ASTER data processing using statistical learning algorithm
NASA Astrophysics Data System (ADS)
Kumar, Anil; Dadhwal, V. K.; Ghosh, S. K.
2006-12-01
In this work fuzzy set theory based as well as statistical learning algorithm have been studied at sub-pixel classification level. Here two Fuzzy set theory based classifiers, namely, Fuzzy c-Means (FCM) and Possibilistic c- Means (PCM) have been used in supervised modes. Support Vector Machines (SVMs) have been used in this study for density estimation as a statistical learning based sub-pixel classifier while using Mean Field (MF) method for learning. An in-house package SMIC (Sub-Pixel -Multi-Spectral Image Classifier) was used and sensitivity of all the three algorithms (FCM, PCM and SVMs) has been checked for dimensionality data sets at 3 to 14 bands from ASTER data. The accuracy of sub-pixel classification outputs has been evaluated using Fuzzy Error Matrix (FERM). In contrast to FCM and PCM, SVM approach showed a clear increase in the accuracy with higher dimensionality data and clearly out performed other two approaches for sub-pixel classification.
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. 2013 APA, all rights reserved
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. PMID:21833201
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.
NASA Astrophysics Data System (ADS)
Kaleva Oikarinen, Juho; Järvelä, Sanna; Kaasila, Raimo
2014-04-01
This design-based research project focuses on documenting statistical learning among 16-17-year-old Finnish upper secondary school students (N = 78) in a computer-supported collaborative learning (CSCL) environment. One novel value of this study is in reporting the shift from teacher-led mathematical teaching to autonomous small-group learning in statistics. The main aim of this study is to examine how student collaboration occurs in learning statistics in a CSCL environment. The data include material from videotaped classroom observations and the researcher's notes. In this paper, the inter-subjective phenomena of students' interactions in a CSCL environment are analysed by using a contact summary sheet (CSS). The development of the multi-dimensional coding procedure of the CSS instrument is presented. Aptly selected video episodes were transcribed and coded in terms of conversational acts, which were divided into non-task-related and task-related categories to depict students' levels of collaboration. The results show that collaborative learning (CL) can facilitate cohesion and responsibility and reduce students' feelings of detachment in our classless, periodic school system. The interactive .pdf material and collaboration in small groups enable statistical learning. It is concluded that CSCL is one possible method of promoting statistical teaching. CL using interactive materials seems to foster and facilitate statistical learning processes.
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…
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…
Statistical learning and auditory processing in children with music training: An ERP study.
Mandikal Vasuki, Pragati Rao; Sharma, Mridula; Ibrahim, Ronny; Arciuli, Joanne
2017-07-01
The question whether musical training is associated with enhanced auditory and cognitive abilities in children is of considerable interest. In the present study, we compared children with music training versus those without music training across a range of auditory and cognitive measures, including the ability to detect implicitly statistical regularities in input (statistical learning). Statistical learning of regularities embedded in auditory and visual stimuli was measured in musically trained and age-matched untrained children between the ages of 9-11years. In addition to collecting behavioural measures, we recorded electrophysiological measures to obtain an online measure of segmentation during the statistical learning tasks. Musically trained children showed better performance on melody discrimination, rhythm discrimination, frequency discrimination, and auditory statistical learning. Furthermore, grand-averaged ERPs showed that triplet onset (initial stimulus) elicited larger responses in the musically trained children during both auditory and visual statistical learning tasks. In addition, children's music skills were associated with performance on auditory and visual behavioural statistical learning tasks. Our data suggests that individual differences in musical skills are associated with children's ability to detect regularities. The ERP data suggest that musical training is associated with better encoding of both auditory and visual stimuli. Although causality must be explored in further research, these results may have implications for developing music-based remediation strategies for children with learning impairments. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
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.
Statistical learning and language acquisition
Romberg, Alexa R.; Saffran, Jenny R.
2011-01-01
Human learners, including infants, are highly sensitive to structure in their environment. Statistical learning refers to the process of extracting this structure. A major question in language acquisition in the past few decades has been the extent to which infants use statistical learning mechanisms to acquire their native language. There have been many demonstrations showing infants’ ability to extract structures in linguistic input, such as the transitional probability between adjacent elements. This paper reviews current research on how statistical learning contributes to language acquisition. Current research is extending the initial findings of infants’ sensitivity to basic statistical information in many different directions, including investigating how infants represent regularities, learn about different levels of language, and integrate information across situations. These current directions emphasize studying statistical language learning in context: within language, within the infant learner, and within the environment as a whole. PMID:21666883
Statistical learning and its consequences.
Turk-Browne, Nicholas B
2012-01-01
Statistical learning refers to an unconscious cognitive process in which repeated patterns, or regularities, are extracted from the sensory environment. In this chapter, I describe what is currently known about statistical learning. First, I classify types of regularities that exist in the visual environment. Second, I introduce a family of experimental paradigms that have been used to study statistical learning in the laboratory. Third, I review a series of behavioral and functional neuroimaging studies that seek to uncover the underlying nature of statistical learning. Finally, I consider ways in which statistical learning may be important for perception, attention, and visual search. The goals of this chapter are thus to highlight the prevalence of regularities, to explain how they are extracted by the mind and brain, and to suggest that the resulting knowledge has widespread consequences for other aspects of cognition.
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)
Students' attitudes towards learning statistics
NASA Astrophysics Data System (ADS)
Ghulami, Hassan Rahnaward; Hamid, Mohd Rashid Ab; Zakaria, Roslinazairimah
2015-05-01
Positive attitude towards learning is vital in order to master the core content of the subject matters under study. This is unexceptional in learning statistics course especially at the university level. Therefore, this study investigates the students' attitude towards learning statistics. Six variables or constructs have been identified such as affect, cognitive competence, value, difficulty, interest, and effort. The instrument used for the study is questionnaire that was adopted and adapted from the reliable instrument of Survey of Attitudes towards Statistics(SATS©). This study is conducted to engineering undergraduate students in one of the university in the East Coast of Malaysia. The respondents consist of students who were taking the applied statistics course from different faculties. The results are analysed in terms of descriptive analysis and it contributes to the descriptive understanding of students' attitude towards the teaching and learning process of statistics.
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…
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…
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
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…
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
Multidimensional visual statistical learning.
Turk-Browne, Nicholas B; Isola, Phillip J; Scholl, Brian J; Treat, Teresa A
2008-03-01
Recent studies of visual statistical learning (VSL) have demonstrated that statistical regularities in sequences of visual stimuli can be automatically extracted, even without intent or awareness. Despite much work on this topic, however, several fundamental questions remain about the nature of VSL. In particular, previous experiments have not explored the underlying units over which VSL operates. In a sequence of colored shapes, for example, does VSL operate over each feature dimension independently, or over multidimensional objects in which color and shape are bound together? The studies reported here demonstrate that VSL can be both object-based and feature-based, in systematic ways based on how different feature dimensions covary. For example, when each shape covaried perfectly with a particular color, VSL was object-based: Observers expressed robust VSL for colored-shape sub-sequences at test but failed when the test items consisted of monochromatic shapes or color patches. When shape and color pairs were partially decoupled during learning, however, VSL operated over features: Observers expressed robust VSL when the feature dimensions were tested separately. These results suggest that VSL is object-based, but that sensitivity to feature correlations in multidimensional sequences (possibly another form of VSL) may in turn help define what counts as an object.
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.
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.
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
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…
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…
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 mechanics of dictionary learning
NASA Astrophysics Data System (ADS)
Sakata, Ayaka; Kabashima, Yoshiyuki
2013-07-01
Finding a basis matrix (dictionary) by which objective signals are represented sparsely is of major relevance in various scientific and technological fields. We consider a problem to learn a dictionary from a set of training signals. We employ techniques of statistical mechanics of disordered systems to evaluate the size of the training set necessary to typically succeed in the dictionary learning. The results indicate that the necessary size is much smaller than previously estimated, which theoretically supports and/or encourages the use of dictionary learning in practical situations.
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…
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…
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…
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…
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…
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…
Learning: Statistical Mechanisms in Language Acquisition
NASA Astrophysics Data System (ADS)
Wonnacott, Elizabeth
The grammatical structure of human languages is extremely complex, yet children master this complexity with apparent ease. One explanation is that we come to the task of acquisition equipped with knowledge about the possible grammatical structures of human languages—so-called "Universal Grammar". An alternative is that grammatical patterns are abstracted from the input via a process of identifying reoccurring patterns and using that information to form grammatical generalizations. This statistical learning hypothesis receives support from computational research, which has revealed that even low level statistics based on adjacent word co-occurrences yield grammatically relevant information. Moreover, even as adults, our knowledge and usage of grammatical patterns is often graded and probabilistic, and in ways which directly reflect the statistical makeup of the language we experience. The current chapter explores such evidence and concludes that statistical learning mechanisms play a critical role in acquisition, whilst acknowledging holes in our current knowledge, particularly with respect to the learning of `higher level' syntactic behaviours. Throughout, I emphasize that although a statistical approach is traditionally associated with a strongly empiricist position, specific accounts make specific claims about the nature of the learner, both in terms of learning mechanisms and the information that is primitive to the learning system. In particular, working models which construct grammatical generalizations often assume inbuilt semantic abstractions.
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.
2013-01-01
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. PMID:23430452
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.
Right hemisphere dominance in visual statistical learning.
Roser, Matthew E; Fiser, József; Aslin, Richard N; Gazzaniga, Michael S
2011-05-01
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 multishape scenes presented in either the right or the left visual fields. Unbeknownst to the participants, the scenes were composed from a random combination of fixed pairs of shapes. Subsequent testing found that control participants could discriminate fixed-pair shapes from randomly combined shapes when presented in either visual field. The split-brain patient performed at chance except when both the practice and the test displays were presented in the left visual field (right hemisphere). These results suggest that the statistical learning of new visual features is dominated by visuospatial processing in the right hemisphere and provide a prediction about how fMRI activation patterns might change during unsupervised statistical learning.
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.
Statistical learning for chemical crystallography
NASA Astrophysics Data System (ADS)
Balachandran, Prasanna V.
A novel computational approach is developed for the study of chemical crystallography in materials science using the tools of information theory and data science. By integrating the information derived from phase homologies, electronic structure calculations and known crystal structure data, a high-dimensional data space is created. From this data space, we seek to extract statistically robust and yet physically meaningful relationships linking structure with chemistry and property in the form of chemical design rules that identifies the exact role of key structure governing factors without any a priori assumptions. The powerful role of data dimensionality reduction, clustering analysis and data mining methods for both classification and prediction of structure-property relationships in materials is discussed. Using examples from two different crystal chemistry families, apatites and perovskite oxides, it is shown how high-dimensional data can be used to assess patterns of behavior as well as establish predictive Quantitative Structure-Activity Relationships (QSARs). A library of potentially new "virtual" stoichiometric and non-stoichiometric apatites and high TC piezoelectric perovskite materials chemistries were identified, thereby reinforcing the value of statistical learning methods in rational materials design.
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…
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…
The automaticity of visual statistical learning.
Turk-Browne, Nicholas B; Jungé, Justin; Scholl, Brian J
2005-11-01
The visual environment contains massive amounts of information involving the relations between objects in space and time, and recent studies of visual statistical learning (VSL) have suggested that this information can be automatically extracted by the visual system. The experiments reported in this article explore the automaticity of VSL in several ways, using both explicit familiarity and implicit response-time measures. The results demonstrate that (a) the input to VSL is gated by selective attention, (b) VSL is nevertheless an implicit process because it operates during a cover task and without awareness of the underlying statistical patterns, and (c) VSL constructs abstracted representations that are then invariant to changes in extraneous surface features. These results fuel the conclusion that VSL both is and is not automatic: It requires attention to select the relevant population of stimuli, but the resulting learning then occurs without intent or awareness. Copyright (c) 2005 APA, all rights reserved.
Statistical quality control for VLSIC fabrication processes
Mozumder, P.K.
1989-01-01
As the complexity of VLSICs increase and the device dimension shrink, random fluctuations become the main reason limiting the par metric yield. Whenever a new process is developed, the initial yield are low. The rate of climbing the learning curve is slow, i.e., the time necessary to bring the yield above an economically acceptable value can be unacceptably long, resulting in lost revenue and competitive edge in the market. The slow rates of climbing the learning curve and the low initial yields can be countered by using design methodologies that take into account the random fluctuations in the fabrication processes, and using statistical on-line and off-line control during the wafer fabrication. An integrated CAD-CAM approach with profit maximization as the objective is necessary to design and fabricate present day VLSICs. In this thesis the author proposes a methodology for monitoring and statistically controlling VLSIC manufacturing processes as part of an integrated CAD-CAM system. Present day statistical quality control systems fail to function satisfactorily due to lack of in-situ and in-line data, and absence of statistical techniques that take into account the multi-dimensionality of the data. A concerted effort has to be made to increase the number of in-situ parameters that are measured during the fabrication process using new generation equipment and sensors. Algorithms for identifying the minimal set of observable in-situ and in-line parameters that have to be measured to monitor the fabrication process are presented. The methodology for statistical quality control is based on the exploration of the multivariate distribution of the observed in-process parameters in the region of acceptability specified by the customer. Criteria for comparing the distributions of the normal process to that of the process under control are used to make the quality control decisions.
Optical Processing Of Statistical Data
NASA Astrophysics Data System (ADS)
Bohm, H.; Lohmann, A. W.; Weigelt, G. P.
1980-08-01
The performance / price ratio of digital data processing is steadily increasing, while the performance / price ratio of optical data processing remains nearly constant, although at a favourable level. Given this trend, one might ask: is there still a future for optical data processing? This question cannot be answered in general, since optical data processing is very competent for some jobs, but clumsy or incapable at other jobs. The category of jobs suitable for optics is characterised by features like: high data rate, large storage requirement, moderate accuracy, repetitive program consisting mainly of linear and quadratic operations. Certain statistical computing problems belong into this category. We will present two examples and analyze their data processing efficiencies. The examples are useful in astro-nomy (speckle interferometry) and in biology (motility studies on bacteria).
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…
Sood, Akshay; Ghani, Khurshid R; Ahlawat, Rajesh; Modi, Pranjal; Abaza, Ronney; Jeong, Wooju; Sammon, Jesse D; Diaz, Mireya; Kher, Vijay; Menon, Mani; Bhandari, Mahendra
2014-08-01
Traditional evaluation of the learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. To prospectively monitor patient safety during the learning phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience (n=7); group 2, robot trained and kidney transplant experienced (n=20); and group 3, kidney transplant experienced with limited robot training (n=14). We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. CUSUM analysis revealed an initial learning phase for group 3, whereas groups 1 and 2 had no to minimal learning time. The learning phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 (p<0.05) secondary to reasons unrelated to RKT. In multivariable analysis, robot training was significantly associated with improved task-completion times (p<0.01). Graft
Dynamics of EEG functional connectivity during statistical learning.
Tóth, Brigitta; Janacsek, Karolina; Takács, Ádám; Kóbor, Andrea; Zavecz, Zsófia; Nemeth, Dezso
2017-10-01
Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning. Copyright © 2017 Elsevier Inc. All rights reserved.
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…
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…
Right hemisphere dominance in visual statistical learning
Roser, Matthew E.; Fiser, József; Aslin, Richard N.; Gazzaniga, Michael S.
2010-01-01
Several studies report a right hemisphere (RH) advantage for visuo-spatial integration and a left hemisphere (LH) 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 multi-shape scenes presented in either the right or left visual fields. Unbeknownst to the participants the scenes were composed from a random combination of fixed pairs of shapes. Subsequent testing found that control participants could discriminate fixed-pair shapes from randomly combined shapes when presented in either visual field. The split-brain patient performed at chance except when both the practice and test displays were presented in the left visual field (RH). These results suggest that the statistical learning of new visual features is dominated by visuospatial processing in the right hemisphere and provide a prediction about how fMRI activation patterns might change during unsupervised statistical learning. PMID:20433243
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.
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.
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
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.
Transforming Graph Data for Statistical Relational Learning
2012-10-01
Other metrics or strategies that could be used include Akaike’s information criterion (AIC) (Akaike, 1974), Mallows Cp ( Mallows , 1973), Bayesian...Machine Learning and Knowledge Discovery in Databases, 5782, 47–62. 434 Transforming Graph Data for Statistical Relational Learning Mallows , C. (1973
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…
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…
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?
Emberson, Lauren L.; Liu, Ran; Zevin, Jason D.
2013-01-01
In order for statistical information to aid in complex developmental processes such as language acquisition, learning from higher-order statistics (e.g. across successive syllables in a speech stream to support segmentation) must be possible while perceptual abilities (e.g. speech categorization) are still developing. The current study examines how perceptual organization interacts with statistical learning. Adult participants were presented with multiple exemplars from novel, complex sound categories designed to reflect some of the spectral complexity and variability of speech. These categories were organized into sequential pairs and presented such that higher-order statistics, defined based on sound categories, could support stream segmentation. Perceptual similarity judgments and multi-dimensional scaling revealed that participants only perceived three perceptual clusters of sounds and thus did not distinguish the four experimenter-defined categories, creating a tension between lower level perceptual organization and higher-order statistical information. We examined whether the resulting pattern of learning is more consistent with statistical learning being “bottom-up,” constrained by the lower levels of organization, or “top-down,” such that higher-order statistical information of the stimulus stream takes priority over the perceptual organization, and perhaps influences perceptual organization. We consistently find evidence that learning is constrained by perceptual organization. Moreover, participants generalize their learning to novel sounds that occupy a similar perceptual space, suggesting that statistical learning occurs based on regions of or clusters in perceptual space. Overall, these results reveal a constraint on learning of sound sequences, such that statistical information is determined based on lower level organization. These findings have important implications for the role of statistical learning in language acquisition. PMID:23618755
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. Copyright © 2013 Elsevier B
Statistical Learning is Related to Early Literacy-Related Skills.
Spencer, Mercedes; Kaschak, Michael P; Jones, John L; Lonigan, Christopher J
2015-04-01
It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one's environment, plays a role in young children's acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from fluent speech and the learning of syntactic structure, some recent studies have explored the extent to which individual differences in statistical learning are related to literacy-relevant knowledge and skills. The present study extends on this literature by investigating the relations between two measures of statistical learning and multiple measures of skills that are critical to the development of literacy-oral language, vocabulary knowledge, and phonological processing-within a single model. Our sample included a total of 553 typically developing children from prekindergarten through second grade. Structural equation modeling revealed that statistical learning accounted for a unique portion of the variance in these literacy-related skills. Practical implications for instruction and assessment are discussed.
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…
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 learning in social action contexts.
Monroy, Claire; Meyer, Marlene; Gerson, Sarah; Hunnius, Sabine
2017-01-01
Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and-if so-whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together ('Joint' condition) or stated the intention to act alone ('Parallel' condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor's action reliably predicted the second actor's action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the
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…
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…
Statistical Learning is Related to Early Literacy-Related Skills
Spencer, Mercedes; Kaschak, Michael P.; Jones, John L.; Lonigan, Christopher J.
2015-01-01
It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one’s environment, plays a role in young children’s acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from fluent speech and the learning of syntactic structure, some recent studies have explored the extent to which individual differences in statistical learning are related to literacy-relevant knowledge and skills. The present study extends on this literature by investigating the relations between two measures of statistical learning and multiple measures of skills that are critical to the development of literacy—oral language, vocabulary knowledge, and phonological processing—within a single model. Our sample included a total of 553 typically developing children from prekindergarten through second grade. Structural equation modeling revealed that statistical learning accounted for a unique portion of the variance in these literacy-related skills. Practical implications for instruction and assessment are discussed. PMID:26478658
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)…
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)…
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.
Learning Predictive Statistics: Strategies and Brain Mechanisms.
Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe
2017-08-30
When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory-motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions.SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to
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
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…
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…
Neger, Thordis M.; Rietveld, Toni; Janse, Esther
2014-01-01
Within a few sentences, listeners learn to understand severely degraded speech such as noise-vocoded speech. However, individuals vary in the amount of such perceptual learning and it is unclear what underlies these differences. The present study investigates whether perceptual learning in speech relates to statistical learning, as sensitivity to probabilistic information may aid identification of relevant cues in novel speech input. If statistical learning and perceptual learning (partly) draw on the same general mechanisms, then statistical learning in a non-auditory modality using non-linguistic sequences should predict adaptation to degraded speech. In the present study, 73 older adults (aged over 60 years) and 60 younger adults (aged between 18 and 30 years) performed a visual artificial grammar learning task and were presented with 60 meaningful noise-vocoded sentences in an auditory recall task. Within age groups, sentence recognition performance over exposure was analyzed as a function of statistical learning performance, and other variables that may predict learning (i.e., hearing, vocabulary, attention switching control, working memory, and processing speed). Younger and older adults showed similar amounts of perceptual learning, but only younger adults showed significant statistical learning. In older adults, improvement in understanding noise-vocoded speech was constrained by age. In younger adults, amount of adaptation was associated with lexical knowledge and with statistical learning ability. Thus, individual differences in general cognitive abilities explain listeners' variability in adapting to noise-vocoded speech. Results suggest that perceptual and statistical learning share mechanisms of implicit regularity detection, but that the ability to detect statistical regularities is impaired in older adults if visual sequences are presented quickly. PMID:25225475
Studies in statistical signal processing
NASA Astrophysics Data System (ADS)
Kailath, Thomas
1990-06-01
The primary objective of our research is to develop efficient and numerically stable algorithms for nonstationary signal processing problems by understanding and exploiting special structures, both deterministic and stochastic, in the problems. We also strive to establish and broaden links with related disciplines, such as cascade filter synthesis, scattering theory, numerical linear algebra, and mathematical operator theory for the purpose of cross fertilization of ideas and techniques. These explorations have led to new results both in estimation theory and in these other fields, e.g., to new algorithms for triangular and QR factorization of structured matrices, new techniques for root location and stability testing, new realizations for multiple-input/multiple-output (MIMO) transfer functions, and new recursions for orthogonal polynomials on the unit circle and the real line as well as on other curves.
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…
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. Copyright © 2013 Elsevier Inc. All rights reserved.
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.
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.
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…
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 predictive statistics from temporal sequences: Dynamics and strategies.
Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe
2017-10-01
Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.
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.
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.
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.
Teaching MBA Statistics Online: A Pedagogically Sound Process Approach
ERIC Educational Resources Information Center
Grandzol, John R.
2004-01-01
Delivering MBA statistics in the online environment presents significant challenges to education and students alike because of varying student preparedness levels, complexity of content, difficulty in assessing learning outcomes, and faculty availability and technological expertise. In this article, the author suggests a process model that…
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…
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…
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
Intact implicit statistical learning in borderline personality disorder.
Unoka, Zsolt; Vizin, Gabriella; Bjelik, Anna; Radics, Dóra; Nemeth, Dezso; Janacsek, Karolina
2017-09-01
Wide-spread neuropsychological deficits have been identified in borderline personality disorder (BPD). Previous research found impairments in decision making, declarative memory, working memory and executive functions; however, no studies have focused on implicit learning in BPD yet. The aim of our study was to investigate implicit statistical learning by comparing learning performance of 19 BPD patients and 19 healthy, age-, education- and gender-matched controls on a probabilistic sequence learning task. Moreover, we also tested whether participants retain the acquired knowledge after a delay period. To this end, participants were retested on a shorter version of the same task 24h after the learning phase. We found intact implicit statistical learning as well as retention of the acquired knowledge in this personality disorder. BPD patients seem to be able to extract and represent regularities implicitly, which is in line with the notion that implicit learning is less susceptible to illness compared to the more explicit processes. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.
Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials
ERIC Educational Resources Information Center
Potter, Christine E.; Wang, Tianlin; Saffran, Jenny R.
2017-01-01
Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning…
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
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.
Memory constraints on infants’ cross-situational statistical learning
Vlach, Haley A.; Johnson, Scott P.
2014-01-01
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 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. PMID:23545387
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. Published by Elsevier B.V.
Statistical Machine Learning for Structured and High Dimensional Data
2014-09-17
AFRL-OSR-VA-TR-2014-0234 STATISTICAL MACHINE LEARNING FOR STRUCTURED AND HIGH DIMENSIONAL DATA Larry Wasserman CARNEGIE MELLON UNIVERSITY Final...Re . 8-98) v Prescribed by ANSI Std. Z39.18 14-06-2014 Final Dec 2009 - Aug 2014 Statistical Machine Learning for Structured and High Dimensional...area of resource-constrained statistical estimation. machine learning , high-dimensional statistics U U U UU John Lafferty 773-702-3813 > Research under
Implicit statistical learning and language skills in bilingual children.
Yim, Dongsun; Rudoy, John
2013-02-01
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. Implicit statistical learning was examined in visual and auditory domains. One hundred twelve English native speaking monolinguals and Spanish-English bilinguals age 5-13 years participated in the study. Language skills were measured by standardized language tests. The overall results showed that all children implicitly learned statistical regularities above chance level in both modalities. However, there was no group difference between monolingual and bilingual children on either visual or auditory tasks. Lastly, a different tendency in predicting implicit statistical learning was observed for each group. In the monolingual group, both age and language scores significantly explained auditory statistical learning, whereas age explained visual statistical learning. In the bilingual group, age explained auditory statistical learning, and nothing was significant for visual statistical learning. These findings are discussed in terms of the extent to which implicit statistical learning is influenced by internal and external factors and a consideration of important notions when testing bilingual children's language skills.
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
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.…
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.…
Statistical process control for total quality
NASA Astrophysics Data System (ADS)
Ali, Syed W.
1992-06-01
The paper explains the techniques and applications of statistical process control (SPC). Examples of control charts used in the Poseidon program of the NASA ocean topography experiment (TOPEX) and a brief discussion of Taguchi methods are presented. It is noted that SPC involves everyone in process improvement by providing objective, workable data. It permits continuous improvement instead of merely aiming for all parts to be within a tolerance band.
Highly automated nonparametric statistical learning for autonomous target recognition
NASA Astrophysics Data System (ADS)
Drake, Keith C.
1992-04-01
Image pattern recognition is presented as three sequential tasks: feature extraction, object plausibility estimation (determining class likelihoods), and decision processing. Several data- driven techniques yield discriminant functions to produce object plausibility estimates from image features, including traditional statistical methods and neural network approaches. A statistical learning algorithm which integrates multiple-regression algorithms, functional networking strategies, and a statistical modeling criterion is presented. It provides a non- parametric learning algorithm for the synthesis of discriminant functions. Image understanding tasks such as object plausibility estimation require robust modeling techniques to deal with the uncertainty prevalent in real-world data. Specifically, these complex tasks require robust and cost-effective techniques to successfully integrate multi-source information. AbTech and others have shown that implementation of the statistical learning concepts discussed provide a modeling approach ideal for information fusion tasks such as autonomous object recognition for tactical targets and space-based assets. The results of using this approach to develop a prototype aircraft recognition system is presented.
The Link between Statistical Segmentation and Word Learning in Adults
ERIC Educational Resources Information Center
Mirman, Daniel; Magnuson, James S.; Estes, Katharine Graf; Dixon, James A.
2008-01-01
Many studies have shown that listeners can segment words from running speech based on conditional probabilities of syllable transitions, suggesting that this statistical learning could be a foundational component of language learning. However, few studies have shown a direct link between statistical segmentation and word learning. We examined this…
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…
ERIC Educational Resources Information Center
Olsen, Jennifer; Aleven, Vincent; Rummel, Nikol
2017-01-01
Within educational data mining, many statistical models capture the learning of students working individually. However, not much work has been done to extend these statistical models of individual learning to a collaborative setting, despite the effectiveness of collaborative learning activities. We extend a widely used model (the additive factors…
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…
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
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.
Predicting radiotherapy outcomes using statistical learning techniques*
El Naqa, Issam; Bradley, Jeffrey D; Lindsay, Patricia E; Hope, Andrew J; Deasy, Joseph O
2013-01-01
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
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
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. Copyright © 2014 Cognitive Science Society, Inc.
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
Turk-Browne, Nicholas B; Scholl, Brian J; Chun, Marvin M; Johnson, Marcia K
2009-10-01
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.
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.
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.
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…
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…
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.
Case-based statistical learning applied to SPECT image classification
NASA Astrophysics Data System (ADS)
Górriz, Juan M.; Ramírez, Javier; Illán, I. A.; Martínez-Murcia, Francisco J.; Segovia, Fermín.; Salas-Gonzalez, Diego; Ortiz, A.
2017-03-01
Statistical learning and decision theory play a key role in many areas of science and engineering. Some examples include time series regression and prediction, optical character recognition, signal detection in communications or biomedical applications for diagnosis and prognosis. This paper deals with the topic of learning from biomedical image data in the classification problem. In a typical scenario we have a training set that is employed to fit a prediction model or learner and a testing set on which the learner is applied to in order to predict the outcome for new unseen patterns. Both processes are usually completely separated to avoid over-fitting and due to the fact that, in practice, the unseen new objects (testing set) have unknown outcomes. However, the outcome yields one of a discrete set of values, i.e. the binary diagnosis problem. Thus, assumptions on these outcome values could be established to obtain the most likely prediction model at the training stage, that could improve the overall classification accuracy on the testing set, or keep its performance at least at the level of the selected statistical classifier. In this sense, a novel case-based learning (c-learning) procedure is proposed which combines hypothesis testing from a discrete set of expected outcomes and a cross-validated classification stage.
The role of partial knowledge in statistical word learning.
Yurovsky, Daniel; Fricker, Damian C; Yu, Chen; Smith, Linda B
2014-02-01
A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word's meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: partial knowledge of one word-object mapping can speed up the acquisition of other word-object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word-object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word's meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data.
The role of partial knowledge in statistical word learning
Fricker, Damian C.; Yu, Chen; Smith, Linda B.
2013-01-01
A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word's meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: Partial knowledge of one word–object mapping can speed up the acquisition of other word–object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word–object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word's meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data. PMID:23702980
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…
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…
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.
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.
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…
Statistical Learning and Language: An Individual Differences Study
ERIC Educational Resources Information Center
Misyak, Jennifer B.; Christiansen, Morten H.
2012-01-01
Although statistical learning and language have been assumed to be intertwined, this theoretical presupposition has rarely been tested empirically. The present study investigates the relationship between statistical learning and language using a within-subject design embedded in an individual-differences framework. Participants were administered…
Statistical Learning of 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…
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…
College Students' Theory of Learning Introductory Statistics: Phase One.
ERIC Educational Resources Information Center
Oathout, Margaret J.
Impediments that hamper learning statistics, the relative characteristics of successful and unsuccessful learners, and the nature of interventions required to make learning statistics successful and meaningful were studied. Twenty graduate and 15 undergraduate students in different fields from a large public university, a small private college,…
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…
Two Heads Are Better Than One: Learning Statistics in Common.
ERIC Educational Resources Information Center
Dunn, Dana S.
Students should not learn statistical concepts in isolation; statistics and data analysis invite conversations concerning which analysis to use and why, what was found and why, and what results mean and why. To emphasize the importance of this learning in common, a college teacher requires students to collaborate on research projects from…
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.
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.
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 management: An essential element of quality improvement
NASA Astrophysics Data System (ADS)
Buckner, M. R.
Successful quality improvement requires a balanced program involving the three elements that control quality: organization, people and technology. The focus of the SPC/SPM User's Group is to advance the technology component of Total Quality by networking within the Group and by providing an outreach within Westinghouse to foster the appropriate use of statistic techniques to achieve Total Quality. SPM encompasses the disciplines by which a process is measured against its intrinsic design capability, in the face of measurement noise and other obscuring variability. SPM tools facilitate decisions about the process that generated the data. SPM deals typically with manufacturing processes, but with some flexibility of definition and technique it accommodates many administrative processes as well. The techniques of SPM are those of Statistical Process Control, Statistical Quality Control, Measurement Control, and Experimental Design. In addition, techniques such as job and task analysis, and concurrent engineering are important elements of systematic planning and analysis that are needed early in the design process to ensure success. The SPC/SPM User's Group is endeavoring to achieve its objectives by sharing successes that have occurred within the member's own Westinghouse department as well as within other US and foreign industry. In addition, failures are reviewed to establish lessons learned in order to improve future applications. In broader terms, the Group is interested in making SPM the accepted way of doing business within Westinghouse.
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.
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…
iMinerva: A Mathematical Model of Distributional Statistical Learning
ERIC Educational Resources Information Center
Thiessen, Erik D.; Pavlik, Philip I., Jr.
2013-01-01
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…
Some Variables in Relation to Students' Anxiety in Learning Statistics.
ERIC Educational Resources Information Center
Sutarso, Toto
The purpose of this study was to investigate some variables that relate to students' anxiety in learning statistics. The variables included sex, class level, students' achievement, school, mathematical background, previous statistics courses, and race. The instrument used was the 24-item Students' Attitudes Toward Statistics (STATS), which was…
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…
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…
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…
Enhanced visual statistical learning in adults with autism
Roser, Matthew E.; Aslin, Richard N.; McKenzie, Rebecca; Zahra, Daniel; Fiser, József
2014-01-01
Individuals with autism spectrum disorder (ASD) are often characterized as having social engagement and language deficiencies, but a sparing of visuo-spatial processing and short-term memory, with some evidence of supra-normal levels of performance in these domains. The present study expanded on this evidence by investigating the observational learning of visuospatial concepts from patterns of covariation across multiple exemplars. Child and adult participants with ASD, and age-matched control participants, viewed multi-shape arrays composed from a random combination of pairs of shapes that were each positioned in a fixed spatial arrangement. After this passive exposure phase, a post-test revealed that all participant groups could discriminate pairs of shapes with high covariation from randomly paired shapes with low covariation. Moreover, learning these shape-pairs with high covariation was superior in adults with ASD than in age-matched controls, while performance in children with ASD was no different than controls. These results extend previous observations of visuospatial enhancement in ASD into the domain of learning, and suggest that enhanced visual statistical learning may have arisen from a sustained bias to attend to local details in complex arrays of visual features. PMID:25151115
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.
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
Changing viewer perspectives reveals constraints to implicit visual statistical learning.
Jiang, Yuhong V; Swallow, Khena M
2014-10-07
Statistical learning-learning environmental regularities to guide behavior-likely plays an important role in natural human behavior. One potential use is in search for valuable items. Because visual statistical learning can be acquired quickly and without intention or awareness, it could optimize search and thereby conserve energy. For this to be true, however, visual statistical learning needs to be viewpoint invariant, facilitating search even when people walk around. To test whether implicit visual statistical learning of spatial information is viewpoint independent, we asked participants to perform a visual search task from variable locations around a monitor placed flat on a stand. Unbeknownst to participants, the target was more often in some locations than others. In contrast to previous research on stationary observers, visual statistical learning failed to produce a search advantage for targets in high-probable regions that were stable within the environment but variable relative to the viewer. This failure was observed even when conditions for spatial updating were optimized. However, learning was successful when the rich locations were referenced relative to the viewer. We conclude that changing viewer perspective disrupts implicit learning of the target's location probability. This form of learning shows limited integration with spatial updating or spatiotopic representations. © 2014 ARVO.
Statistical signal processing in sensor networks
NASA Astrophysics Data System (ADS)
Guerriero, Marco
approach to overcoming the difficulties in large-sensor surveillance, and we illustrate promising performance results with simulated surveillance data. The third topic of this dissertation deals with distributed target detection in SN using Scan Statistics. We introduce a sequential procedure to detect a target with distributed sensors in a two dimensional region. The detection is carried out in a mobile fusion center which successively counts the number of binary decisions reported by local sensors lying inside its moving field of view. This is a two-dimensional scan statistic an emerging tool from the statistics field that has been applied to a variety of anomaly detection problems such as of epidemics or computer intrusion, but that seems to be unfamiliar to the signal processing community. We show that an optimal size of the field of view exists. We compare the sequential two-dimensional scan statistic test and two other tests. We also present results for system level detection. In the last topic we study a Repeated Significance Test (RST) with applications to sequential detection in SN. We introduce a randomly truncated sequential hypothesis test. Using the framework of a RST, we study a sequential test with truncation time based on a random stopping time. Using the Functional Central Limit Theorem (FCLT) for a sequence of statistics, we derive a general result that can be employed in developing a repeated significance test with random sample size. We present effective methods for evaluating accurate approximations for the probability of type I error and the power function. Numerical results are presented to evaluate the accuracy of these approximations. We apply the proposed test to a decentralized sequential detection in sensor networks (SN) with communication constraints. Finally a sequential detection problem with measurements at random times is investigated.
Otsuka, Sachio; Saiki, Jun
2016-02-01
Prior studies have shown that visual statistical learning (VSL) enhances familiarity (a type of memory) of sequences. How do statistical regularities influence the processing of each triplet element and inserted distractors that disrupt the regularity? Given that increased attention to triplets induced by VSL and inhibition of unattended triplets, we predicted that VSL would promote memory for each triplet constituent, and degrade memory for inserted stimuli. Across the first two experiments, we found that objects from structured sequences were more likely to be remembered than objects from random sequences, and that letters (Experiment 1) or objects (Experiment 2) inserted into structured sequences were less likely to be remembered than those inserted into random sequences. In the subsequent two experiments, we examined an alternative account for our results, whereby the difference in memory for inserted items between structured and random conditions is due to individuation of items within random sequences. Our findings replicated even when control letters (Experiment 3A) or objects (Experiment 3B) were presented before or after, rather than inserted into, random sequences. Our findings suggest that statistical learning enhances memory for each item in a regular set and impairs memory for items that disrupt the regularity. Copyright © 2015 Elsevier B.V. All rights reserved.
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
Statistical process control for electron beam monitoring.
López-Tarjuelo, Juan; Luquero-Llopis, Naika; García-Mollá, Rafael; Quirós-Higueras, Juan David; Bouché-Babiloni, Ana; Juan-Senabre, Xavier Jordi; de Marco-Blancas, Noelia; Ferrer-Albiach, Carlos; Santos-Serra, Agustín
2015-07-01
To assess the electron beam monitoring statistical process control (SPC) in linear accelerator (linac) daily quality control. We present a long-term record of our measurements and evaluate which SPC-led conditions are feasible for maintaining control. We retrieved our linac beam calibration, symmetry, and flatness daily records for all electron beam energies from January 2008 to December 2013, and retrospectively studied how SPC could have been applied and which of its features could be used in the future. A set of adjustment interventions designed to maintain these parameters under control was also simulated. All phase I data was under control. The dose plots were characterized by rising trends followed by steep drops caused by our attempts to re-center the linac beam calibration. Where flatness and symmetry trends were detected they were less-well defined. The process capability ratios ranged from 1.6 to 9.3 at a 2% specification level. Simulated interventions ranged from 2% to 34% of the total number of measurement sessions. We also noted that if prospective SPC had been applied it would have met quality control specifications. SPC can be used to assess the inherent variability of our electron beam monitoring system. It can also indicate whether a process is capable of maintaining electron parameters under control with respect to established specifications by using a daily checking device, but this is not practical unless a method to establish direct feedback from the device to the linac can be devised. Copyright © 2015 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
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…
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…
Statistically optimal perception and learning: from behavior to neural representations
Fiser, József; Berkes, Pietro; Orbán, Gergő; Lengyel, Máté
2010-01-01
Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and reevaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty. PMID:20153683
Conway, Christopher M; Christiansen, Morten H
2006-10-01
When learners encode sequential patterns and generalize their knowledge to novel instances, are they relying on abstract or stimulus-specific representations? Research on artificial grammar learning (AGL) has shown transfer of learning from one stimulus set to another, and such findings have encouraged the view that statistical learning is mediated by abstract representations that are independent of the sense modality or perceptual features of the stimuli. Using a novel modification of the standard AGL paradigm, we obtained data to the contrary. These experiments pitted abstract processing against stimulus-specific learning. The findings show that statistical learning results in knowledge that is stimulus-specific rather than abstract. They show furthermore that learning can proceed in parallel for multiple input streams along separate perceptual dimensions or sense modalities. We conclude that learning sequential structure and generalizing to novel stimuli inherently involve learning mechanisms that are closely tied to the perceptual characteristics of the input.
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.
ERIC Educational Resources Information Center
Peters, Susan A.
2014-01-01
This retrospective phenomenological study investigates activities and actions identified by secondary statistics teachers who exhibit robust understandings of variation as deepening their understandings of statistical variation. Using phenomenological methods and a frame of Mezirow's transformation theory, analysis revealed learning factors…
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…
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…
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…
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…
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.
Problems with visual statistical learning in developmental dyslexia.
Sigurdardottir, Heida Maria; Danielsdottir, Hilda Bjork; Gudmundsdottir, Margret; Hjartarson, Kristjan Helgi; Thorarinsdottir, Elin Astros; Kristjánsson, Árni
2017-04-04
Previous research shows that dyslexic readers are impaired in their recognition of faces and other complex objects, and show hypoactivation in ventral visual stream regions that support word and object recognition. Responses of these brain regions are shaped by visual statistical learning. If such learning is compromised, people should be less sensitive to statistically likely feature combinations in words and other objects, and impaired visual word and object recognition should be expected. We therefore tested whether people with dyslexia showed diminished capability for visual statistical learning. Matched dyslexic and typical readers participated in tests of visual statistical learning of pairs of novel shapes that frequently appeared together. Dyslexic readers on average recognized fewer pairs than typical readers, indicating some problems with visual statistical learning. These group differences were not accounted for by differences in intelligence, ability to remember individual shapes, or spatial attention paid to the stimuli, but other attentional problems could play a mediating role. Deficiencies in visual statistical learning may in some cases prevent appropriate experience-driven shaping of neuronal responses in the ventral visual stream, hampering visual word and object recognition.
Statistical Methods in Ai: Rare Event Learning Using Associative Rules and Higher-Order Statistics
NASA Astrophysics Data System (ADS)
Iyer, V.; Shetty, S.; Iyengar, S. S.
2015-07-01
Rare event learning has not been actively researched since lately due to the unavailability of algorithms which deal with big samples. The research addresses spatio-temporal streams from multi-resolution sensors to find actionable items from a perspective of real-time algorithms. This computing framework is independent of the number of input samples, application domain, labelled or label-less streams. A sampling overlap algorithm such as Brooks-Iyengar is used for dealing with noisy sensor streams. We extend the existing noise pre-processing algorithms using Data-Cleaning trees. Pre-processing using ensemble of trees using bagging and multi-target regression showed robustness to random noise and missing data. As spatio-temporal streams are highly statistically correlated, we prove that a temporal window based sampling from sensor data streams converges after n samples using Hoeffding bounds. Which can be used for fast prediction of new samples in real-time. The Data-cleaning tree model uses a nonparametric node splitting technique, which can be learned in an iterative way which scales linearly in memory consumption for any size input stream. The improved task based ensemble extraction is compared with non-linear computation models using various SVM kernels for speed and accuracy. We show using empirical datasets the explicit rule learning computation is linear in time and is only dependent on the number of leafs present in the tree ensemble. The use of unpruned trees (t) in our proposed ensemble always yields minimum number (m) of leafs keeping pre-processing computation to n × t log m compared to N2 for Gram Matrix. We also show that the task based feature induction yields higher Qualify of Data (QoD) in the feature space compared to kernel methods using Gram Matrix.
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…
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…
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…
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…
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…
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…
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…
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…
Challenges to Faithful Learning and Teaching: The Case of Statistics
ERIC Educational Resources Information Center
Mvududu, Nyaradzo
2007-01-01
Statistics, and more generally mathematics, have been neglected in Christian scholarship on faithful learning and teaching. This subject has been generally classified in the "some areas are harder than others when it comes to making connections with faith" category. If statistics is more than just a technical tool and is in fact a way of thinking,…
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…
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…
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…
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…
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;…
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
Cross-Situational Statistical Word Learning in Young Children
Suanda, Sumarga H.; Mugwanya, Nassali; Namy, Laura L.
2014-01-01
Recent empirical work has highlighted the potential role of cross-situational statistical word learning in children's early vocabulary development. In the current study, we tested five-to seven-year-old children's cross-situational learning by presenting children with a series of ambiguous naming events containing multiple words and multiple referents. Children rapidly learned word-to-object mappings by attending to the co-occurrence regularities across these ambiguous naming events. The current study begins to address the mechanisms underlying children's learning by demonstrating that the diversity of learning contexts impacts performance. The implications of the current findings for the role of cross-situational word learning at different points in development are discussed along with the methodological implications of employing school-aged children to test hypotheses regarding the mechanisms supporting early word learning. PMID:25015421
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…
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…
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.
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
Competitive Processes in Cross-Situational Word Learning
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. But the information that learners pick up from these regularities is dependent on their learning mechanism. This paper investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input, and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple scales – both within and across trials/situations –learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is then considered from the perspective of a process-level understanding of cross-situational learning. PMID:23607610
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.
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 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…
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…
Isolated words enhance statistical language learning in infancy
Lew-Williams, Casey; Pelucchi, Bruna; Saffran, Jenny R.
2012-01-01
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 reported here embraced a key dimension of variability in infant-directed speech. English-learning infants (8–10 months) listened briefly to natural Italian speech that contained either fluent speech only or a combination of fluent speech and single-word utterances. Listening times revealed successful learning of the statistical properties of target words only when words appeared both in fluent speech and in isolation; brief exposure to fluent speech alone was not sufficient to facilitate detection of the words’ statistical properties. This investigation suggests that statistical learning mechanisms actually benefit from variability in utterance length, and provides the first evidence that isolated words and longer utterances act in concert to support infant word segmentation. PMID:22010892
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.
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.
Linking Sounds to Meanings: Infant Statistical Learning in a Natural Language
ERIC Educational Resources Information Center
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…
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
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.
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. (c) 2011 APA, all rights reserved.
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…
The Acquisition of Allophonic Rules: Statistical Learning with Linguistic Constraints
ERIC Educational Resources Information Center
Peperkamp, Sharon; Le Calvez, Rozenn; Nadal, Jean-Pierre; Dupoux, Emmanuel
2006-01-01
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…
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…
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)…
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
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.
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.
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
Understanding evaluation of learning support in mathematics and statistics
NASA Astrophysics Data System (ADS)
MacGillivray, Helen; Croft, Tony
2011-03-01
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 challenges for measurement and analysis of its effects. After briefly reviewing the purpose, rationale for, and extent of current provision, this article provides a framework for those working in learning support to think about how their efforts can be evaluated. It provides references and specific examples of how workers in this field are collecting, analysing and reporting their findings. The framework is used to structure evaluation in terms of usage of facilities, resources and services provided, and also in terms of improvements in performance of the students and staff who engage with them. Very recent developments have started to address the effects of learning support on the development of deeper approaches to learning, the affective domain and the development of communities of practice of both learners and teachers. This article intends to be a stimulus to those who work in mathematics and statistics support to gather even richer, more valuable, forms of data. It provides a 'toolkit' for those interested in evaluation of learning support and closes by referring to an on-line resource being developed to archive the growing body of evidence.
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
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
USDA-ARS?s Scientific Manuscript database
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...
On-line Assessment of Statistical Learning by Event-related Potentials.
Abla, Dilshat; Katahira, Kentaro; Okanoya, Kazuo
2008-06-01
Abstract We investigated the neural processes involved in on-line statistical learning and word segmentation. Auditory event-related potentials (ERPs) were recorded while participants were exposed to continuous, nonlinguistic auditory sequences, the elements of which were organized into "tritone words" that were sequenced in random order, with no silent spaces between them. After listening to three 6.6-min sessions of sequences, the participants performed a behavioral choice test, in which they were instructed to indicate the most familiar tone sequence in each test trial by pressing buttons. The participants were divided into three groups (high, middle, and low learners) based on their behavioral performance. The overall mean performance was 74.4%, indicating that the tone sequence was segmented and that the participants learned the tone words statistically. Grand-averaged ERPs showed that word onset (initial tone) elicited the largest N100 and N400 in the early learning session of high learners, but in middle learners, the word-onset effect was elicited in a later session, and there was no effect in low learners. The N400 amplitudes significantly differed between the three learning sessions in the high- and middle-learner groups. The results suggest that the N400 effect indicates not only on-line word segmentation but also the degree of statistical learning. This study provides insight into the neural mechanisms underlying on-line statistical learning processes.
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
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
Statistical kinetics of processive molecular motors
NASA Astrophysics Data System (ADS)
Schnitzer, Mark Jacob
1999-10-01
We describe new theoretical and experimental tools for studying biological motor proteins at the single molecule scale. These tools enable measurements of molecular fuel economies, thereby providing insight into the pathways for conversion of biochemical energy into mechanical work. Kinesin is an ATP-dependent motor that moves processively along microtubules in discrete steps of 8 nm. How many molecules of ATP are hydrolysed per step? To determine this coupling ratio, we develop a fluctuation analysis, which relates the variance in records of mechanical displacement to the number of rate-limiting biochemical transitions in the engine cycle. Using fluctuation analysis and optical trapping interferometry, we determine that near zero load, single molecules of kinesin hydrolyse one ATP nucleotide per 8-nm step. To study kinesin behavior under load, we use a molecular force clamp, capable of maintaining constant loads on single kinesin motors moving processively. Analysis of records of motion under variable ATP concentrations and loads reveals that kinesin is a `tightly- coupled' motor, maintaining the 1:1 coupling ratio up to loads of ~ 5 pN. Moreover, a Michaelis-Menten analysis of velocity shows that the kinesin cycle contains at least two load- dependent transitions. The rate of one of these transitions affects ATP affinity, while the other does not. Therefore, the kinesin stall force must depend on the ATP concentration, as is demonstrated experimentally. These findings rule out existing theoretical models of kinesin motility. We develop a simple theoretical formalism describing a tightly-coupled mechanism for movement. This `energy-landscape' formalism quantitatively accounts for motile properties of RNA polymerase (RNAP), the enzyme that transcribes DNA into RNA. The shapes of RNAP force-velocity curves indicate that biochemical steps limiting transcription rates at low loads do not generate movement. Modeling suggests that high loads may halt RNAP by promoting a
Using Statistical Process Control to Enhance Student Progression
ERIC Educational Resources Information Center
Hanna, Mark D.; Raichura, Nilesh; Bernardes, Ednilson
2012-01-01
Public interest in educational outcomes has markedly increased in the most recent decade; however, quality management and statistical process control have not deeply penetrated the management of academic institutions. This paper presents results of an attempt to use Statistical Process Control (SPC) to identify a key impediment to continuous…
Statistical learning as a basis for social understanding in children.
Ruffman, Ted; Taumoepeau, Mele; Perkins, Chris
2012-03-01
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 also consider which skills and insights are likely to be innate, and why it is difficult to say exactly when children understand mental states as opposed to behaviours. © 2011 The British Psychological Society.
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'.
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
New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes.
Zhao, Ying-Qi; Zeng, Donglin; Laber, Eric B; Kosorok, Michael R
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.
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.
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…
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…
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…
Challenges in identifying asthma subgroups using unsupervised statistical learning techniques.
Prosperi, Mattia C F; Sahiner, Umit M; Belgrave, Danielle; Sackesen, Cansin; Buchan, Iain E; Simpson, Angela; Yavuz, Tolga S; Kalayci, Omer; Custovic, Adnan
2013-12-01
Unsupervised statistical learning techniques, such as exploratory factor analysis (EFA) and hierarchical clustering (HC), have been used to identify asthma phenotypes, with partly consistent results. Some of the inconsistency is caused by the variable selection and demographic and clinical differences among study populations. To investigate the effects of the choice of statistical method and different preparations of data on the clustering results; and to relate these to disease severity. Several variants of EFA and HC were applied and compared using various sets of variables and different encodings and transformations within a dataset of 383 children with asthma. Variables included lung function, inflammatory and allergy markers, family history, environmental exposures, and medications. Clusters and original variables were related to asthma severity (logistic regression and Bayesian network analysis). EFA identified five components (eigenvalues ≥ 1) explaining 35% of the overall variance. Variations of the HC (as linkage-distance functions) did not affect the cluster inference; however, using different variable encodings and transformations did. The derived clusters predicted asthma severity less than the original variables. Prognostic factors of severity were medication usage, current symptoms, lung function, paternal asthma, body mass index, and age of asthma onset. Bayesian networks indicated conditional dependence among variables. The use of different unsupervised statistical learning methods and different variable sets and encodings can lead to multiple and inconsistent subgroupings of asthma, not necessarily correlated with severity. The search for asthma phenotypes needs more careful selection of markers, consistent across different study populations, and more cautious interpretation of results from unsupervised learning.
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
Statistical learning analysis in neuroscience: aiming for transparency.
Hanke, Michael; Halchenko, Yaroslav O; Haxby, James V; Pollmann, Stefan
2010-01-01
Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires "neuroscience-aware" technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities.
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.
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.
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.
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. PMID:23555797
Rohrmeier, Martin A; Cross, Ian
2014-07-01
Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.
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)
Difficulties in learning and teaching statistics: teacher views
NASA Astrophysics Data System (ADS)
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 semi-structured interview technique was applied in the research. In accordance with the aim, teacher opinions about the statistics subjects were examined and analysed. Similar responses from the teachers were grouped and evaluated. The teachers stated that it was positive that middle school statistics subjects were taught gradually in every grade but some difficulties were experienced in the teaching of this subject. The findings are presented in eight themes which are context, sample, data representation, central tendency and dispersion measure, probability, variance, and other difficulties.
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…
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 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…
Content, Affective, and Behavioral Challenges to Learning: Students' Experiences Learning Statistics
ERIC Educational Resources Information Center
McGrath, April L.
2014-01-01
This study examined the experiences of and challenges faced by students when completing a statistics course. As part of the requirement for this course, students completed a learning check-in, which consisted of an individual meeting with the instructor to discuss questions and the completion of a learning reflection and study plan. Forty…
Learning by Invention: Small Group Discussion Activities That Support Learning in Statistics
ERIC Educational Resources Information Center
Jarosz, Andrew F.; Goldenberg, Olga; Wiley, Jennifer
2017-01-01
Learning by invention is an alternative approach to teaching statistics where students are tasked with attempting to solve a problem before being taught the canonical formula for solving it, often resulting in increased understanding of material compared with traditional instruction. The first study, conducted in a college statistics classroom…
ERIC Educational Resources Information Center
Apfelbaum, Keith S.; Hazeltine, Eliot; McMurray, Bob
2013-01-01
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…
Applying Statistical Process Quality Control Methodology to Educational Settings.
ERIC Educational Resources Information Center
Blumberg, Carol Joyce
A subset of Statistical Process Control (SPC) methodology known as Control Charting is introduced. SPC methodology is a collection of graphical and inferential statistics techniques used to study the progress of phenomena over time. The types of control charts covered are the null X (mean), R (Range), X (individual observations), MR (moving…
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…
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…
Are the Products of Statistical Learning Abstract or Stimulus-Specific?
Vouloumanos, Athena; Brosseau-Liard, Patricia E.; Balaban, Evan; Hager, Alanna D.
2012-01-01
Learners can segment potential lexical units from syllable streams when statistically variable transitional probabilities between adjacent syllables are the only cues to word boundaries. Here we examine the nature of the representations that result from statistical learning by assessing learners’ ability to generalize across acoustically different stimuli. In three experiments, we compare two possibilities: that the products of statistical segmentation processes are abstract and generalizable representations, or, alternatively, that products of statistical learning are stimulus-bound and restricted to perceptually similar instances. In Experiment 1, learners segmented units from statistically predictable streams, and recognized these units when they were acoustically transformed by temporal reversals. In Experiment 2, learners were able to segment units from temporally reversed syllable streams, but were only able to generalize in conditions of mild acoustic transformation. In Experiment 3, learners were able to recognize statistically segmented units after a voice change but were unable to do so when the novel voice was mildly distorted. Together these results suggest that representations that result from statistical learning can be abstracted to some degree, but not in all listening conditions. PMID:22470357
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.
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.
The influence of bilingualism on statistical word learning.
Poepsel, Timothy J; Weiss, Daniel J
2016-07-01
Statistical learning is a fundamental component of language acquisition, yet to date, relatively few studies have examined whether these abilities differ in bilinguals. In the present study, we examine this issue by comparing English monolinguals with Chinese-English and English-Spanish bilinguals in a cross-situational statistical learning (CSSL) task. In Experiment 1, we assessed the ability of both monolinguals and bilinguals on a basic CSSL task that contained only one-to-one mappings. In Experiment 2, learners were asked to form both one-to-one and two-to-one mappings, and were tested at three points during familiarization. Overall, monolinguals and bilinguals did not differ in their learning of one-to-one mappings. However, bilinguals more quickly acquired two-to-one mappings, while also exhibiting greater proficiency than monolinguals. We conclude that the fundamental SL mechanism may not be affected by language experience, in accord with previous studies. However, when the input contains greater variability, bilinguals may be more prone to detecting the presence of multiple structures. Copyright © 2016 Elsevier B.V. All rights reserved.
ERIC Educational Resources Information Center
Averitt, Sallie D.
This instructor guide, which was developed for use in a manufacturing firm's advanced technical preparation program, contains the materials required to present a learning module that is designed to prepare trainees for the program's statistical process control module by improving their basic math skills and instructing them in basic calculator…
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…
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 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
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.
Statistical learning of speech, not music, in congenital amusia.
Peretz, Isabelle; Saffran, Jenny; Schön, Daniele; Gosselin, Nathalie
2012-04-01
The acquisition of both speech and music uses general principles: learners extract statistical regularities present in the environment. Yet, individuals who suffer from congenital amusia (commonly called tone-deafness) have experienced lifelong difficulties in acquiring basic musical skills, while their language abilities appear essentially intact. One possible account for this dissociation between music and speech is that amusics lack normal experience with music. If given appropriate exposure, amusics might be able to acquire basic musical abilities. To test this possibility, a group of 11 adults with congenital amusia, and their matched controls, were exposed to a continuous stream of syllables or tones for 21-minute. Their task was to try to identify three-syllable nonsense words or three-tone motifs having an identical statistical structure. The results of five experiments show that amusics can learn novel words as easily as controls, whereas they systematically fail on musical materials. Thus, inappropriate musical exposure cannot fully account for the musical disorder. Implications of the results for the domain specificity of statistical learning are discussed. © 2012 New York Academy of Sciences.
Statistical learning of speech, not music, in congenital amusia
Peretz, Isabelle; Saffran, Jenny; Schön, Daniele; Gosselin, Nathalie
2013-01-01
The acquisition of both speech and music uses general principles: learners extract statistical regularities present in the environment. Yet, individuals who suffer from congenital amusia (commonly called tone-deafness) have experienced lifelong difficulties in acquiring basic musical skills, while their language abilities appear essentially intact. One possible account for this dissociation between music and speech is that amusics lack normal experience with music. If given appropriate exposure, amusics might be able to acquire basic musical abilities. To test this possibility, a group of 11 adults with congenital amusia, and their matched controls, were exposed to a continuous stream of syllables or tones for 21-minute. Their task was to try to identify three-syllable nonsense words or three-tone motifs having an identical statistical structure. The results of five experiments show that amusics can learn novel words as easily as controls, whereas they systematically fail on musical materials. Thus, inappropriate musical exposure cannot fully account for the musical disorder. Implications of the results for the domain specificity of statistical learning are discussed. PMID:22524380
Multivariate Analysis and Statistics in Pharmaceutical Process Research and Development.
Tabora, José E; Domagalski, Nathan
2017-06-07
The application of statistics in pharmaceutical process research and development has evolved significantly over the past decades, motivated in part by the introduction of the Quality by Design paradigm, a landmark change in regulatory expectations for the level of scientific understanding associated with the manufacturing process. Today, statistical methods are increasingly applied to accelerate the characterization and optimization of new drugs created via numerous unit operations well known to the chemical engineering discipline. We offer here a review of the maturity in the implementation of design of experiment techniques, the increased incorporation of latent variable methods in process and material characterization, and the adoption of Bayesian methodology for process risk assessment.
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…
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…
Musical Expertise and Statistical Learning of Musical and Linguistic Structures
Schön, Daniele; François, Clément
2011-01-01
Adults and infants can use the statistical properties of syllable sequences to extract words from continuous speech. Here we present a review of a series of electrophysiological studies investigating (1) Speech segmentation resulting from exposure to spoken and sung sequences (2) The extraction of linguistic versus musical information from a sung sequence (3) Differences between musicians and non-musicians in both linguistic and musical dimensions. The results show that segmentation is better after exposure to sung compared to spoken material and moreover, that linguistic structure is better learned than the musical structure when using sung material. In addition, musical expertise facilitates the learning of both linguistic and musical structures. Finally, an electrophysiological approach, which directly measures brain activity, appears to be more sensitive than a behavioral one. PMID:21811482
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…
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…
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.
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.
Improving Learning Processes: Principles, Strategies and Techniques.
ERIC Educational Resources Information Center
Cox, Philip
This guide, which examines the relationship between learning processes and learning outcomes, is aimed at senior managers, quality managers, and others at colleges and other post-16 learning providers in the United Kingdom. It is intended to help them define the key processes undertaken by learning providers, understand the critical relationships…
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.
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
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.
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…
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…
2008-01-01
There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly at the lower-division level. Elements of statistics were incorporated into an introductory biology course, including a review of statistics concepts and opportunity for students to perform statistical analysis in a biological context. Learning gains were measured with an 11-item statistics learning survey instrument developed for the course. Students showed a statistically significant 25% (p < 0.005) increase in statistics knowledge after completing introductory biology. Students improved their scores on the survey after completing introductory biology, even if they had previously completed an introductory statistics course (9%, improvement p < 0.005). Students retested 1 yr after completing introductory biology showed no loss of their statistics knowledge as measured by this instrument, suggesting that the use of statistics in biology course work may aid long-term retention of statistics knowledge. No statistically significant differences in learning were detected between male and female students in the study. PMID:18765754
Metz, Anneke M
2008-01-01
There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly at the lower-division level. Elements of statistics were incorporated into an introductory biology course, including a review of statistics concepts and opportunity for students to perform statistical analysis in a biological context. Learning gains were measured with an 11-item statistics learning survey instrument developed for the course. Students showed a statistically significant 25% (p < 0.005) increase in statistics knowledge after completing introductory biology. Students improved their scores on the survey after completing introductory biology, even if they had previously completed an introductory statistics course (9%, improvement p < 0.005). Students retested 1 yr after completing introductory biology showed no loss of their statistics knowledge as measured by this instrument, suggesting that the use of statistics in biology course work may aid long-term retention of statistics knowledge. No statistically significant differences in learning were detected between male and female students in the study.
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
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…
Introduction to this special issue on statistics for wildfire processes
Marcia Gumpertz
2009-01-01
This special issue on statistics for wildfire processes brings together foresters, wildfire ecologists, statisticians, mathematicians, and economists. All of these disciplines bring different interests, approaches and expertise to the modeling of wildfire processes. It is not necessarily easy, however, to communicate across disciplines or follow the developments in a...
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…
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…
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
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.
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.
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
[Auditory processing maturation in children with and without learning difficulties].
Ivone, Ferreira Neves; Schochat, Eliane
2005-01-01
Auditory processing maturation in school children with and without learning difficulties. To verify response improvement with the increase in age of the auditory processing skills in school children with ages ranging from eight to ten years, with and without learning difficulties and to perform a comparative study. Eighty-nine children without learning complaints (Group 1) and 60 children with learning difficulties (Group II) were assessed. The used auditory processing tests were: Pediatric Speech Intelligibility (PSI), Speech in Noise, Dichotic Non-Verbal (DNV) and Staggered Spondaic Word (SSW). A better performance was observed for Group I between the ages of eight and ten in all of the used tests. However, the observed differences were statistically significant only for PSI and SSW. For Group II, a better performance was also observed with the increase in age, with statistically significant differences for all of the used tests. Comparing the results between Groups I and II, a better performance was verified for children with no learning difficulties, in the three age groups, in PSI, DNV and SSW. A statistically significant improvement was verified in the responses of the auditory processing with the increase in age, for the ages between eight and ten years, in children with and without learning difficulties. In the comparative study, it was verified that children with learning difficulties presented a lower performance in all of the used tests in the three age groups. This suggests, for this group, a delay in the maturation of the auditory processing skills.
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
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.
Prediction during statistical learning, and implications for the implicit/explicit divide
Dale, Rick; Duran, Nicholas D.; Morehead, J. Ryan
2012-01-01
Accounts of statistical learning, both implicit and explicit, often invoke predictive processes as central to learning, yet practically all experiments employ non-predictive measures during training. We argue that the common theoretical assumption of anticipation and prediction needs clearer, more direct evidence for it during learning. We offer a novel experimental context to explore prediction, and report results from a simple sequential learning task designed to promote predictive behaviors in participants as they responded to a short sequence of simple stimulus events. Predictive tendencies in participants were measured using their computer mouse, the trajectories of which served as a means of tapping into predictive behavior while participants were exposed to very short and simple sequences of events. A total of 143 participants were randomly assigned to stimulus sequences along a continuum of regularity. Analysis of computer-mouse trajectories revealed that (a) participants almost always anticipate events in some manner, (b) participants exhibit two stable patterns of behavior, either reacting to vs. predicting future events, (c) the extent to which participants predict relates to performance on a recall test, and (d) explicit reports of perceiving patterns in the brief sequence correlates with extent of prediction. We end with a discussion of implicit and explicit statistical learning and of the role prediction may play in both kinds of learning. PMID:22723817
Prediction during statistical learning, and implications for the implicit/explicit divide.
Dale, Rick; Duran, Nicholas D; Morehead, J Ryan
2012-01-01
Accounts of statistical learning, both implicit and explicit, often invoke predictive processes as central to learning, yet practically all experiments employ non-predictive measures during training. We argue that the common theoretical assumption of anticipation and prediction needs clearer, more direct evidence for it during learning. We offer a novel experimental context to explore prediction, and report results from a simple sequential learning task designed to promote predictive behaviors in participants as they responded to a short sequence of simple stimulus events. Predictive tendencies in participants were measured using their computer mouse, the trajectories of which served as a means of tapping into predictive behavior while participants were exposed to very short and simple sequences of events. A total of 143 participants were randomly assigned to stimulus sequences along a continuum of regularity. Analysis of computer-mouse trajectories revealed that (a) participants almost always anticipate events in some manner, (b) participants exhibit two stable patterns of behavior, either reacting to vs. predicting future events, (c) the extent to which participants predict relates to performance on a recall test, and (d) explicit reports of perceiving patterns in the brief sequence correlates with extent of prediction. We end with a discussion of implicit and explicit statistical learning and of the role prediction may play in both kinds of learning.
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.
Learning the Image Processing Pipeline
NASA Astrophysics Data System (ADS)
Jiang, Haomiao; Tian, Qiyuan; Farrell, Joyce; Wandell, Brian A.
2017-10-01
Many creative ideas are being proposed for image sensor designs, and these may be useful in applications ranging from consumer photography to computer vision. To understand and evaluate each new design, we must create a corresponding image processing pipeline that transforms the sensor data into a form that is appropriate for the application. The need to design and optimize these pipelines is time-consuming and costly. We explain a method that combines machine learning and image systems simulation that automates the pipeline design. The approach is based on a new way of thinking of the image processing pipeline as a large collection of local linear filters. We illustrate how the method has been used to design pipelines for novel sensor architectures in consumer photography applications.
Survival-time statistics for sample space reducing stochastic processes
NASA Astrophysics Data System (ADS)
Yadav, Avinash Chand
2016-04-01
Stochastic processes wherein the size of the state space is changing as a function of time offer models for the emergence of scale-invariant features observed in complex systems. I consider such a sample-space reducing (SSR) stochastic process that results in a random sequence of strictly decreasing integers {x (t )},0 ≤t ≤τ , with boundary conditions x (0 )=N and x (τ ) = 1. This model is shown to be exactly solvable: PN(τ ) , the probability that the process survives for time τ is analytically evaluated. In the limit of large N , the asymptotic form of this probability distribution is Gaussian, with mean and variance both varying logarithmically with system size: <τ >˜lnN and στ2˜lnN . Correspondence can be made between survival-time statistics in the SSR process and record statistics of independent and identically distributed random variables.
Survival-time statistics for sample space reducing stochastic processes.
Yadav, Avinash Chand
2016-04-01
Stochastic processes wherein the size of the state space is changing as a function of time offer models for the emergence of scale-invariant features observed in complex systems. I consider such a sample-space reducing (SSR) stochastic process that results in a random sequence of strictly decreasing integers {x(t)},0≤t≤τ, with boundary conditions x(0)=N and x(τ) = 1. This model is shown to be exactly solvable: P_{N}(τ), the probability that the process survives for time τ is analytically evaluated. In the limit of large N, the asymptotic form of this probability distribution is Gaussian, with mean and variance both varying logarithmically with system size: 〈τ〉∼lnN and σ_{τ}^{2}∼lnN. Correspondence can be made between survival-time statistics in the SSR process and record statistics of independent and identically distributed random variables.
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…
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
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…
Statistical Process Control in the Practice of Program Evaluation.
ERIC Educational Resources Information Center
Posavac, Emil J.
1995-01-01
A technique developed to monitor the quality of manufactured products, statistical process control (SPC), incorporates several features that may prove attractive to evaluators. This paper reviews the history of SPC, suggests how the approach can enrich program evaluation, and illustrates its use in a hospital-based example. (SLD)
Statistical Process Control. Impact and Opportunities for Ohio.
ERIC Educational Resources Information Center
Brown, Harold H.
The first purpose of this study is to help the reader become aware of the evolution of Statistical Process Control (SPC) as it is being implemented and used in industry today. This is approached through the presentation of a brief historical account of SPC, from its inception through the technological miracle that has occurred in Japan. The…
Statistical Process Control. A Summary. FEU/PICKUP Project Report.
ERIC Educational Resources Information Center
Owen, M.; Clark, I.
A project was conducted to develop a curriculum and training materials to be used in training industrial operatives in statistical process control (SPC) techniques. During the first phase of the project, questionnaires were sent to 685 companies (215 of which responded) to determine where SPC was being used, what type of SPC firms needed, and how…
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
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.
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)
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.
Temporal and statistical information in causal structure learning.
McCormack, Teresa; Frosch, Caren; Patrick, Fiona; Lagnado, David
2015-03-01
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 system that operated probabilistically. Participants of all ages preferentially relied on the temporal pattern of events in their inferences, even if this conflicted with statistical information. In Experiments 2 and 3, participants observed a series of interventions on the system, which in these experiments operated deterministically. In Experiment 2, participants found it easier to use temporal pattern information than statistical information provided as a result of interventions. In Experiment 3, in which no temporal pattern information was provided, children from 6- to 7-years-old, but not younger children, were able to use intervention information to make causal chain judgments, although they had difficulty when the structure was a common cause. The findings suggest that participants, and children in particular, may find it more difficult to use statistical information than temporal pattern information because of its demands on information processing resources. However, there may also be an inherent preference for temporal information. PsycINFO Database Record (c) 2015 APA, all rights reserved.
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.
Using zebrafish to learn statistical analysis and Mendelian genetics.
Lindemann, Samantha; Senkler, Jon; Auchter, Elizabeth; Liang, Jennifer O
2011-06-01
This project was developed to promote understanding of how mathematics and statistical analysis are used as tools in genetic research. It gives students the opportunity to carry out hypothesis-driven experiments in the classroom: students generate hypotheses about Mendelian and non-Mendelian inheritance patterns, gather raw data, and test their hypotheses using chi-square statistical analysis. In the first protocol, students are challenged to analyze inheritance patterns using GloFish, brightly colored, commercially available, transgenic zebrafish that express Green, Yellow, or Red Fluorescent Protein throughout their muscles. In the second protocol, students learn about genetic screens, microscopy, and developmental biology by analyzing the inheritance patterns of mutations that cause developmental defects. The difficulty of the experiments can be adapted for middle school to upper level undergraduate students. Since the GloFish experiments use only fish and materials that can be purchased from pet stores, they should be accessible to many schools. For each protocol, we provide detailed instructions, ideas for how the experiments fit into an undergraduate curriculum, raw data, and example analyses. Our plan is to have these protocols form the basis of a growing and adaptable educational tool available on the Zebrafish in the Classroom Web site.
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
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
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…
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.
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…
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…
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.
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.
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.
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…
Learning Science: A Generative Process.
ERIC Educational Resources Information Center
Osborne, R. J.; Wittrock, M. C.
1983-01-01
The generative learning model is explored and linked to recent science education research findings. The implications of the model for the teaching and learning of science, the training of science teachers, and science educational research are discussed. (JN)
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.
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
Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models
2015-09-12
AFRL-AFOSR-VA-TR-2015-0278 DERIVATIVE FREE OPTIMIZATION OF COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS Katya Scheinberg...COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-11-1-0239 5c. PROGRAM ELEMENT...developed, which has been the focus of our research. 15. SUBJECT TERMS optimization, Derivative-Free Optimization, Statistical Machine Learning 16. SECURITY
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
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…
Post-processing for statistical image analysis in light microscopy.
Cardullo, Richard A; Hinchcliffe, Edward H
2013-01-01
Image processing of images serves a number of important functions including noise reduction, contrast enhancement, and feature extraction. Whatever the final goal, an understanding of the nature of image acquisition and digitization and subsequent mathematical manipulations of that digitized image is essential. Here we discuss the basic mathematical and statistical processes that are routinely used by microscopists to routinely produce high quality digital images and to extract key features of interest using a variety of extraction and thresholding tools. Copyright © 2013 Elsevier Inc. All rights reserved.
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.
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. Copyright © 2014 Elsevier Inc. All rights reserved.
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…
Intercultural Competency: A Transformative Learning Process.
ERIC Educational Resources Information Center
Taylor, Edward W.
1994-01-01
From interviews with 12 U.S. adults who successfully lived in another culture emerged a model of the learning process for intercultural competence. Its components are setting the stage (learning readiness), cultural disequilibrium, cognitive orientation (reflective/nonreflective), learning strategies (observer, participant, friend), and evolving…
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…
Statistical Learning in Children With Specific Language Impairment
Evans, Julia L.; Saffran, Jenny R.; Robe-Torres, Kathryn
2013-01-01
Purpose In this study, the authors examined (a) whether children with specific language impairment (SLI) can implicitly compute the probabilities of adjacent sound sequences, (b) if this ability is related to degree of exposure, (c) if it is domain specific or domain general and, (d) if it is related to vocabulary. Method Children with SLI and normal language controls (ages 6;5–14;4 [years;months]) listened to 21 min of a language in which transitional probabilities within words were higher than those between words. In a second study, children with SLI and Age–Nonverbal IQ matched controls (8;0–10;11) listened to the same language for 42 min and to a second 42 min “tone” language containing the identical statistical structure as the “speech” language. Results After 21 min, the SLI group's performance was at chance, whereas performance for the control group was significantly greater than chance and significantly correlated with receptive and expressive vocabulary knowledge. In the 42-minute speech condition, the SLI group's performance was significantly greater than chance and correlated with receptive vocabulary but was no different from chance in the analogous 42-minute tone condition. Performance for the control group was again significantly greater than chance in 42-minute speech and tone conditions. Conclusions These findings suggest that poor implicit learning may underlie aspects of the language impairments in SLI. PMID:19339700
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.
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.
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.
NASA Astrophysics Data System (ADS)
Havens, Timothy C.; Cummings, Ian; Botts, Jonathan; Summers, Jason E.
2017-05-01
The linear ordered statistic (LOS) is a parameterized ordered statistic (OS) that is a weighted average of a rank-ordered sample. LOS operators are useful generalizations of aggregation as they can represent any linear aggregation, from minimum to maximum, including conventional aggregations, such as mean and median. In the fuzzy logic field, these aggregations are called ordered weighted averages (OWAs). Here, we present a method for learning LOS operators from training data, viz., data for which you know the output of the desired LOS. We then extend the learning process with regularization, such that a lower complexity or sparse LOS can be learned. Hence, we discuss what 'lower complexity' means in this context and how to represent that in the optimization procedure. Finally, we apply our learning methods to the well-known constant-false-alarm-rate (CFAR) detection problem, specifically for the case of background levels modeled by long-tailed distributions, such as the K-distribution. These backgrounds arise in several pertinent imaging problems, including the modeling of clutter in synthetic aperture radar and sonar (SAR and SAS) and in wireless communications.
The influence of process parameters on electromigration lifetime statistics
NASA Astrophysics Data System (ADS)
Hauschildt, M.; Gall, M.; Justison, P.; Hernandez, R.; Ho, P. S.
2008-08-01
Even after the successful introduction of Cu-based metallization, the electromigration failure risk has remained one of the important reliability concerns for advanced process technologies mostly due to ever increasing operating current densities. The main factors that require understanding are the activation energy related to the dominating diffusion mechanism, the median lifetimes, and the lognormal standard deviation sigma of experimentally obtained lifetime distributions. This study investigates the effect of different process parameters on electromigration lifetime statistics in Cu interconnects. First, the failure distributions of single damascene interconnects with smaller line height are examined, followed by an analysis of the influence of different passivation layers on electromigration statistics. A third part focuses on samples with dual damascene technology. It is observed that the first two process modifications change the median time to failure but do not alter the sigma value. Geometrical and kinetic models developed to describe the electromigration characteristics in Cu/SiN interconnects are successfully employed to explain this observation. These models imply that the lifetime statistics depend on variations in void sizes, geometrical and experimental factors of the electromigration test, and kinetic aspects of the mass transport process. The sigma value in dual damascene interconnects is found to be larger compared to corresponding single damascene structures as a result of an increase in possible void shapes and sizes for void growth into the via in addition to evolution along the line. Furthermore, simulations of expected characteristics of lifetime distributions for future technology nodes using the above models and current electromigration data are discussed.
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
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.
Automatic statistical processing of visual properties in simultanagnosia.
Demeyere, Nele; Rzeskiewicz, Anna; Humphreys, Katharine A; Humphreys, Glyn W
2008-09-01
Previous research has suggested that, when operating in a distributed attention mode, the visual system automatically represents visual displays by their overall statistics, rather than their individual properties. Recent neuropsychological work shows partly preserved distributed attention in simultanagnosic patients, who are typically defined as only perceiving one object at a time. Here we assessed whether GK, a patient with simultanagnosia, shows averaging of stimulus properties when distributing his attention across a set of items. We manipulated different stimulus properties in two experiments: color shades and size. We found that, when GK was in a distributed mode of attention, he (incorrectly) identified the mean object from two classes of exemplars more than in a control condition, when only one exemplar class was present. Overall, this study suggests that automatic statistical processing of color and size is possible in simultanagnosia.
Daikoku, Tatsuya; Takahashi, Yuji; Tarumoto, Nagayoshi; Yasuda, Hideki
2017-09-05
Previous studies suggest that statistical learning is preserved when acoustic changes are made to auditory sequences. However, statistical learning effects can vary with and without concurrent exercise. The present study examined how concurrent physical exercise influences auditory statistical learning when acoustic and temporal changes are made to auditory sequences. Participants were presented with 500-tone sequences based on a Markov chain while cycling or resting in ignored and attended conditions. Learning effects were evaluated using a familiarity test with four types of short tone series: tone series in which stimuli were same as 500-tone sequence and three tone series in which frequencies, tempo, or rhythm was changed. We suggested that, regardless of attention, concurrent exercise interferes with tolerance in statistical learning for rhythm, rather than tempo changes. There may be specific relationships among statistical learning, rhythm perception, and motor system underlying physical exercise.
Statistical representation of a spray as a point process
Subramaniam, S.
2000-10-01
The statistical representation of a spray as a finite point process is investigated. One objective is to develop a better understanding of how single-point statistical information contained in descriptions such as the droplet distribution function (ddf), relates to the probability density functions (pdfs) associated with the droplets themselves. Single-point statistical information contained in the droplet distribution function (ddf) is shown to be related to a sequence of single surrogate-droplet pdfs, which are in general different from the physical single-droplet pdfs. It is shown that the ddf contains less information than the fundamental single-point statistical representation of the spray, which is also described. The analysis shows which events associated with the ensemble of spray droplets can be characterized by the ddf, and which cannot. The implications of these findings for the ddf approach to spray modeling are discussed. The results of this study also have important consequences for the initialization and evolution of direct numerical simulations (DNS) of multiphase flows, which are usually initialized on the basis of single-point statistics such as the droplet number density in physical space. If multiphase DNS are initialized in this way, this implies that even the initial representation contains certain implicit assumptions concerning the complete ensemble of realizations, which are invalid for general multiphase flows. Also the evolution of a DNS initialized in this manner is shown to be valid only if an as yet unproven commutation hypothesis holds true. Therefore, it is questionable to what extent DNS that are initialized in this manner constitute a direct simulation of the physical droplets. Implications of these findings for large eddy simulations of multiphase flows are also discussed. (c) 2000 American Institute of Physics.
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.
Statistical signal processing for an implantable ethanol biosensor.
Han, Jae-Joon; Doerschuk, Peter C; Gelfand, Saul B; O'Connor, Sean J
2006-01-01
The understanding of drinking patterns leading to alcoholism has been hindered by an inability to unobtrusively measure ethanol consumption over periods of weeks to months in the community environment. Signal processing for an implantable ethanol MEMS bio sensor under simultaneous development is described where the sensor-signal processing system will provide a novel approach to this need. For safety and user acceptability issues, the sensor will be implanted subcutaneously and therefore measure peripheral-tissue ethanol concentration. A statistical signal processing system based on detailed models of the physiology and using extended Kalman filtering and dynamic programming tools is described which determines ethanol consumption and kinetics in other compartments from the time course of peripheral-tissue ethanol concentration.
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.
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.
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.
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…
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…
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…
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…
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…
Statistical Inference in the Learning of Novel Phonetic Categories
ERIC Educational Resources Information Center
Zhao, Yuan
2010-01-01
Learning a phonetic category (or any linguistic category) requires integrating different sources of information. A crucial unsolved problem for phonetic learning is how this integration occurs: how can we update our previous knowledge about a phonetic category as we hear new exemplars of the category? One model of learning is Bayesian Inference,…
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…
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…
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.
Monitoring Pharmacy Expert System Performance Using Statistical Process Control Methodology
Doherty, Joshua A.; Reichley, Richard M.; Noirot, Laura A.; Resetar, Ervina; Hodge, Michael R.; Sutter, Robert D.; Dunagan, Wm Claiborne; Bailey, Thomas C.
2003-01-01
Automated expert systems provide a reliable and effective way to improve patient safety in a hospital environment. Their ability to analyze large amounts of data without fatigue is a decided advantage over clinicians who perform the same tasks. As dependence on expert systems increase and the systems become more complex, it is important to closely monitor their performance. Failure to generate alerts can jeopardize the health and safety of patients, while generating excessive false positives can cause valid alerts to be dismissed as noise. In this study, statistical process control charts were used to monitor an expert system, and the strengths and weaknesses of this technology are presented. PMID:14728163
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…
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…
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.
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 a Natural Language by 8-Month-Old Infants
ERIC Educational Resources Information Center
Pelucchi, Bruna; Hay, Jessica F.; Saffran, Jenny R.
2009-01-01
Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real…
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…
"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…
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…
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…
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…
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…
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…
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…
"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…
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…
A Constructivist Approach in a Blended E-Learning Environment for Statistics
ERIC Educational Resources Information Center
Poelmans, Stephan; Wessa, Patrick
2015-01-01
In this study, we report on the students' evaluation of a self-constructed constructivist e-learning environment for statistics, the compendium platform (CP). The system was built to endorse deeper learning with the incorporation of statistical reproducibility and peer review practices. The deployment of the CP, with interactive workshops and…
Statistical Learning 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…
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.
Milic, Natasa M; Trajkovic, Goran Z; Bukumiric, Zoran M; Cirkovic, Andja; Nikolic, Ivan M; Milin, Jelena S; Milic, Nikola V; Savic, Marko D; Corac, Aleksandar M; Marinkovic, Jelena M; Stanisavljevic, Dejana M
2016-01-01
Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013-14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001). This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training
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…
Vertical compression algorithms for sequentially processed statistical files
Batory, D.S.
1984-01-01
Horizontal data compression eliminates redundancies or regularities that occur within individual records. Suppression of trailing blanks and leading zeros are examples. Vertical compression eliminates regularities that occur across consecutively stored records. Prefix and suffix compression are examples. Two new vertical compression algorithms, VRE and HVRE, are presented in this paper. They are based on a combination of character matrix transposition (where rows are initially identified with records) and horizontal compression algorithms (run-length encoding and Huffman encoding). Experimental and theoretical results are presented that show the performance of VRE and HVRE is superior to that of some reputable commercial algorithms. Specifically, these are the compression algorithms of the ADABAS, IDMS and SHRINK/2 data management systems. VRE and HVRE are best suited for compressing statistical files which are sequentially processed and batch updated. They may also be used for file archival and for compressing randomly processed files as well.
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.
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
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…
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…
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…
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…
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…
Graphene growth process modeling: a physical-statistical approach
NASA Astrophysics Data System (ADS)
Wu, Jian; Huang, Qiang
2014-09-01
As a zero-band semiconductor, graphene is an attractive material for a wide variety of applications such as optoelectronics. Among various techniques developed for graphene synthesis, chemical vapor deposition on copper foils shows high potential for producing few-layer and large-area graphene. Since fabrication of high-quality graphene sheets requires the understanding of growth mechanisms, and methods of characterization and control of grain size of graphene flakes, analytical modeling of graphene growth process is therefore essential for controlled fabrication. The graphene growth process starts with randomly nucleated islands that gradually develop into complex shapes, grow in size, and eventually connect together to cover the copper foil. To model this complex process, we develop a physical-statistical approach under the assumption of self-similarity during graphene growth. The growth kinetics is uncovered by separating island shapes from area growth rate. We propose to characterize the area growth velocity using a confined exponential model, which not only has clear physical explanation, but also fits the real data well. For the shape modeling, we develop a parametric shape model which can be well explained by the angular-dependent growth rate. This work can provide useful information for the control and optimization of graphene growth process on Cu foil.
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.
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.
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.
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…
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…
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…
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…
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.…
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.…
Conceptual learning processes in physical therapy students.
Graham, C L
1996-08-01
The purpose of this study was to investigate processes used by students in developing conceptual knowledge in physical therapy. The subjects were 10 first-year physical therapist students enrolled in a professional Master of Physical Therapy degree program. Qualitative methods were used to collect data during a 15-week kinesiology course. Data were collected using open-ended interviews, observation, and journals kept by the students throughout the course. Several major themes emerged, including use of discussion, use of visualization, and use of experience by the students as they learned concepts in kinesiology. The type of learning processes used by students in this study may be enhanced by educational methods such as collaboration and group learning, situated cognition and use of authentic contexts, cognitive apprenticeship, and whole-part-whole learning. Further research is needed to explore the relationship between student learning processes and teaching methods used in physical therapy education.
Designing Instruction That Supports Cognitive Learning Processes
Clark, Ruth; Harrelson, Gary L.
2002-01-01
Objective: To provide an overview of current cognitive learning processes, including a summary of research that supports the use of specific instructional methods to foster those processes. We have developed examples in athletic training education to help illustrate these methods where appropriate. Data Sources: Sources used to compile this information included knowledge base and oral and didactic presentations. Data Synthesis: Research in educational psychology within the past 15 years has provided many principles for designing instruction that mediates the cognitive processes of learning. These include attention, management of cognitive load, rehearsal in working memory, and retrieval of new knowledge from long-term memory. By organizing instruction in the context of tasks performed by athletic trainers, transfer of learning and learner motivation are enhanced. Conclusions/Recommendations: Scientific evidence supports instructional methods that can be incorporated into lesson design and improve learning by managing cognitive load in working memory, stimulating encoding into long-term memory, and supporting transfer of learning. PMID:12937537
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.
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.
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.…
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.…
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,…
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…
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…
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…
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…
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…
Process Systems Engineering Education: Learning by Research
ERIC Educational Resources Information Center
Abbas, A.; Alhammadi, H. Y.; Romagnoli, J. A.
2009-01-01
In this paper, we discuss our approach in teaching the final-year course Process Systems Engineering. Students are given ownership of the course by transferring to them the responsibility of learning. A project-based group environment stimulates learning while solving a real engineering problem. We discuss postgraduate student involvement and how…
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,…
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…
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…
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.
Dissociable Learning Processes Underlie Human Pain Conditioning
Zhang, Suyi; Mano, Hiroaki; Ganesh, Gowrishankar; Robbins, Trevor; Seymour, Ben
2016-01-01
Summary 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. PMID:26711494
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
Linking sounds to meanings: infant statistical learning in a natural language.
Hay, Jessica F; Pelucchi, Bruna; Graf Estes, Katharine; Saffran, Jenny R
2011-09-01
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. Copyright © 2011 Elsevier Inc. All rights reserved.
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.
Jeste, Shafali S; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F N; Johnson, Scott P
2015-01-01
Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event-related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non-verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non-verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non-verbal cognition and adaptive social function. © 2014 John Wiley & Sons Ltd.
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.
Cross-situational statistically-based word learning intervention for late-talking toddlers
Alt, Mary; Meyers, Christina; Oglivie, Trianna; Nicholas, Katrina; Arizmendi, Genesis
2015-01-01
Purpose To explore the efficacy of a word learning intervention for late-talking toddlers that is based on principles of cross-situational statistical learning. Methods Four late-talking toddlers were individually provided with 7–10 weeks of bi-weekly word learning intervention that incorporated principles of cross-situational statistical learning. Treatment was input-based meaning that, aside from initial probes, children were not asked to produce any language during the sessions. Pre-intervention data included parent-reported measures of productive vocabulary and language samples. Data collected during intervention included production on probes, spontaneous production during treatment, and parent report of words used spontaneously at home. Data were analyzed for number of target words learned relative to control words, effect sizes, and pre-post treatment vocabulary measures. Results All children learned more target words than control words, and, on average, showed a large treatment effect size. Children made pre-post vocabulary gains, increasing their percentile scores on the MCDI, and demonstrated a rate of word learning that was faster than rates found in the literature. Conclusions Cross-situational statistically-based word learning intervention has the potential to improve vocabulary learning in late-talking toddlers. Limitations on interpretation are also discussed. Cross-situational statistically-based word learning intervention for late-talking toddlers PMID:25155254
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.
Rudd, James; Moore, Jason H; Urbanowicz, Ryan J
2013-11-01
Permutation-based statistics for evaluating the significance of class prediction, predictive attributes, and patterns of association have only appeared within the learning classifier system (LCS) literature since 2012. While still not widely utilized by the LCS research community, formal evaluations of test statistic confidence are imperative to large and complex real world applications such as genetic epidemiology where it is standard practice to quantify the likelihood that a seemingly meaningful statistic could have been obtained purely by chance. LCS algorithms are relatively computationally expensive on their own. The compounding requirements for generating permutation-based statistics may be a limiting factor for some researchers interested in applying LCS algorithms to real world problems. Technology has made LCS parallelization strategies more accessible and thus more popular in recent years. In the present study we examine the benefits of externally parallelizing a series of independent LCS runs such that permutation testing with cross validation becomes more feasible to complete on a single multi-core workstation. We test our python implementation of this strategy in the context of a simulated complex genetic epidemiological data mining problem. Our evaluations indicate that as long as the number of concurrent processes does not exceed the number of CPU cores, the speedup achieved is approximately linear.
ERIC Educational Resources Information Center
Averitt, Sallie D.
This instructor guide, which was developed for use in a manufacturing firm's advanced technical preparation program, contains the materials required to present a learning module that is designed to prepare trainees for the program's statistical process control module by improving their basic math skills in working with line graphs and teaching…
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.
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
Emberson, Lauren L.; Rubinstein, Dani
2016-01-01
The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1—dog1, bird2—dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1— dog_picture1, bird_picture2—dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs
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)
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)
Seismicity driven by transient aseismic processes: Detection and statistical modeling
NASA Astrophysics Data System (ADS)
Hainzl, S.; Marsan, D.
2012-04-01
It is widely accepted that the Coulomb failure stress variations are underlying earthquake activity. Usually two components of stress variations are considered, the slow and stationary stress build-up due to tectonic forcing and static stress changes related to earthquake occurrences. In this case, the epidemic-type aftershock sequence (ETAS) model has been shown to describe successfully the spatiotemporal evolution of the statistical properties of seismicity. However, in many cases, seismicity might be locally dominated by stress changes related to transient aseismic processes such as magma intrusion, fluid flow or slow slip events which are not directly observable in general. Therefore, it is important to account for those potential transients, firstly to avoid erroneous model fitting leading to biased forecasts and secondly to retrieve important information about the underlying transient processes. In this work, we apply a recently developed methodology to identify the time-dependent background-term which is based on iteratively applying a ETAS-based declustering where the size of the internally applied smoothing filter is set by the Akaike information criterion. This procedure is shown to work well for synthetic data sets. We find that the estimated model parameters are biased if the time-dependence is not taken into account. In particular, the alpha-value describing the magnitude-dependence of the trigger potential can be strongly underestimated if transients are ignored. Low alpha-values have been previously found to indicate swarm activity which is often related to transient processes. Thus observed anomalous alpha-values might refer to transient forcing rather than to differences in the earthquake-earthquake trigger mechanism. To explore this, we apply the procedure systematically to earthquake clusters detected in Southern California and to earthquake swarm data in Vogtland/Western Bohemia. We identify clusters with significant transient forcing and show
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…
Learning process in public goods games
NASA Astrophysics Data System (ADS)
Amado, André; Huang, Weini; Campos, Paulo R. A.; Ferreira, Fernando Fagundes
2015-07-01
We propose an individual-based model to describe the effects of memory and learning in the evolution of cooperation in a public goods game (PGG) in a well-mixed population. Individuals are endowed with a set of strategies, and in every round of the game they use one strategy out of this set based on their memory and learning process. The payoff of a player using a given strategy depends on the public goods enhancement factor r and the collective action of all players. We investigate the distribution of used strategies as well as the distribution of information patterns. The outcome depends on the learning process, which can be dynamic or static. In the dynamic learning process, the players can switch their strategies along the whole game, and use the strategy providing the highest payoff at current time step. In the static learning process, there is a training period where the players randomly explore different strategies out of their strategy sets. In the rest of the game, players only use the strategy providing the highest payoff during the training period. In the dynamic learning process, we observe a transition from a non-cooperative regime to a regime where the level of cooperation reaches about 50 %. As in the standard PGG, in the static learning process there is a transition from the non-cooperative regime to a regime where the level of cooperation can be higher than 50% at r = N. In both learning processes the transition becomes smoother as the memory size of individuals increases, which means that the lack of information is a key ingredient causing the defection.
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.
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.
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 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…
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…
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.
Statistical Learning in a Natural Language by 8-Month-Old Infants
Pelucchi, Bruna; Hay, Jessica F.; Saffran, Jenny R.
2013-01-01
Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants’ ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition. PMID:19489896
All words are not created equal: expectations about word length guide infant statistical learning.
Lew-Williams, Casey; Saffran, Jenny R
2012-02-01
Infants have been described as 'statistical learners' capable of extracting structure (such as words) from patterned input (such as language). Here, we investigated whether prior knowledge influences how infants track transitional probabilities in word segmentation tasks. Are infants biased by prior experience when engaging in sequential statistical learning? In a laboratory simulation of learning across time, we exposed 9- and 10-month-old infants to a list of either disyllabic or trisyllabic nonsense words, followed by a pause-free speech stream composed of a different set of disyllabic or trisyllabic nonsense words. Listening times revealed successful segmentation of words from fluent speech only when words were uniformly disyllabic or trisyllabic throughout both phases of the experiment. Hearing trisyllabic words during the pre-exposure phase derailed infants' abilities to segment speech into disyllabic words, and vice versa. We conclude that prior knowledge about word length equips infants with perceptual expectations that facilitate efficient processing of subsequent language input. Published by Elsevier B.V.
All words are not created equal: Expectations about word length guide infant statistical learning
Lew-Williams, Casey; Saffran, Jenny R.
2011-01-01
Infants have been described as ‘statistical learners’ capable of extracting structure (such as words) from patterned input (such as language). Here, we investigated whether prior knowledge influences how infants track transitional probabilities in word segmentation tasks. Are infants biased by prior experience when engaging in sequential statistical learning? In a laboratory simulation of learning across time, we exposed 9- and 10-month-old infants to a list of either bisyllabic or trisyllabic nonsense words, followed by a pause-free speech stream composed of a different set of bisyllabic or trisyllabic nonsense words. Listening times revealed successful segmentation of words from fluent speech only when words were uniformly bisyllabic or trisyllabic throughout both phases of the experiment. Hearing trisyllabic words during the pre-exposure phase derailed infants’ abilities to segment speech into bisyllabic words, and vice versa. We conclude that prior knowledge about word length equips infants with perceptual expectations that facilitate efficient processing of subsequent language input. PMID:22088408
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.
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 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.
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,…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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,…
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)
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)
Haebig, Eileen; Saffran, Jenny R; Ellis Weismer, Susan
2017-05-02
Word learning is an important component of language development that influences child outcomes across multiple domains. Despite the importance of word knowledge, word-learning mechanisms are poorly understood in children with specific language impairment (SLI) and children with autism spectrum disorder (ASD). This study examined underlying mechanisms of word learning, specifically, statistical learning and fast-mapping, in school-aged children with typical and atypical development. Statistical learning was assessed through a word segmentation task and fast-mapping was examined in an object-label association task. We also examined children's ability to map meaning onto newly segmented words in a third task that combined exposure to an artificial language and a fast-mapping task. Children with SLI had poorer performance on the word segmentation and fast-mapping tasks relative to the typically developing and ASD groups, who did not differ from one another. However, when children with SLI were exposed to an artificial language with phonemes used in the subsequent fast-mapping task, they successfully learned more words than in the isolated fast-mapping task. There was some evidence that word segmentation abilities are associated with word learning in school-aged children with typical development and ASD, but not SLI. Follow-up analyses also examined performance in children with ASD who did and did not have a language impairment. Children with ASD with language impairment evidenced intact statistical learning abilities, but subtle weaknesses in fast-mapping abilities. As the Procedural Deficit Hypothesis (PDH) predicts, children with SLI have impairments in statistical learning. However, children with SLI also have impairments in fast-mapping. Nonetheless, they are able to take advantage of additional phonological exposure to boost subsequent word-learning performance. In contrast to the PDH, children with ASD appear to have intact statistical learning, regardless of
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…