematical understanding of the notion of harmonized development through the use of dilatation, which is a measure of the departure from conformal mapping. The asymptotics for dilatation have been developed using certain efficient models of quasiconformal mapping. The proposed method of dilatation is effectively applied to the human fetus data.
NASA Technical Reports Server (NTRS)
Safford, Robert R.; Jackson, Andrew E.; Swart, William W.; Barth, Timothy S.
1994-01-01
Successful ground processing at KSC requires that flight hardware and ground support equipment conform to specifications at tens of thousands of checkpoints. Knowledge of conformance is an essential requirement for launch. That knowledge of conformance at every requisite point does not, however, enable identification of past problems with equipment, or potential problem areas. This paper describes how the introduction of Statistical Process Control and Process Capability Analysis identification procedures into existing shuttle processing procedures can enable identification of potential problem areas and candidates for improvements to increase processing performance measures. Results of a case study describing application of the analysis procedures to Thermal Protection System processing are used to illustrate the benefits of the approaches described in the paper.
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 Process Control Techniques for the Telecommunications Systems Manager
1992-03-01
products that are out of 59 tolerance and bad designs. The third type of defect, mistakes, are remedied by Poka - Yoke methods that are 1 introduced later...based on total production costs plus quality costs. Once production is underway, interventions are determined by their impact on the QLF. F. POKA - YOKE Mistakes...require process improvements called Poka Yoke or mistake proofing. Shiego Shingo developed Poka Yoke methods to incorporate 100% inspection at
Haque, Shafiul; Khan, Saif; Wahid, Mohd; Dar, Sajad A.; Soni, Nipunjot; Mandal, Raju K.; Singh, Vineeta; Tiwari, Dileep; Lohani, Mohtashim; Areeshi, Mohammed Y.; Govender, Thavendran; Kruger, Hendrik G.; Jawed, Arshad
2016-01-01
For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery. RSM predicted an optimum of feed rate of 310.73 mL/h, bead loading of 79.9% (v/v), cell loading OD600 nm of 74, and run time of 29.9 min with a recovery of ~3.2 g/L. ANN-GA predicted a maximum COD recovery of ~3.5 g/L at an optimum feed rate (mL/h): 258.08, bead loading (%, v/v): 80%, cell loading (OD600 nm): 73.99, and run time of 32 min. An overall 3.7-fold increase in productivity is obtained when compared to a batch process. Optimization and comparison of statistical vs. artificial intelligence techniques in continuous bead milling process has been attempted for the very first time in our study. We were able to successfully represent the complex non-linear multivariable dependence of enzyme recovery on bead milling parameters. The quadratic second order response functions are not flexible enough to represent such complex non-linear dependence. ANN being a summation function of multiple layers are capable to represent complex non-linear dependence of variables in this case; enzyme recovery as a function of bead milling parameters. Since GA can even optimize discontinuous functions present study cites a perfect example of using machine learning (ANN) in combination with evolutionary optimization (GA) for representing undefined biological functions which is the case for common industrial processes involving biological moieties. PMID:27920762
Haque, Shafiul; Khan, Saif; Wahid, Mohd; Dar, Sajad A; Soni, Nipunjot; Mandal, Raju K; Singh, Vineeta; Tiwari, Dileep; Lohani, Mohtashim; Areeshi, Mohammed Y; Govender, Thavendran; Kruger, Hendrik G; Jawed, Arshad
2016-01-01
For a commercially viable recombinant intracellular protein production process, efficient cell lysis and protein release is a major bottleneck. The recovery of recombinant protein, cholesterol oxidase (COD) was studied in a continuous bead milling process. A full factorial response surface methodology (RSM) design was employed and compared to artificial neural networks coupled with genetic algorithm (ANN-GA). Significant process variables, cell slurry feed rate (A), bead load (B), cell load (C), and run time (D), were investigated and optimized for maximizing COD recovery. RSM predicted an optimum of feed rate of 310.73 mL/h, bead loading of 79.9% (v/v), cell loading OD600nm of 74, and run time of 29.9 min with a recovery of ~3.2 g/L. ANN-GA predicted a maximum COD recovery of ~3.5 g/L at an optimum feed rate (mL/h): 258.08, bead loading (%, v/v): 80%, cell loading (OD600nm): 73.99, and run time of 32 min. An overall 3.7-fold increase in productivity is obtained when compared to a batch process. Optimization and comparison of statistical vs. artificial intelligence techniques in continuous bead milling process has been attempted for the very first time in our study. We were able to successfully represent the complex non-linear multivariable dependence of enzyme recovery on bead milling parameters. The quadratic second order response functions are not flexible enough to represent such complex non-linear dependence. ANN being a summation function of multiple layers are capable to represent complex non-linear dependence of variables in this case; enzyme recovery as a function of bead milling parameters. Since GA can even optimize discontinuous functions present study cites a perfect example of using machine learning (ANN) in combination with evolutionary optimization (GA) for representing undefined biological functions which is the case for common industrial processes involving biological moieties.
Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models
2015-09-12
free setting. We extensively tested our software on a complex problem of protein alignment. The ridge regression models did not produce a noticeable...AFRL-AFOSR-VA-TR-2015-0278 DERIVATIVE FREE OPTIMIZATION OF COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS Katya Scheinberg...12-09-2015 2. REPORT TYPE Final Performance 3. DATES COVERED (From - To) 15-08-2011 to 14-08-2014 4. TITLE AND SUBTITLE DERIVATIVE FREE OPTIMIZATION OF
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.
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.
Tools for the Improvement of Organizational Learning Processes in Innovation.
ERIC Educational Resources Information Center
de Weerd-Nederhof, Petra C.; Pacitti, Bernice J.; da Silva Gomes, Jorge F.; Pearson, Alan W.
2002-01-01
From case studies of organizational learning in research and development companies, learning tools or mechanisms were identified: job rotation, innovation process planning, and product innovation project review. Organizational learning involved parallel rather than linear processes of information acquisition, distribution, and interpretation and…
Understanding the Learning Process in SMEs
ERIC Educational Resources Information Center
Carr, James; Gannon-Leary, Pat
2007-01-01
A major obstacle to the diffusion of management development learning technologies from Higher Education Institutions to Small and Medium-sized Enterprises (SMEs) is a lack of understanding about how SME learners learn. This article examines the nature of learning in SMEs and considers the incidence of informal support for informal learning.…
Perspectives on Learning: Methodologies for Exploring Learning Processes and Outcomes
ERIC Educational Resources Information Center
Goldman, Susan R.
2014-01-01
The papers in this Special Issue were initially prepared for an EARLI 2013 Symposium that was designed to examine methodologies in use by researchers from two sister communities, Learning and Instruction and Learning Sciences. The four papers reflect a common ground in advances in conceptions of learning since the early days of the "cognitive…
Statistical process control (SPC) for coordinate measurement machines
Escher, R.N.
2000-01-04
The application of process capability analysis, using designed experiments, and gage capability studies as they apply to coordinate measurement machine (CMM) uncertainty analysis and control will be demonstrated. The use of control standards in designed experiments, and the use of range charts and moving range charts to separate measurement error into it's discrete components will be discussed. The method used to monitor and analyze the components of repeatability and reproducibility will be presented with specific emphasis on how to use control charts to determine and monitor CMM performance and capability, and stay within your uncertainty assumptions.
77 FR 46096 - Statistical Process Controls for Blood Establishments; Public Workshop
Federal Register 2010, 2011, 2012, 2013, 2014
2012-08-02
...: Statistical process control is the application of statistical methods to the monitoring, or quality control... monitors manufacturing procedures, validation summaries, and quality control data prior to licensure and... at implementation and then monitor these processes on a regular basis, using quality control...
Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann
2003-01-01
Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.
Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van't
2012-03-15
Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.
Nonlinear Statistical Signal Processing: A Particle Filtering Approach
Candy, J
2007-09-19
A introduction to particle filtering is discussed starting with an overview of Bayesian inference from batch to sequential processors. Once the evolving Bayesian paradigm is established, simulation-based methods using sampling theory and Monte Carlo realizations are discussed. Here the usual limitations of nonlinear approximations and non-gaussian processes prevalent in classical nonlinear processing algorithms (e.g. Kalman filters) are no longer a restriction to perform Bayesian inference. It is shown how the underlying hidden or state variables are easily assimilated into this Bayesian construct. Importance sampling methods are then discussed and shown how they can be extended to sequential solutions implemented using Markovian state-space models as a natural evolution. With this in mind, the idea of a particle filter, which is a discrete representation of a probability distribution, is developed and shown how it can be implemented using sequential importance sampling/resampling methods. Finally, an application is briefly discussed comparing the performance of the particle filter designs with classical nonlinear filter implementations.
Dyscalculia, dyslexia, and medical students' needs for learning and using statistics.
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.
Counting statistics of non-Markovian quantum stochastic processes.
Flindt, Christian; Novotný, Tomás; Braggio, Alessandro; Sassetti, Maura; Jauho, Antti-Pekka
2008-04-18
We derive a general expression for the cumulant generating function (CGF) of non-Markovian quantum stochastic transport processes. The long-time limit of the CGF is determined by a single dominating pole of the resolvent of the memory kernel from which we extract the zero-frequency cumulants of the current using a recursive scheme. The finite-frequency noise is expressed not only in terms of the resolvent, but also initial system-environment correlations. As an illustrative example we consider electron transport through a dissipative double quantum dot for which we study the effects of dissipation on the zero-frequency cumulants of high orders and the finite-frequency noise.
Wurtz, R.; Kaplan, A.
2015-10-28
Pulse shape discrimination (PSD) is a variety of statistical classifier. Fully-realized statistical classifiers rely on a comprehensive set of tools for designing, building, and implementing. PSD advances rely on improvements to the implemented algorithm. PSD advances can be improved by using conventional statistical classifier or machine learning methods. This paper provides the reader with a glossary of classifier-building elements and their functions in a fully-designed and operational classifier framework that can be used to discover opportunities for improving PSD classifier projects. This paper recommends reporting the PSD classifier’s receiver operating characteristic (ROC) curve and its behavior at a gamma rejection rate (GRR) relevant for realistic applications.
Advanced statistical process control: controlling sub-0.18-μm lithography and other processes
NASA Astrophysics Data System (ADS)
Zeidler, Amit; Veenstra, Klaas-Jelle; Zavecz, Terrence E.
2001-08-01
access of the analysis to include the external variables involved in CMP, deposition etc. We then applied yield analysis methods to identify the significant lithography-external process variables from the history of lots, subsequently adding the identified process variable to the signatures database and to the PPC calculations. With these improvements, the authors anticipate a 50% improvement of the process window. This improvement results in a significant reduction of rework and improved yield depending on process demands and equipment configuration. A statistical theory that explains the PPC is then presented. This theory can be used to simulate a general PPC application. In conclusion, the PPC concept is not lithography or semiconductors limited. In fact it is applicable for any production process that is signature biased (chemical industry, car industry, .). Requirements for the PPC are large data collection, a controllable process that is not too expensive to tune the process for every lot, and the ability to employ feedback calculations. PPC is a major change in the process management approach and therefor will first be employed where the need is high and the return on investment is very fast. The best industry to start with is the semiconductors and the most likely process area to start with is lithography.
Automatic document processing system with learning capability
NASA Astrophysics Data System (ADS)
Li, Xuhong; Ng, Peter A.
2000-08-01
This automatic document processing system proceeds from scanning a given paper-document into the system, automatic recognizing the document layout structure, classifying it as a particular document type, which is characterized in terms of attributes to form a frame template, and extracting the pertinent information from the document to form its corresponding frame instance, which is an effective digital form of the original document. The key attribute of the system is that it is a general-purpose system, which can be adapted easily to any application domains. A segmentation method based on the 'logical closeness' is proposed. A novel and natural representation of document layout structure -- Labeled Directed Weighted Graph (LDWG) and a methodology of transforming document segmentation into LDWG representation are described. To classify a given document, we compare its layout structure with the sample layout structures of various document types prestored in the knowledge base and then use logical structure to verify the initial matching from the first step. There is a weight associated with each component of the layout structure. During the learning stage, the system can adjust the weights automatically based on the human being's correction. Modified Perceptron Learning Algorithm (PLA) is applied.
ERIC Educational Resources Information Center
Erdem, Mukaddes
2009-01-01
The study described looks at the effects of learning style profile of teams on the quality of materials developed in a collaborative learning process. The study was carried out on collaborative teams of four or five university students, formed through learner preferences. Learning styles of the teams were determined using Kolb's Learning Styles…
Counterfeit Electronics Detection Using Image Processing and Machine Learning
NASA Astrophysics Data System (ADS)
Asadizanjani, Navid; Tehranipoor, Mark; Forte, Domenic
2017-01-01
Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.
Visual statistical learning is not reliably modulated by selective attention to isolated events
Musz, Elizabeth; Weber, Matthew J.; Thompson-Schill, Sharon L.
2014-01-01
Recent studies of visual statistical learning (VSL) indicate that the visual system can automatically extract temporal and spatial relationships between objects. We report several attempts to replicate and extend earlier work (Turk-Browne et al., 2005) in which observers performed a cover task on one of two interleaved stimulus sets, resulting in learning of temporal relationships that occur in the attended stream, but not those present in the unattended stream. Across four experiments, we exposed observers to a similar or identical familiarization protocol, directing attention to one of two interleaved stimulus sets; afterward, we assessed VSL efficacy for both sets using either implicit response-time measures or explicit familiarity judgments. In line with prior work, we observe learning for the attended stimulus set. However, unlike previous reports, we also observe learning for the unattended stimulus set. When instructed to selectively attend to only one of the stimulus sets and ignore the other set, observers could extract temporal regularities for both sets. Our efforts to experimentally decrease this effect by changing the cover task (Experiment 1) or the complexity of the statistical regularities (Experiment 3) were unsuccessful. A fourth experiment using a different assessment of learning likewise failed to show an attentional effect. Simulations drawing random samples our first three experiments (n=64) confirm that the distribution of attentional effects in our sample closely approximates the null. We offer several potential explanations for our failure to replicate earlier findings, and discuss how our results suggest limiting conditions on the relevance of attention to VSL. PMID:25172196
The Web as an educational tool for/in learning/teaching bioinformatics statistics.
Oliver, J; Pisano, M E; Alonso, T; Roca, P
2005-12-01
Statistics provides essential tool in Bioinformatics to interpret the results of a database search or for the management of enormous amounts of information provided from genomics, proteomics and metabolomics. The goal of this project was the development of a software tool that would be as simple as possible to demonstrate the use of the Bioinformatics statistics. Computer Simulation Methods (CSMs) developed using Microsoft Excel were chosen for their broad range of applications, immediate and easy formula calculation, immediate testing and easy graphics representation, and of general use and acceptance by the scientific community. The result of these endeavours is a set of utilities which can be accessed from the following URL: http://gmein.uib.es/bioinformatica/statistics. When tested on students with previous coursework with traditional statistical teaching methods, the general opinion/overall consensus was that Web-based instruction had numerous advantages, but traditional methods with manual calculations were also needed for their theory and practice. Once having mastered the basic statistical formulas, Excel spreadsheets and graphics were shown to be very useful for trying many parameters in a rapid fashion without having to perform tedious calculations. CSMs will be of great importance for the formation of the students and professionals in the field of bioinformatics, and for upcoming applications of self-learning and continuous formation.
NASA Astrophysics Data System (ADS)
Abdullah, Rusli; Samah, Bahaman Abu; Bolong, Jusang; D'Silva, Jeffrey Lawrence; Shaffril, Hayrol Azril Mohamed
2014-09-01
Today, teaching and learning (T&L) using technology as tool is becoming more important especially in the field of statistics as a part of the subject matter in higher education system environment. Eventhough, there are many types of technology of statistical learnig tool (SLT) which can be used to support and enhance T&L environment, however, there is lack of a common standard knowledge management as a knowledge portal for guidance especially in relation to infrastructure requirement of SLT in servicing the community of user (CoU) such as educators, students and other parties who are interested in performing this technology as a tool for their T&L. Therefore, there is a need of a common standard infrastructure requirement of knowledge portal in helping CoU for managing of statistical knowledge in acquiring, storing, desseminating and applying of the statistical knowedge for their specific purposes. Futhermore, by having this infrastructure requirement of knowledge portal model of SLT as a guidance in promoting knowledge of best practise among the CoU, it can also enhance the quality and productivity of their work towards excellence of statistical knowledge application in education system environment.
ERIC Educational Resources Information Center
Liou, Hsien-Chin; Chang, Jason S; Chen, Hao-Jan; Lin, Chih-Cheng; Liaw, Meei-Ling; Gao, Zhao-Ming; Jang, Jyh-Shing Roger; Yeh, Yuli; Chuang, Thomas C.; You, Geeng-Neng
2006-01-01
This paper describes the development of an innovative web-based environment for English language learning with advanced data-driven and statistical approaches. The project uses various corpora, including a Chinese-English parallel corpus ("Sinorama") and various natural language processing (NLP) tools to construct effective English…
Learning at a distance II. Statistical learning of non-adjacent dependencies in a non-human primate.
Newport, Elissa L; Hauser, Marc D; Spaepen, Geertrui; Aslin, Richard N
2004-09-01
In earlier work we have shown that adults, infants, and cotton-top tamarin monkeys are capable of computing the probability with which syllables occur in particular orders in rapidly presented streams of human speech, and of using these probabilities to group adjacent syllables into word-like units. We have also investigated adults' learning of regularities among elements that are not adjacent, and have found strong selectivities in their ability to learn various kinds of non-adjacent regularities. In the present paper we investigate the learning of these same non-adjacent regularities in tamarin monkeys, using the same materials and familiarization methods. Three types of languages were constructed. In one, words were formed by statistical regularities between non-adjacent syllables. Words contained predictable relations between syllables 1 and 3; syllable 2 varied. In a second type of language, words were formed by statistical regularities between non-adjacent segments. Words contained predictable relations between consonants; the vowels varied. In a third type of language, also formed by regularities between non-adjacent segments, words contained predictable relations between vowels; the consonants varied. Tamarin monkeys were exposed to these languages in the same fashion as adults (21 min of exposure to a continuous speech stream) and were then tested in a playback paradigm measuring spontaneous looking (no reinforcement). Adult subjects learned the second and third types of language easily, but failed to learn the first. However, tamarin monkeys showed a different pattern, learning the first and third type of languages but not the second. These differences held up over multiple replications, using different sounds instantiating each of the patterns. These results suggest differences among learners in the elementary units perceived in speech (syllables, consonants, and vowels) and/or the distance over which such units can be related, and therefore differences
Science Integrating Learning Objectives: A Cooperative Learning Group Process
ERIC Educational Resources Information Center
Spindler, Matt
2015-01-01
The integration of agricultural and science curricular content that capitalizes on natural and inherent connections represents a challenge for secondary agricultural educators. The purpose of this case study was to create information about the employment of Cooperative Learning Groups (CLG) to enhance the science integrating learning objectives…
Simulation-Based Learning: The Learning-Forgetting-Relearning Process and Impact of Learning History
ERIC Educational Resources Information Center
Davidovitch, Lior; Parush, Avi; Shtub, Avy
2008-01-01
The results of empirical experiments evaluating the effectiveness and efficiency of the learning-forgetting-relearning process in a dynamic project management simulation environment are reported. Sixty-six graduate engineering students performed repetitive simulation-runs with a break period of several weeks between the runs. The students used a…
We're Born To Learn: Using the Brain's Natural Learning Process To Create Today's Curriculum.
ERIC Educational Resources Information Center
Smilkstein, Rita
This book provides research-based, concrete strategies for creating a student-centered curriculum in which every student can learn. It breaks down the Natural Human Learning Process (NHLP) into six stages, providing guidelines and models showing educators how to create learning experiences at each stage of the process for individuals, small…
Perceptual Processing Development: Its Relation to Learning Disabilities. Section I.
ERIC Educational Resources Information Center
Wepman, Joseph M.
Presented is a developmental concept of perceptual processing as related to learning disabilities in young children. Learning is seen to involve the interaction of cognitive developmental stages at the preverbal, verbal, and postverbal levels with learning disabilities seen to be due to perceptual handicaps. A model is offered which posits a…
Implementation of Process Oriented Guided Inquiry Learning (POGIL) in Engineering
ERIC Educational Resources Information Center
Douglas, Elliot P.; Chiu, Chu-Chuan
2013-01-01
This paper describes implementation and testing of an active learning, team-based pedagogical approach to instruction in engineering. This pedagogy has been termed Process Oriented Guided Inquiry Learning (POGIL), and is based upon the learning cycle model. Rather than sitting in traditional lectures, students work in teams to complete worksheets…
Game Inspired Tool Support for e-Learning Processes
ERIC Educational Resources Information Center
Charles, Marie-Therese; Bustard, David; Black, Michaela
2009-01-01
Student engagement is crucial to the success of e-learning but is often difficult to achieve in practice. One significant factor is the quality of the learning content; also important, however, is the suitability of the process through which that material is studied. In recent years much research has been devoted to improving e-learning content…
NASA Astrophysics Data System (ADS)
Conde, Miguel Ángel; García-Peñalvo, Francisco José; Casany, Marià José; Alier Forment, Marc
Learning processes are changing related to technological and sociological evolution, taking this in to account, a new learning strategy must be considered. Specifically what is needed is to give an effective step towards the eLearning 2.0 environments consolidation. This must imply the fusion of the advantages of the traditional LMS (Learning Management System) - more formative program control and planning oriented - with the social learning and the flexibility of the web 2.0 educative applications.
Statistical Process Control Charts for Measuring and Monitoring Temporal Consistency of Ratings
ERIC Educational Resources Information Center
Omar, M. Hafidz
2010-01-01
Methods of statistical process control were briefly investigated in the field of educational measurement as early as 1999. However, only the use of a cumulative sum chart was explored. In this article other methods of statistical quality control are introduced and explored. In particular, methods in the form of Shewhart mean and standard deviation…
The learning process of capita selecta based on journals review
NASA Astrophysics Data System (ADS)
Diniaty, Artina; Febriana, Beta Wulan; Arlianty, Widinda Normalia
2017-03-01
The learning process on capita selecta subject of Chemistry Education Department, Islamic University of Indonesia, was carried out based on reviewing of journals in chemistry and chemistry education scopes. The learning process procedure included planning, implementation and reflection. The purposes of learning were 1) students got an insight into the trend research in chemistry and chemistry education scopes, 2) students knew how to access and search journals, 3) increased students learning motivation on reading scientific journals, 4) students had be trained for reviewing scientific journals, and inspiring students to think about research ideas, performed research and publishing in scientific journals. The result showed that the students' responses in this learning were good.
NASA Astrophysics Data System (ADS)
Zeng, Bobo; Wang, Guijin; Ruan, Zhiwei; Lin, Xinggang; Meng, Long
2012-07-01
High-performance pedestrian detection with good accuracy and fast speed is an important yet challenging task in computer vision. We design a novel feature named pair normalized channel feature (PNCF), which simultaneously combines and normalizes two channel features in image channels, achieving a highly discriminative power and computational efficiency. PNCF applies to both gradient channels and color channels so that shape and appearance information are described and integrated in the same feature. To efficiently explore the formidably large PNCF feature space, we propose a statistics-based feature learning method to select a small number of potentially discriminative candidate features, which are fed into the boosting algorithm. In addition, channel compression and a hybrid pyramid are employed to speed up the multiscale detection. Experiments illustrate the effectiveness of PNCF and its learning method. Our proposed detector outperforms the state-of-the-art on several benchmark datasets in both detection accuracy and efficiency.
Infants rapidly learn word-referent mappings via cross-situational statistics
Smith, Linda; Yu, Chen
2008-01-01
First word learning should be difficult because any pairing of a word and scene presents the learner with an infinite number of possible referents. Accordingly, theorists of children’s rapid word learning have sought constraints on word-referent mappings. These constraints are thought to work by enabling learners to resolve the ambiguity inherent in any labeled scene to determine the speaker’s intended referent at that moment. The present study shows that 12- and 14-month old infants can resolve the uncertainty problem in another way, not by unambiguously deciding the referent in a single word-scene pairing, but by rapidly evaluating the statistical evidence across many individually ambiguous words and scenes. PMID:17692305
Visual statistical learning is related to natural language ability in adults: An ERP study.
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.
Kim, Jihoon; Grillo, Janice M; Ohno-Machado, Lucila
2011-01-01
Objective To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs. Methods From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures. Results The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM. Limitations The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction. Conclusion The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs. PMID:21672912
Marked point processes for statistical and morphological analysis of astronomical data
NASA Astrophysics Data System (ADS)
Stoica, R. S.
2010-09-01
This chapter presents fundamental notions concerning stochastic modelling, simulation and statistical inference based on marked point processes. The application aimed by this presentation is pattern detection in spatial astronomical data.
Mitra, Jhimli; Shen, Kai-kai; Ghose, Soumya; Bourgeat, Pierrick; Fripp, Jurgen; Salvado, Olivier; Pannek, Kerstin; Taylor, D Jamie; Mathias, Jane L; Rose, Stephen
2016-04-01
Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which probe the connectivity of neural networks, show significant promise. We present a machine learning approach to classify TBI participants primarily with mild traumatic brain injury (mTBI) based on altered structural connectivity patterns derived through the network based statistical analysis of structural connectomes generated from TBI and age-matched control groups. In this approach, higher order diffusion models were used to map white matter connections between 116 cortical and subcortical regions. Tracts between these regions were generated using probabilistic tracking and mean fractional anisotropy (FA) measures along these connections were encoded in the connectivity matrices. Network-based statistical analysis of the connectivity matrices was performed to identify the network differences between a representative subset of the two groups. The affected network connections provided the feature vectors for principal component analysis and subsequent classification by random forest. The validity of the approach was tested using data acquired from a total of 179 TBI patients and 146 controls participants. The analysis revealed altered connectivity within a number of intra- and inter-hemispheric white matter pathways associated with DAI, in consensus with existing literature. A mean classification accuracy of 68.16%±1.81% and mean sensitivity of 80.0%±2.36% were achieved in correctly classifying the TBI patients evaluated on the subset of the participants that was not used for the statistical analysis, in a 10-fold cross-validation framework. These results highlight the potential for statistical machine learning approaches applied to structural connectomes to identify patients with diffusive axonal injury.
OPTIMAL COMPUTATIONAL AND STATISTICAL RATES OF CONVERGENCE FOR SPARSE NONCONVEX LEARNING PROBLEMS.
Wang, Zhaoran; Liu, Han; Zhang, Tong
2014-01-01
We provide theoretical analysis of the statistical and computational properties of penalized M-estimators that can be formulated as the solution to a possibly nonconvex optimization problem. Many important estimators fall in this category, including least squares regression with nonconvex regularization, generalized linear models with nonconvex regularization and sparse elliptical random design regression. For these problems, it is intractable to calculate the global solution due to the nonconvex formulation. In this paper, we propose an approximate regularization path-following method for solving a variety of learning problems with nonconvex objective functions. Under a unified analytic framework, we simultaneously provide explicit statistical and computational rates of convergence for any local solution attained by the algorithm. Computationally, our algorithm attains a global geometric rate of convergence for calculating the full regularization path, which is optimal among all first-order algorithms. Unlike most existing methods that only attain geometric rates of convergence for one single regularization parameter, our algorithm calculates the full regularization path with the same iteration complexity. In particular, we provide a refined iteration complexity bound to sharply characterize the performance of each stage along the regularization path. Statistically, we provide sharp sample complexity analysis for all the approximate local solutions along the regularization path. In particular, our analysis improves upon existing results by providing a more refined sample complexity bound as well as an exact support recovery result for the final estimator. These results show that the final estimator attains an oracle statistical property due to the usage of nonconvex penalty.
OPTIMAL COMPUTATIONAL AND STATISTICAL RATES OF CONVERGENCE FOR SPARSE NONCONVEX LEARNING PROBLEMS
Wang, Zhaoran; Liu, Han; Zhang, Tong
2014-01-01
We provide theoretical analysis of the statistical and computational properties of penalized M-estimators that can be formulated as the solution to a possibly nonconvex optimization problem. Many important estimators fall in this category, including least squares regression with nonconvex regularization, generalized linear models with nonconvex regularization and sparse elliptical random design regression. For these problems, it is intractable to calculate the global solution due to the nonconvex formulation. In this paper, we propose an approximate regularization path-following method for solving a variety of learning problems with nonconvex objective functions. Under a unified analytic framework, we simultaneously provide explicit statistical and computational rates of convergence for any local solution attained by the algorithm. Computationally, our algorithm attains a global geometric rate of convergence for calculating the full regularization path, which is optimal among all first-order algorithms. Unlike most existing methods that only attain geometric rates of convergence for one single regularization parameter, our algorithm calculates the full regularization path with the same iteration complexity. In particular, we provide a refined iteration complexity bound to sharply characterize the performance of each stage along the regularization path. Statistically, we provide sharp sample complexity analysis for all the approximate local solutions along the regularization path. In particular, our analysis improves upon existing results by providing a more refined sample complexity bound as well as an exact support recovery result for the final estimator. These results show that the final estimator attains an oracle statistical property due to the usage of nonconvex penalty. PMID:25544785
Statistics to the Rescue!: Using Data to Evaluate a Manufacturing Process
ERIC Educational Resources Information Center
Keithley, Michael G.
2009-01-01
The use of statistics and process controls is too often overlooked in educating students. This article describes an activity appropriate for high school students who have a background in material processing. It gives them a chance to advance their knowledge by determining whether or not a manufacturing process works well. The activity follows a…
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
ERIC Educational Resources Information Center
Liu, Tzu-Chien
2010-01-01
Understanding and applying statistical concepts is essential in modern life. However, common statistical misconceptions limit the ability of students to understand statistical concepts. Although simulation-based computer assisted learning (CAL) is promising for use in students learning statistics, substantial improvement is still needed. For…
ERIC Educational Resources Information Center
Endress, Ansgar D.; Mehler, Jacques
2009-01-01
Word-segmentation, that is, the extraction of words from fluent speech, is one of the first problems language learners have to master. It is generally believed that statistical processes, in particular those tracking "transitional probabilities" (TPs), are important to word-segmentation. However, there is evidence that word forms are stored in…
LANGUAGE LEARNING, THE INDIVIDUAL AND THE PROCESS.
ERIC Educational Resources Information Center
NAJAM, EDWARD W.
THE PROCEEDINGS OF THE INDIANA-PURDUE FOREIGN LANGUAGE CONFERENCE ON LANGUAGE LEARNING ARE DIVIDED INTO THREE GENERAL CATEGORIES AND INTRODUCED BY DIEKHOFF'S SPEECH ADVOCATING TEACHER PARTICIPATION IN THE REVISION OF PROGRAM POLICY TO MEET CONTINUOUS SOCIAL CHANGE. IN THE FIRST SECTION, THE INTERRELATION OF PSYCHOLOGY AND LANGUAGE LEARNING, ARE…
Learning Process and Vocational Experience Attainments.
ERIC Educational Resources Information Center
Colardyn, Danielle; White, Kathleen M.
From a search of (mostly French) literature, a hypothesis was formulated that students with both academic training and work experience would solve a practical learning problem more easily than students with academic learning only. A study was conducted at the Conservatoire National des Arts et Metiers in Paris to test this hypothesis. Two groups,…
Feedback Processes in Multimedia Language Learning Software
ERIC Educational Resources Information Center
Kartal, Erdogan
2010-01-01
Feedback has been one of the important elements of learning and teaching theories and still pervades the literature and instructional models, especially computer and web-based ones. However, the mechanisms about feedback dominating the fundamentals of all the instructional models designed for self-learning have changed considerably with the…
Modeling Learning Processes in Lexical CALL.
ERIC Educational Resources Information Center
Goodfellow, Robin; Laurillard, Diana
1994-01-01
Studies the performance of a novice Spanish student using a Computer-assisted language learning (CALL) system designed for vocabulary enlargement. Results indicate that introspective evidence may be used to validate performance data within a theoretical framework that characterizes the learning approach as "surface" or "deep." (25 references)…
Explicit and Implicit Processes Constitute the Fast and Slow Processes of Sensorimotor Learning.
McDougle, Samuel D; Bond, Krista M; Taylor, Jordan A
2015-07-01
A popular model of human sensorimotor learning suggests that a fast process and a slow process work in parallel to produce the canonical learning curve (Smith et al., 2006). Recent evidence supports the subdivision of sensorimotor learning into explicit and implicit processes that simultaneously subserve task performance (Taylor et al., 2014). We set out to test whether these two accounts of learning processes are homologous. Using a recently developed method to assay explicit and implicit learning directly in a sensorimotor task, along with a computational modeling analysis, we show that the fast process closely resembles explicit learning and the slow process approximates implicit learning. In addition, we provide evidence for a subdivision of the slow/implicit process into distinct manifestations of motor memory. We conclude that the two-state model of motor learning is a close approximation of sensorimotor learning, but it is unable to describe adequately the various implicit learning operations that forge the learning curve. Our results suggest that a wider net be cast in the search for the putative psychological mechanisms and neural substrates underlying the multiplicity of processes involved in motor learning.
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
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 Vo2max 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.
The Moeawatea Heritage Conservation Process: Service Learning with Kiwi Attitude.
ERIC Educational Resources Information Center
Harre, David; Boshier, Roger
1999-01-01
Describes how 12 socioeconomically excluded young adults restored a New Zealand social activist's home using a process related to service learning but enhanced with critical analysis of society. Compares the process to service learning in terms of organizational setting, social class, governance, level of abstraction, conceptual and reflective…
Process Writing for High-Risk and Learning Disabled Students.
ERIC Educational Resources Information Center
Zaragoza, Nina
1987-01-01
Discusses the major elements of writing process instruction and the positive influence they have on learning-disabled and inner-city high-risk students. Argues that such process-writing programs might serve as a preventative measure for children likely to be categorized as learning disabled in the future. (HTH)
Attentional Processes in Children's Learning. Appendix A: Project Papers.
ERIC Educational Resources Information Center
White, Sheldon H.
This appendix includes seven papers which focus on various aspects of the learning processes of children ages 5-7: (1) S. Thompson, "Transitions to concrete operations: A survey of Piaget's writings" (in outline form); (2) S. H. White, "Changes in learning processes in the late preschool years," an examination of cross-cultural…
Students' Learning Activities While Studying Biological Process Diagrams
ERIC Educational Resources Information Center
Kragten, Marco; Admiraal, Wilfried; Rijlaarsdam, Gert
2015-01-01
Process diagrams describe how a system functions (e.g. photosynthesis) and are an important type of representation in Biology education. In the present study, we examined students' learning activities while studying process diagrams, related to their resulting comprehension of these diagrams. Each student completed three learning tasks. Verbal…
Electrical Storm Simulation to Improve the Learning Physics Process
ERIC Educational Resources Information Center
Martínez Muñoz, Miriam; Jiménez Rodríguez, María Lourdes; Gutiérrez de Mesa, José Antonio
2013-01-01
This work is part of a research project whose main objective is to understand the impact that the use of Information and Communication Technology (ICT) has on the teaching and learning process on the subject of Physics. We will show that, with the use of a storm simulator, physics students improve their learning process on one hand they understand…
Enhancing the Teaching-Learning Process: A Knowledge Management Approach
ERIC Educational Resources Information Center
Bhusry, Mamta; Ranjan, Jayanthi
2012-01-01
Purpose: The purpose of this paper is to emphasize the need for knowledge management (KM) in the teaching-learning process in technical educational institutions (TEIs) in India, and to assert the impact of information technology (IT) based KM intervention in the teaching-learning process. Design/methodology/approach: The approach of the paper is…
The Adoption Process of Corporate E-Learning in Italy
ERIC Educational Resources Information Center
Comacchio, Anna; Scapolan, AnnaChiara
2004-01-01
The diffusion process of e-learning has been, in recent years, at the centre of several studies. These researches focused mainly on the USA case, where there has been an exponential adoption both in the public and private sectors. From this perspective the paper would give a contribution to understand the diffusion process of e-learning in a…
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…
The Assurance of Learning Process Components and the Effects of Engaging Students in the Learning
ERIC Educational Resources Information Center
Mosca, Joseph B.; Agacer, Gilder; Flaming, Linda; Buzza, John
2011-01-01
Assurance of learning process plays a major role in higher education and has increased the accountability on the part of instructors at all levels. This paper will discuss the role of assurance processes in teaching and the ways to measure these processes of student learning. The research focus will be to determine if student engagement in problem…
Nolan, Bernard T.; Fienen, Michael N.; Lorenz, David L.
2015-01-01
We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R2 and a version with R2 within one standard error of the maximum (the 1SE model). The former yielded CV training R2 values of 0.94–1.0. Cross-validation testing R2 values indicate predictive performance, and these were 0.22–0.39 for the maximum R2 models and 0.19–0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.
Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul
2016-01-01
Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257
Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R
2016-12-01
Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record
Rapid e-Learning Tools Selection Process for Cognitive and Psychomotor Learning Objectives
ERIC Educational Resources Information Center
Ku, David Tawei; Huang, Yung-Hsin
2012-01-01
This study developed a decision making process for the selection of rapid e-learning tools that could match different learning domains. With the development of the Internet, the speed of information updates has become faster than ever. E-learning has rapidly become the mainstream for corporate training and academic instruction. In order to reduce…
Tele-Learning and Distance Learning Re-Engineering Process.
ERIC Educational Resources Information Center
Cruz, Dulce Marcia; de Moraes, Marialice; Barcia, Ricardo Miranda
The adoption and use of new interactive technologies in Distance Education, especially Tele-learning is a growing tendency in the most advanced countries. Nowadays, this tendency is so strong that being interactive is seen as a necessary pre-condition. Some reasons for this are a the growing perceived value of group-working; the popularization of…
A Concept Transformation Learning Model for Architectural Design Learning Process
ERIC Educational Resources Information Center
Wu, Yun-Wu; Weng, Kuo-Hua; Young, Li-Ming
2016-01-01
Generally, in the foundation course of architectural design, much emphasis is placed on teaching of the basic design skills without focusing on teaching students to apply the basic design concepts in their architectural designs or promoting students' own creativity. Therefore, this study aims to propose a concept transformation learning model to…
Beginning Reading: A Natural Language Learning Process.
ERIC Educational Resources Information Center
Tovey, Duane R.; And Others
1988-01-01
Offers teachers and parents practical suggestions for helping children to begin reading naturally. Looks at specific strategies for helping young children to learn to read, particularly a built-in success procedure emphasizing nonvisual aspects of reading. (RWB)
NASA Astrophysics Data System (ADS)
Gao, Yi; Gholami, Behnood; MacLeod, Robert S.; Blauer, Joshua; Haddad, Wassim M.; Tannenbaum, Allen R.
2010-03-01
Atrial fibrillation, a cardiac arrhythmia characterized by unsynchronized electrical activity in the atrial chambers of the heart, is a rapidly growing problem in modern societies. One treatment, referred to as catheter ablation, targets specific parts of the left atrium for radio frequency ablation using an intracardiac catheter. Magnetic resonance imaging has been used for both pre- and and post-ablation assessment of the atrial wall. Magnetic resonance imaging can aid in selecting the right candidate for the ablation procedure and assessing post-ablation scar formations. Image processing techniques can be used for automatic segmentation of the atrial wall, which facilitates an accurate statistical assessment of the region. As a first step towards the general solution to the computer-assisted segmentation of the left atrial wall, in this paper we use shape learning and shape-based image segmentation to identify the endocardial wall of the left atrium in the delayed-enhancement magnetic resonance images.
Project T.E.A.M. (Technical Education Advancement Modules). Advanced Statistical Process Control.
ERIC Educational Resources Information Center
Dunlap, Dale
This instructional guide, one of a series developed by the Technical Education Advancement Modules (TEAM) project, is a 20-hour advanced statistical process control (SPC) and quality improvement course designed to develop the following competencies: (1) understanding quality systems; (2) knowing the process; (3) solving quality problems; and (4)…
Statistical data generated through CFD to aid in the scale-up of shear sensitive processes
NASA Astrophysics Data System (ADS)
Khan, Irfan; Das, Shankhadeep; Cloeter, Mike; Gillis, Paul; Poindexter, Michael
2016-11-01
A number of industrial processes are considered shear-sensitive, where the product quality depends on achieving the right balance between mixing energy input and the resulting strain rate distribution in the process. Examples of such industrial processes are crystallization, flocculation and suspension polymerization. Scale-up of such processes are prone to a number of challenges including the optimization of mixing and shear rate distribution in the process. Computational Fluid Dynamics (CFD) can be a valuable tool to aid in the process scale-up; however for modeling purpose, the process will often need to be simplified appropriately to reduce the computational complexity. Commercial CFD tools with appropriate Lagrangian particle tracking models can be used to gather statistical data such as maximum strain rate distribution and maximum number of passes through a specific strain rate. This presentation will discuss such statistical tools and their application to a model scale-up problem.
Considerations for implementing an organizational lessons learned process.
Fosshage, Erik D
2013-05-01
This report examines the lessons learned process by a review of the literature in a variety of disciplines, and is intended as a guidepost for organizations that are considering the implementation of their own closed-loop learning process. Lessons learned definitions are provided within the broader context of knowledge management and the framework of a learning organization. Shortcomings of existing practices are summarized in an attempt to identify common pitfalls that can be avoided by organizations with fledgling experiences of their own. Lessons learned are then examined through a dual construct of both process and mechanism, with emphasis on integrating into organizational processes and promoting lesson reuse through data attributes that contribute toward changed behaviors. The report concludes with recommended steps for follow-on efforts.
Statistical error in simulations of Poisson processes: Example of diffusion in solids
NASA Astrophysics Data System (ADS)
Nilsson, Johan O.; Leetmaa, Mikael; Vekilova, Olga Yu.; Simak, Sergei I.; Skorodumova, Natalia V.
2016-08-01
Simulations of diffusion in solids often produce poor statistics of diffusion events. We present an analytical expression for the statistical error in ion conductivity obtained in such simulations. The error expression is not restricted to any computational method in particular, but valid in the context of simulation of Poisson processes in general. This analytical error expression is verified numerically for the case of Gd-doped ceria by running a large number of kinetic Monte Carlo calculations.
A computational model associating learning process, word attributes, and age of acquisition.
Hidaka, Shohei
2013-01-01
We propose a new model-based approach linking word learning to the age of acquisition (AoA) of words; a new computational tool for understanding the relationships among word learning processes, psychological attributes, and word AoAs as measures of vocabulary growth. The computational model developed describes the distinct statistical relationships between three theoretical factors underpinning word learning and AoA distributions. Simply put, this model formulates how different learning processes, characterized by change in learning rate over time and/or by the number of exposures required to acquire a word, likely result in different AoA distributions depending on word type. We tested the model in three respects. The first analysis showed that the proposed model accounts for empirical AoA distributions better than a standard alternative. The second analysis demonstrated that the estimated learning parameters well predicted the psychological attributes, such as frequency and imageability, of words. The third analysis illustrated that the developmental trend predicted by our estimated learning parameters was consistent with relevant findings in the developmental literature on word learning in children. We further discuss the theoretical implications of our model-based approach.
A Measurable Model of the Creative Process in the Context of a Learning Process
ERIC Educational Resources Information Center
Ma, Min; Van Oystaeyen, Fred
2016-01-01
The authors' aim was to arrive at a measurable model of the creative process by putting creativity in the context of a learning process. The authors aimed to provide a rather detailed description of how creative thinking fits in a general description of the learning process without trying to go into an analysis of a biological description of the…
NASA Technical Reports Server (NTRS)
Hutchens, Dale E.; Doan, Patrick A.; Boothe, Richard E.
1997-01-01
Bonding labs at both MSFC and the northern Utah production plant prepare bond test specimens which simulate or witness the production of NASA's Reusable Solid Rocket Motor (RSRM). The current process for preparing the bonding surfaces employs 1,1,1-trichloroethane vapor degreasing, which simulates the current RSRM process. Government regulations (e.g., the 1990 Amendments to the Clean Air Act) have mandated a production phase-out of a number of ozone depleting compounds (ODC) including 1,1,1-trichloroethane. In order to comply with these regulations, the RSRM Program is qualifying a spray-in-air (SIA) precision cleaning process using Brulin 1990, an aqueous blend of surfactants. Accordingly, surface preparation prior to bonding process simulation test specimens must reflect the new production cleaning process. The Bonding Lab Statistical Process Control (SPC) program monitors the progress of the lab and its capabilities, as well as certifies the bonding technicians, by periodically preparing D6AC steel tensile adhesion panels with EA-91 3NA epoxy adhesive using a standardized process. SPC methods are then used to ensure the process is statistically in control, thus producing reliable data for bonding studies, and identify any problems which might develop. Since the specimen cleaning process is being changed, new SPC limits must be established. This report summarizes side-by-side testing of D6AC steel tensile adhesion witness panels and tapered double cantilevered beams (TDCBs) using both the current baseline vapor degreasing process and a lab-scale spray-in-air process. A Proceco 26 inches Typhoon dishwasher cleaned both tensile adhesion witness panels and TDCBs in a process which simulates the new production process. The tests were performed six times during 1995, subsequent statistical analysis of the data established new upper control limits (UCL) and lower control limits (LCL). The data also demonstrated that the new process was equivalent to the vapor
The Words Children Hear: Picture Books and the Statistics for Language Learning.
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.
Anxious individuals have difficulty learning the causal statistics of aversive environments.
Browning, Michael; Behrens, Timothy E; Jocham, Gerhard; O'Reilly, Jill X; Bishop, Sonia J
2015-04-01
Statistical regularities in the causal structure of the environment enable us to predict the probable outcomes of our actions. Environments differ in the extent to which action-outcome contingencies are stable or volatile. Difficulty in being able to use this information to optimally update outcome predictions might contribute to the decision-making difficulties seen in anxiety. We tested this using an aversive learning task manipulating environmental volatility. Human participants low in trait anxiety matched updating of their outcome predictions to the volatility of the current environment, as predicted by a Bayesian model. Individuals with high trait anxiety showed less ability to adjust updating of outcome expectancies between stable and volatile environments. This was linked to reduced sensitivity of the pupil dilatory response to volatility, potentially indicative of altered norepinephrinergic responsivity to changes in this aspect of environmental information.
Anxious individuals have difficulty learning the causal statistics of aversive environments
Browning, Michael; Behrens, Timothy E; Jocham, Gerhard; O’Reilly, Jill X; Bishop, Sonia J
2015-01-01
Statistical regularities in the causal structure of the environment enable us to predict the probable outcomes of our actions. Environments differ in the extent to which action-outcome contingencies are stable or volatile. Difficulty in being able to use this information to optimally update outcome predictions might contribute to the decision-making difficulties seen in anxiety. We tested this using an aversive learning task manipulating environmental volatility. Low trait anxious human participants matched updating of their outcome predictions to the volatility of the current environment, as predicted by a Bayesian model. High trait anxious individuals showed less ability to adjust updating of outcome expectancies between stable and volatile environments. This was linked to reduced sensitivity of the pupil dilatory response to volatility, potentially indicative of altered norepinephrinergic responsivity to changes in this aspect of environmental information. PMID:25730669
The words children hear: Picture books and the statistics for language learning
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
ERIC Educational Resources Information Center
Guler, Cetin; Altun, Arif
2010-01-01
Learning objects (LOs) can be defined as resources that are reusable, digital with the aim of fulfilling learning objectives (or expectations). Educators, both at the individual and institutional levels, are cautioned about the fact that LOs are to be processed through a proper development process. Who should be involved in the LO development…
Pearce, Marcus T; Ruiz, María Herrojo; Kapasi, Selina; Wiggins, Geraint A; Bhattacharya, Joydeep
2010-03-01
The ability to anticipate forthcoming events has clear evolutionary advantages, and predictive successes or failures often entail significant psychological and physiological consequences. In music perception, the confirmation and violation of expectations are critical to the communication of emotion and aesthetic effects of a composition. Neuroscientific research on musical expectations has focused on harmony. Although harmony is important in Western tonal styles, other musical traditions, emphasizing pitch and melody, have been rather neglected. In this study, we investigated melodic pitch expectations elicited by ecologically valid musical stimuli by drawing together computational, behavioural, and electrophysiological evidence. Unlike rule-based models, our computational model acquires knowledge through unsupervised statistical learning of sequential structure in music and uses this knowledge to estimate the conditional probability (and information content) of musical notes. Unlike previous behavioural paradigms that interrupt a stimulus, we devised a new paradigm for studying auditory expectation without compromising ecological validity. A strong negative correlation was found between the probability of notes predicted by our model and the subjectively perceived degree of expectedness. Our electrophysiological results showed that low-probability notes, as compared to high-probability notes, elicited a larger (i) negative ERP component at a late time period (400-450 ms), (ii) beta band (14-30 Hz) oscillation over the parietal lobe, and (iii) long-range phase synchronization between multiple brain regions. Altogether, the study demonstrated that statistical learning produces information-theoretic descriptions of musical notes that are proportional to their perceived expectedness and are associated with characteristic patterns of neural activity.
Lessons Learned from the Collaborative Writing Process
ERIC Educational Resources Information Center
Bhavsar, Victoria; Ahn, Ruth
2013-01-01
We reflect on how to implement the instrumental aspect of collaborative writing in such a way that the developmental aspect of collaborative writing is maximally fostered, based on conditions necessary for socially constructed learning. We discuss four instrumental strategies that bolster mutual ownership of the writing and protect the social…
Dual Learning Processes in Interactive Skill Acquisition
ERIC Educational Resources Information Center
Fu, Wai-Tat; Anderson, John R.
2008-01-01
Acquisition of interactive skills involves the use of internal and external cues. Experiment 1 showed that when actions were interdependent, learning was effective with and without external cues in the single-task condition but was effective only with the presence of external cues in the dual-task condition. In the dual-task condition, actions…
Powerful Practices in Digital Learning Processes
ERIC Educational Resources Information Center
Sørensen, Birgitte Holm; Levinsen, Karin Tweddell
2015-01-01
The present paper is based on two empirical research studies. The "Netbook 1:1" project (2009-2012), funded by the municipality of Gentofte and Microsoft Denmark, is complete, while "Students' digital production and students as learning designers" (2013-2015), funded by the Danish Ministry of Education, is ongoing. Both…
Leadership as Learning: Conceptualizing the Process
ERIC Educational Resources Information Center
Amey, Marilyn J.
2005-01-01
Community college leaders face new and diverse challenges, often requiring different orientations to leadership than were effective previously. Yet, focusing on leadership as a series of career stages through which particular skills and techniques are learned often leaves leaders without the capacity to do the adaptive work required of their…
Professional Learning Is a Lifelong Process
ERIC Educational Resources Information Center
Blanton, Patricia
2009-01-01
At a recent conference sponsored by the National Science Teachers Association on Professional Learning Communities in Science, I was reminded how crucial it is for teachers to continually examine their practice. As one new to teaching physics, you may be overwhelmed by the task of helping your students develop accurate understanding of the…
Comparing estimates of climate change impacts from process-based and statistical crop models
NASA Astrophysics Data System (ADS)
Lobell, David B.; Asseng, Senthold
2017-01-01
The potential impacts of climate change on crop productivity are of widespread interest to those concerned with addressing climate change and improving global food security. Two common approaches to assess these impacts are process-based simulation models, which attempt to represent key dynamic processes affecting crop yields, and statistical models, which estimate functional relationships between historical observations of weather and yields. Examples of both approaches are increasingly found in the scientific literature, although often published in different disciplinary journals. Here we compare published sensitivities to changes in temperature, precipitation, carbon dioxide (CO2), and ozone from each approach for the subset of crops, locations, and climate scenarios for which both have been applied. Despite a common perception that statistical models are more pessimistic, we find no systematic differences between the predicted sensitivities to warming from process-based and statistical models up to +2 °C, with limited evidence at higher levels of warming. For precipitation, there are many reasons why estimates could be expected to differ, but few estimates exist to develop robust comparisons, and precipitation changes are rarely the dominant factor for predicting impacts given the prominent role of temperature, CO2, and ozone changes. A common difference between process-based and statistical studies is that the former tend to include the effects of CO2 increases that accompany warming, whereas statistical models typically do not. Major needs moving forward include incorporating CO2 effects into statistical studies, improving both approaches’ treatment of ozone, and increasing the use of both methods within the same study. At the same time, those who fund or use crop model projections should understand that in the short-term, both approaches when done well are likely to provide similar estimates of warming impacts, with statistical models generally
How Students Learn: Information Processing, Intellectual Development and Confrontation
ERIC Educational Resources Information Center
Entwistle, Noel
1975-01-01
A model derived from information processing theory is described, which helps to explain the complex verbal learning of students and suggests implications for lecturing techniques. Other factors affecting learning, which are not covered by the model, are discussed in relationship to it: student's intellectual development and effects of individual…
Assessing Adult Learning Preferences Using the Analytic Hierarchy Process.
ERIC Educational Resources Information Center
Lee, Doris; McCool, John; Napieralski, Laura
2000-01-01
Graduate students (n=134) used the analytic hierarchy process, which weights expressed preferences, to rate four learning activities: lectures, discussion/reflection, individual projects, and group projects. Their preferences for discussion/reflection and individual projects were independent of auditory, visual, and kinesthetic learning styles.…
Informal Learning in the Workplace: Key Activities and Processes
ERIC Educational Resources Information Center
Cunningham, John; Hillier, Emilie
2013-01-01
Purpose: The purpose of this study is to define characteristics and processes that enhance informal learning in a public sector workplace. Design/methodology/approach: Based on interviews and questionnaires, the authors solicited examples of informal learning practices that 40 supervisors experienced during their careers. The examples were content…
The Self-Directed Learning Process of Older, Rural Adults
ERIC Educational Resources Information Center
Roberson, Donald N., Jr.; Merriam, Sharan B.
2005-01-01
Medical advances and lifestyle changes have resulted in older adults living longer and healthier lives. Nevertheless, older adulthood, as other life stages, requires change in work, family, and health. Self-directed learning (SDL) is one way of negotiating these transitions. The purpose of this study was to understand this process of learning.…
Supporting Reflection on Self-Directed Learning Process in Hyperspace
NASA Astrophysics Data System (ADS)
Kashihara, Akihiro; Sakamoto, Masanao; Hasegawa, Shinobu; Toyoda, Jun'ichi
Hypermedia/hypertext-based resources for learning generally provide learners with hyperspace, which consists of pages and links among the pages. In the hyperspace, they can navigate the pages in a self-directed way to learn the domain concepts/knowledge. The navigation often involves constructing knowledge, in which they would make semantic relationships among the contents learned in the navigated pages. Such self-directed learning in hyperspace requires learners to reflect on their knowledge construction process, which they have carried out so far, since what and how they have learned becomes hazy as the navigation progresses. However, it is hard for them to keep reflection during navigating hyperspace. The main issue addressed in this paper is how to facilitate learners' reflection to promote their self-directed learning. Our approach to this issue is to provide learners with a learning tool, which allows learners to annotate their navigation history representation with their knowledge construction process. The annotated navigation history enables them to reflect on their knowledge construction process. This paper also demonstrates an interactive history, which generates the annotated navigation history from learners' annotation. It also generates a knowledge map that visualizes the semantic relationships among the pages learners have learned in hyperspace. This paper also describes a case study with the interactive history. The results indicate that it facilitates reflection on knowledge construction process carried out in hyperspace.
Experiential Learning: A Course Design Process for Critical Thinking
ERIC Educational Resources Information Center
Hamilton, Janet G.; Klebba, Joanne M.
2011-01-01
This article describes a course design process to improve the effectiveness of using experiential learning techniques to foster critical thinking skills. The authors examine prior research to identify essential dimensions of experiential learning in relation to higher order thinking. These dimensions provide key insights for the selection of…
Expert Knowledge, Distinctiveness, and Levels of Processing in Language Learning
ERIC Educational Resources Information Center
Bird, Steve
2012-01-01
The foreign language vocabulary learning research literature often attributes strong mnemonic potency to the cognitive processing of meaning when learning words. Routinely cited as support for this idea are experiments by Craik and Tulving (C&T) demonstrating superior recognition and recall of studied words following semantic tasks ("deep"…
Reframing Teachers' Intercultural Learning as an Emotional Process
ERIC Educational Resources Information Center
Jokikokko, Katri
2016-01-01
The importance of emotions in the process of intercultural learning has been recognised, but the topic has not been extensively theorised. This theoretical review article synthesises the research literature on emotions in the context of teachers' intercultural learning. The article argues that emotions are a vital part of any change, and thus play…
Web-Enabled Simulations: Exploring the Learning Process
ERIC Educational Resources Information Center
Whitehouse, Kendall
2005-01-01
With funding from Alfred West, Jr., an alumnus of The Wharton School of the University of Pennsylvania and chairman of SEI Investments, Wharton Dean Patrick T. Harker established the Alfred West Jr. Learning Lab to explore ways "to reach a deeper understanding of how people learn and to push that process to a higher level using advancements…
Schlichting, Margaret L; Guarino, Katharine F; Schapiro, Anna C; Turk-Browne, Nicholas B; Preston, Alison R
2017-01-01
Despite the importance of learning and remembering across the lifespan, little is known about how the episodic memory system develops to support the extraction of associative structure from the environment. Here, we relate individual differences in volumes along the hippocampal long axis to performance on statistical learning and associative inference tasks-both of which require encoding associations that span multiple episodes-in a developmental sample ranging from ages 6 to 30 years. Relating age to volume, we found dissociable patterns across the hippocampal long axis, with opposite nonlinear volume changes in the head and body. These structural differences were paralleled by performance gains across the age range on both tasks, suggesting improvements in the cross-episode binding ability from childhood to adulthood. Controlling for age, we also found that smaller hippocampal heads were associated with superior behavioral performance on both tasks, consistent with this region's hypothesized role in forming generalized codes spanning events. Collectively, these results highlight the importance of examining hippocampal development as a function of position along the hippocampal axis and suggest that the hippocampal head is particularly important in encoding associative structure across development.
Going Beyond a Mean-field Model for the Learning Cortex: Second-Order Statistics
Steyn-Ross, Moira L.; Steyn-Ross, D. A.; Sleigh, J. W.
2008-01-01
Mean-field models of the cortex have been used successfully to interpret the origin of features on the electroencephalogram under situations such as sleep, anesthesia, and seizures. In a mean-field scheme, dynamic changes in synaptic weights can be considered through fluctuation-based Hebbian learning rules. However, because such implementations deal with population-averaged properties, they are not well suited to memory and learning applications where individual synaptic weights can be important. We demonstrate that, through an extended system of equations, the mean-field models can be developed further to look at higher-order statistics, in particular, the distribution of synaptic weights within a cortical column. This allows us to make some general conclusions on memory through a mean-field scheme. Specifically, we expect large changes in the standard deviation of the distribution of synaptic weights when fluctuation in the mean soma potentials are large, such as during the transitions between the “up” and “down” states of slow-wave sleep. Moreover, a cortex that has low structure in its neuronal connections is most likely to decrease its standard deviation in the weights of excitatory to excitatory synapses, relative to the square of the mean, whereas a cortex with strongly patterned connections is most likely to increase this measure. This suggests that fluctuations are used to condense the coding of strong (presumably useful) memories into fewer, but dynamic, neuron connections, while at the same time removing weaker (less useful) memories. PMID:19669541
A statistical learning strategy for closed-loop control of fluid flows
NASA Astrophysics Data System (ADS)
Guéniat, Florimond; Mathelin, Lionel; Hussaini, M. Yousuff
2016-12-01
This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz'63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.
The use of statistical process control to improve the accuracy of turning
NASA Astrophysics Data System (ADS)
Pisarciuc, Cristian
2016-11-01
The present work deals with the turning process improvement using means of statistical process control. The approach on improvement is related to the fact that several methods are used in order to achieve quality defined by technical specifications. The experimental data is collected during identical and successive manufacturing processes of turning of an electrical motor shaft. The initial process presents some difficulties because many machined parts are nonconforming as a consequence of reduced precision of turning. The article is using data collected in turning process, presented through histograms and control charts, to improve the accuracy in order to reduce scrap.
Statistical learning of serial visual transitions by neurons in monkey inferotemporal cortex.
Meyer, Travis; Ramachandran, Suchitra; Olson, Carl R
2014-07-09
If monkeys repeatedly, over the course of weeks, view displays in which two images appear in fixed sequence, then neurons of inferotemporal cortex (ITC) come to exhibit prediction suppression. The response to the trailing image is weaker if it follows the leading image with which it was paired during training than if it follows some other leading image. Prediction suppression is a plausible neural mechanism for statistical learning of visual transitions such as has been demonstrated in behavioral studies of human infants and adults. However, in the human studies, subjects are exposed to continuous sequences in which the same image can be both predicted and predicting and statistical dependency can exist between nonadjacent items. The aim of the present study was to investigate whether prediction suppression in ITC develops under such circumstances. To resolve this issue, we exposed monkeys repeatedly to triplets of images presented in fixed order. The results indicate that prediction suppression can be induced by training not only with pairs of images but also with longer sequences.
Semi-supervised learning of statistical models for natural language understanding.
Zhou, Deyu; He, Yulan
2014-01-01
Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.
ERIC Educational Resources Information Center
Jarman, Jay
2011-01-01
This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms,…
NASA Astrophysics Data System (ADS)
Goetz, Jason; Brenning, Alexander; Petschko, Helene; Leopold, Philip
2015-04-01
With so many techniques now available for landslide susceptibility modelling, it can be challenging to decide on which technique to apply. Generally speaking, the criteria for model selection should be tied closely to end users' purpose, which could be spatial prediction, spatial analysis or both. In our research, we focus on comparing the spatial predictive abilities of landslide susceptibility models. We illustrate how spatial cross-validation, a statistical approach for assessing spatial prediction performance, can be applied with the area under the receiver operating characteristic curve (AUROC) as a prediction measure for model comparison. Several machine learning and statistical techniques are evaluated for prediction in Lower Austria: support vector machine, random forest, bundling with penalized linear discriminant analysis, logistic regression, weights of evidence, and the generalized additive model. In addition to predictive performance, the importance of predictor variables in each model was estimated using spatial cross-validation by calculating the change in AUROC performance when variables are randomly permuted. The susceptibility modelling techniques were tested in three areas of interest in Lower Austria, which have unique geologic conditions associated with landslide occurrence. Overall, we found for the majority of comparisons that there were little practical or even statistically significant differences in AUROCs. That is the models' prediction performances were very similar. Therefore, in addition to prediction, the ability to interpret models for spatial analysis and the qualitative qualities of the prediction surface (map) are considered and discussed. The measure of variable importance provided some insight into the model behaviour for prediction, in particular for "black-box" models. However, there were no clear patterns in all areas of interest to why certain variables were given more importance over others.
NASA Astrophysics Data System (ADS)
Neri, Izaak; Roldán, Édgar; Jülicher, Frank
2017-01-01
We study the statistics of infima, stopping times, and passage probabilities of entropy production in nonequilibrium steady states, and we show that they are universal. We consider two examples of stopping times: first-passage times of entropy production and waiting times of stochastic processes, which are the times when a system reaches a given state for the first time. Our main results are as follows: (i) The distribution of the global infimum of entropy production is exponential with mean equal to minus Boltzmann's constant; (ii) we find exact expressions for the passage probabilities of entropy production; (iii) we derive a fluctuation theorem for stopping-time distributions of entropy production. These results have interesting implications for stochastic processes that can be discussed in simple colloidal systems and in active molecular processes. In particular, we show that the timing and statistics of discrete chemical transitions of molecular processes, such as the steps of molecular motors, are governed by the statistics of entropy production. We also show that the extreme-value statistics of active molecular processes are governed by entropy production; for example, we derive a relation between the maximal excursion of a molecular motor against the direction of an external force and the infimum of the corresponding entropy-production fluctuations. Using this relation, we make predictions for the distribution of the maximum backtrack depth of RNA polymerases, which follow from our universal results for entropy-production infima.
ERIC Educational Resources Information Center
Rodriguez, Clemente; Gutierrez-Perez, Jose; Pozo, Teresa
2010-01-01
Introduction: This research seeks to determine the influence exercised by a set of presage and process variables (students' pre-existing opinion towards statistics, their dedication to mastery of statistics content, assessment of the teaching materials, and the teacher's effort in the teaching of statistics) in students' resolution of activities…
Methods and Learning Processes in a Continuing Education Programme
ERIC Educational Resources Information Center
Shrivastava, Virginia
1976-01-01
Discussion of learning processes and methods for use with adult learners by the teaching staff, extension committees, councils, and other policy making bodies. Paper presented at the Hyderabad conference on university continuing education. (Editor/ABM)
Evaluating Collaborative Learning Processes Using System-Based Measurement
ERIC Educational Resources Information Center
Collazos, Cesar A.; Guerrero, Luis A.; Pino, Jose A.; Renzi, Stefano; Klobas, Jane; Ortega, Manuel; Redondo, Miguel A.; Bravo, Crescencio
2007-01-01
Much of the research on collaborative work focuses on the quality of the group outcome as a measure of success. There is less research on the collaboration process itself, but an understanding of the process should help to improve both the process and the outcomes of collaboration. Understanding and analyzing collaborative learning processes…
Dipnall, Joanna F.
2016-01-01
Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and
ERIC Educational Resources Information Center
Matthews, Tansy E.
2009-01-01
This article describes the development of the Virtual Library of Virginia (VIVA). The VIVA statistics-processing system remains a work in progress. Member libraries will benefit from the ability to obtain the actual data from the VIVA site, rather than just the summaries, so a project to make these data available is currently being planned. The…
ERIC Educational Resources Information Center
Smith, Toni M.; Hjalmarson, Margret A.
2013-01-01
The purpose of this study is to examine prospective mathematics specialists' engagement in an instructional sequence designed to elicit and develop their understandings of random processes. The study was conducted with two different sections of a probability and statistics course for K-8 teachers. Thirty-two teachers participated. Video analyses…
ERIC Educational Resources Information Center
Karimi, Hamid; O'Brian, Sue; Onslow, Mark; Jones, Mark; Menzies, Ross; Packman, Ann
2013-01-01
Purpose: Stuttering varies between and within speaking situations. In this study, the authors used statistical process control charts with 10 case studies to investigate variability of stuttering frequency. Method: Participants were 10 adults who stutter. The authors counted the percentage of syllables stuttered (%SS) for segments of their speech…
2007-08-01
quite robust to input noise. Wang and Acero (2003) propose an extended HMMmodel for the ATIS do- main, where a multiple-word segment is generated from...tational Natural Language Learning (EMNLP-CoNLL-2007), pp. 22–32. Prague, Czech Republic. Ye-Yi Wang and Alex Acero (2003). Combination of CFG and n-gram...2809–2812. Geneva, Switzerland. Ye-Yi Wang, Li Deng and Alex Acero (2005). Spoken language understanding. IEEE Signal Processing Magazine, 22(5):16–31
Statistically Qualified Neuro-Analytic system and Method for Process Monitoring
Vilim, Richard B.; Garcia, Humberto E.; Chen, Frederick W.
1998-11-04
An apparatus and method for monitoring a process involves development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two steps: deterministic model adaption and stochastic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics,augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation emor minimization technique. Stochastic model adaptation involves qualifying any remaining uncertainty in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output. Preferably, the developed SQNA model is validated using known sequential probability ratio tests and applied to the process as an on-line monitoring system.
A survey of image processing techniques and statistics for ballistic specimens in forensic science.
Gerules, George; Bhatia, Sanjiv K; Jackson, Daniel E
2013-06-01
This paper provides a review of recent investigations on the image processing techniques used to match spent bullets and cartridge cases. It is also, to a lesser extent, a review of the statistical methods that are used to judge the uniqueness of fired bullets and spent cartridge cases. We review 2D and 3D imaging techniques as well as many of the algorithms used to match these images. We also provide a discussion of the strengths and weaknesses of these methods for both image matching and statistical uniqueness. The goal of this paper is to be a reference for investigators and scientists working in this field.
NASA Astrophysics Data System (ADS)
Neelin, J.; Stechmann, S.; Sahany, S.
2013-12-01
Climate models exhibit substantial sensitivity to parameters and formulation assumptions in their convective parameterizations, leading to a search for better ways to constrain these. While the community now has vast satellite and in situ data sets on convection, one of the bottlenecks is to find diagnostics that 1) provide additional information beyond standard verifications, such as precipitation patterns; 2) yield insight into the fast-time behavior; 3) can be reasonably directly related to specific parameters or assumptions within the convective parameterization. This community endeavor involves identifying statistics that appear to cleanly condense information in observations and then, in addition to analyzing model successes and shortcomings with respect to these, attempting to pinpoint the processes involved. Simplified process models can play an important role in identifying the relationship between observational statistics and climate model representations and suggesting observational quantities to assess. Illustrating this strategy, diagnostics of the Community Earth System Model at various resolutions and parameters are compared to a set of observed convective onset statistics (requiring nonstandard but straightforward fast-timescale output). The model validates well in several nontrivial targets, including for precipitation event size distributions. Analytic solutions of a simple model for convective onset provide both a sense of how various processes affect the statistics and a baseline that the climate model should beat.
NASA Astrophysics Data System (ADS)
Goetz, J. N.; Brenning, A.; Petschko, H.; Leopold, P.
2015-08-01
Statistical and now machine learning prediction methods have been gaining popularity in the field of landslide susceptibility modeling. Particularly, these data driven approaches show promise when tackling the challenge of mapping landslide prone areas for large regions, which may not have sufficient geotechnical data to conduct physically-based methods. Currently, there is no best method for empirical susceptibility modeling. Therefore, this study presents a comparison of traditional statistical and novel machine learning models applied for regional scale landslide susceptibility modeling. These methods were evaluated by spatial k-fold cross-validation estimation of the predictive performance, assessment of variable importance for gaining insights into model behavior and by the appearance of the prediction (i.e. susceptibility) map. The modeling techniques applied were logistic regression (GLM), generalized additive models (GAM), weights of evidence (WOE), the support vector machine (SVM), random forest classification (RF), and bootstrap aggregated classification trees (bundling) with penalized discriminant analysis (BPLDA). These modeling methods were tested for three areas in the province of Lower Austria, Austria. The areas are characterized by different geological and morphological settings. Random forest and bundling classification techniques had the overall best predictive performances. However, the performances of all modeling techniques were for the majority not significantly different from each other; depending on the areas of interest, the overall median estimated area under the receiver operating characteristic curve (AUROC) differences ranged from 2.9 to 8.9 percentage points. The overall median estimated true positive rate (TPR) measured at a 10% false positive rate (FPR) differences ranged from 11 to 15pp. The relative importance of each predictor was generally different between the modeling methods. However, slope angle, surface roughness and plan
ERIC Educational Resources Information Center
Simonson, Shawn R.; Shadle, Susan E.
2013-01-01
Process Oriented Guided Inquiry Learning (POGIL) uses specially designed activities and cooperative learning to teach content and to actively engage students in inquiry, analytical thinking and teamwork. It has been used extensively in Chemistry education, but the use of POGIL is not well documented in other physical and biological sciences. This…
Learning Processes in Blended Language Learning: A Mixed-Methods Approach
ERIC Educational Resources Information Center
Shahrokni, Seyed Abdollah; Talaeizadeh, Ali
2013-01-01
This article attempts to investigate the learning processes in blended language learning through assessing sources of information: logs, chat and forum scripts, and semi-structured interviews. Creating a MOODLE-based parallel component to face-to-face instruction for a group of EFL learners, we probed into 2,984 logged actions providing raw…
ERIC Educational Resources Information Center
Nakamura, Yasuyuki; Nishi, Shinnosuke; Muramatsu, Yuta; Yasutake, Koichi; Yamakawa, Osamu; Tagawa, Takahiro
2014-01-01
In this paper, we introduce a mathematical model for collaborative learning and the answering process for multiple-choice questions. The collaborative learning model is inspired by the Ising spin model and the model for answering multiple-choice questions is based on their difficulty level. An intensive simulation study predicts the possibility of…
A Low-Achiever's Learning Process in Mathematics: Shirley's Fraction Learning
ERIC Educational Resources Information Center
Keijzer, Ronald; Terwel, Jan
2004-01-01
Research in mathematics education offers a considerable body of evidence that both high and low-achievers can benefit from learning mathematics in meaningful contexts. This case study offers an in-depth analysis of the learning process of a low-achieving student in the context of Realistic Mathematics Education (RME). The focus is on the use of…
ERIC Educational Resources Information Center
Keskitalo, Tuulikki
2012-01-01
Expectations for simulations in healthcare education are high; however, little is known about healthcare students' expectations of the learning process in virtual reality (VR) and simulation-based learning environments (SBLEs). This research aims to describe first-year healthcare students' (N=97) expectations regarding teaching, studying, and…