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…
Experiment to Learn Statistical Characteristics of Dispersion Occurred in a Manufacturing Process
NASA Astrophysics Data System (ADS)
Hiraoka, Kazunori
Statistical quality control (SQC) is widely applied to manufacturing process control. Although engineers graduated a college of technology are required to master SQC, they had not sufficiently taken lesson of statistics in a college. To teach statistics practically, lesson of experiment using Pb free solder is created in our college. Experiments are performed with various parameters as solder metals and soldering temperature. Results are analyzed using Hayashi‧s quantification methods which are a kind of multivariable analysis. The conditions on which standard deviations of solder amount per a connected point are minimum and maximum are estimated analytically and verified experimentally. Students learn that dispersion occurred in a manufacturing process is unavoidable but able to be reduced. They are expected to be more familiar to statistics from this lesson.
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…
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.
Multidimensional Visual Statistical Learning
ERIC Educational Resources Information Center
Turk-Browne, Nicholas B.; Isola, Phillip J.; Scholl, Brian J.; Treat, Teresa A.
2008-01-01
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…
Statistics for Learning Genetics
NASA Astrophysics Data System (ADS)
Charles, Abigail Sheena
This study investigated the knowledge and skills that biology students may need to help them understand statistics/mathematics as it applies to genetics. The data are based on analyses of current representative genetics texts, practicing genetics professors' perspectives, and more directly, students' perceptions of, and performance in, doing statistically-based genetics problems. This issue is at the emerging edge of modern college-level genetics instruction, and this study attempts to identify key theoretical components for creating a specialized biological statistics curriculum. The goal of this curriculum will be to prepare biology students with the skills for assimilating quantitatively-based genetic processes, increasingly at the forefront of modern genetics. To fulfill this, two college level classes at two universities were surveyed. One university was located in the northeastern US and the other in the West Indies. There was a sample size of 42 students and a supplementary interview was administered to a select 9 students. Interviews were also administered to professors in the field in order to gain insight into the teaching of statistics in genetics. Key findings indicated that students had very little to no background in statistics (55%). Although students did perform well on exams with 60% of the population receiving an A or B grade, 77% of them did not offer good explanations on a probability question associated with the normal distribution provided in the survey. The scope and presentation of the applicable statistics/mathematics in some of the most used textbooks in genetics teaching, as well as genetics syllabi used by instructors do not help the issue. It was found that the text books, often times, either did not give effective explanations for students, or completely left out certain topics. The omission of certain statistical/mathematical oriented topics was seen to be also true with the genetics syllabi reviewed for this study. Nonetheless
Analysis of Self-Regulated Learning Processing Using Statistical Models for Count Data
ERIC Educational Resources Information Center
Greene, Jeffrey Alan; Costa, Lara-Jeane; Dellinger, Kristin
2011-01-01
Researchers often use measures of the frequency of self-regulated learning (SRL; Zimmerman, "American Educational Research Journal," 45(1), 166-183, 2000) processing as a predictor of learning gains. These frequency data, which are really counts of SRL processing events, are often non-normally distributed, and the accurate analysis of these data…
ERIC Educational Resources Information Center
Canturk-Gunhan, Berna; Bukova-Guzel, Esra; Ozgur, Zekiye
2012-01-01
The purpose of this study is to determine prospective mathematics teachers' views about using problem-based learning (PBL) in statistics teaching and to examine their thought processes. It is a qualitative study conducted with 15 prospective mathematics teachers from a state university in Turkey. The data were collected via participant observation…
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…
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. PMID:21585492
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.
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. PMID:24035820
Oakland, J.S.
1986-01-01
Addressing the increasing importance for firms to have a thorough knowledge of statistically based quality control procedures, this book presents the fundamentals of statistical process control (SPC) in a non-mathematical, practical way. It provides real-life examples and data drawn from a wide variety of industries. The foundations of good quality management and process control, and control of conformance and consistency during production are given. Offers clear guidance to those who wish to understand and implement modern SPC techniques.
Domain General Constraints on Statistical Learning
ERIC Educational Resources Information Center
Thiessen, Erik D.
2011-01-01
All theories of language development suggest that learning is constrained. However, theories differ on whether these constraints arise from language-specific processes or have domain-general origins such as the characteristics of human perception and information processing. The current experiments explored constraints on statistical learning of…
Statistical Downscaling: Lessons Learned
NASA Astrophysics Data System (ADS)
Walton, D.; Hall, A. D.; Sun, F.
2013-12-01
In this study, we examine ways to improve statistical downscaling of general circulation model (GCM) output. Why do we downscale GCM output? GCMs have low resolution, so they cannot represent local dynamics and topographic effects that cause spatial heterogeneity in the regional climate change signal. Statistical downscaling recovers fine-scale information by utilizing relationships between the large-scale and fine-scale signals to bridge this gap. In theory, the downscaled climate change signal is more credible and accurate than its GCM counterpart, but in practice, there may be little improvement. Here, we tackle the practical problems that arise in statistical downscaling, using temperature change over the Los Angeles region as a test case. This region is an ideal place to apply downscaling since its complex topography and shoreline are poorly simulated by GCMs. By comparing two popular statistical downscaling methods and one dynamical downscaling method, we identify issues with statistically downscaled climate change signals and develop ways to fix them. We focus on scale mismatch, domain of influence, and other problems - many of which users may be unaware of - and discuss practical solutions.
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,…
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…
Teaching Statistics through Learning Projects
ERIC Educational Resources Information Center
Moreira da Silva, Mauren Porciúncula; Pinto, Suzi Samá
2014-01-01
This paper aims to reflect on the teaching of statistics through student research, in the form of projects carried out by students on self-selected topics. The paper reports on a study carried out with two undergraduate classes using a methodology of teaching that we call "learning projects." Monitoring the development of the various…
Functional Differences between Statistical Learning with and without Explicit Training
ERIC Educational Resources Information Center
Batterink, Laura J.; Reber, Paul J.; Paller, Ken A.
2015-01-01
Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and…
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.
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
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 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 process control in healthcare].
Anhøj, Jacob; Bjørn, Brian
2009-05-18
Statistical process control (SPC) is a branch of statistical science which comprises methods for the study of process variation. Common cause variation is inherent in any process and predictable within limits. Special cause variation is unpredictable and indicates change in the process. The run chart is a simple tool for analysis of process variation. Run chart analysis may reveal anomalies that suggest shifts or unusual patterns that are attributable to special cause variation. PMID:19454196
Early language acquisition: Statistical learning and social learning
NASA Astrophysics Data System (ADS)
Kuhl, Patricia K.
2003-10-01
Infants are sensitive to the statistical patterns in language input, and exposure to them alters phonetic perception. Our recent data indicate that first-time exposure to a foreign language at 9 months of age results in learning after only 5 h, suggesting a process that is fairly automatic, given natural language input. At the same time, it appears that early phonetic learning from natural language may be constrained by the need for social interaction. Our work demonstrates that infants learn phonetically when exposed to a live, but not a pre-recorded, speaker. This talk will focus on statistical learning in a social context and develop the thesis that this combination provides an ideal situation for the acquisition of a natural language.
Visual Statistical Learning in the Newborn Infant
ERIC Educational Resources Information Center
Bulf, Hermann; Johnson, Scott P.; Valenza, Eloisa
2011-01-01
Statistical learning--implicit learning of statistical regularities within sensory input--is a way of acquiring structure within continuous sensory environments. Statistics computation, initially shown to be involved in word segmentation, has been demonstrated to be a general mechanism that operates across domains, across time and space, and…
Learning Abstract Statistics Concepts Using Simulation
ERIC Educational Resources Information Center
Mills, Jamie D.
2004-01-01
The teaching and learning of statistics has impacted the curriculum in elementary, secondary, and post-secondary education. Because of this growing movement to expand and include statistics into all levels of education, there is also a considerable interest in how to teach statistics. For statistics concepts that tend to be very difficult or…
Statistical prediction of cyclostationary processes
Kim, K.Y.
2000-03-15
Considered in this study is a cyclostationary generalization of an EOF-based prediction method. While linear statistical prediction methods are typically optimal in the sense that prediction error variance is minimal within the assumption of stationarity, there is some room for improved performance since many physical processes are not stationary. For instance, El Nino is known to be strongly phase locked with the seasonal cycle, which suggests nonstationarity of the El Nino statistics. Many geophysical and climatological processes may be termed cyclostationary since their statistics show strong cyclicity instead of stationarity. Therefore, developed in this study is a cyclostationary prediction method. Test results demonstrate that performance of prediction methods can be improved significantly by accounting for the cyclostationarity of underlying processes. The improvement comes from an accurate rendition of covariance structure both in space and time.
Representational Versatility in Learning Statistics
ERIC Educational Resources Information Center
Graham, Alan T.; Thomas, Michael O. J.
2005-01-01
Statistical data can be represented in a number of qualitatively different ways, the choice depending on the following three conditions: the concepts to be investigated; the nature of the data; and the purpose for which they were collected. This paper begins by setting out frameworks that describe the nature of statistical thinking in schools, and…
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…
Rhythmic Grouping Biases Constrain Infant Statistical Learning
ERIC Educational Resources Information Center
Hay, Jessica F.; Saffran, Jenny R.
2012-01-01
Linguistic stress and sequential statistical cues to word boundaries interact during speech segmentation in infancy. However, little is known about how the different acoustic components of stress constrain statistical learning. The current studies were designed to investigate whether intensity and duration each function independently as cues to…
Screencast Tutorials Enhance Student Learning of Statistics
ERIC Educational Resources Information Center
Lloyd, Steven A.; Robertson, Chuck L.
2012-01-01
Although the use of computer-assisted instruction has rapidly increased, there is little empirical research evaluating these technologies, specifically within the context of teaching statistics. The authors assessed the effect of screencast tutorials on learning outcomes, including statistical knowledge, application, and interpretation. Students…
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…
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 process control for lathes
Barkman, W.E.; Babelay, E.F.; Woodard, L.M.
1986-12-18
The Oak Ridge Y-12 Plant produces large numbers of hemishell workpieces using precision computer-controlled lathes. In order to improve the quality/productivity of these machines, a pilot project is under way to demonstrate the utility of automatic, on-machine measurement of key workpiece features. This system utilizes tough-trigger probes and on automatic tool changer to generate data for a host data base that monitors and adjusts the machine's operations for variable machining conditions. This paper discusses the individual components, control software and data communications that are used to achieve an automated machining system which incorporates statistical process control.
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
Establishment of an attentional set via statistical learning.
Cosman, Joshua D; Vecera, Shaun P
2014-02-01
The ability to overcome attentional capture and attend goal-relevant information is typically viewed as a volitional, effortful process that relies on the maintenance of current task priorities or "attentional sets" in working memory. However, the visual system possesses statistical learning mechanisms that can incidentally encode probabilistic associations between goal-relevant objects and the attributes likely to define them. Thus, it is possible that statistical learning may contribute to the establishment of a given attentional set and modulate the effects of attentional capture. Here we provide evidence for such a mechanism, showing that implicitly learned associations between a search target and its likely color directly influence the ability of a salient color precue to capture attention in a classic attentional capture task. This indicates a novel role for statistical learning in the modulation of attentional capture, and emphasizes the role that this learning may play in goal-directed attentional control more generally. PMID:24099589
Musical experience influences statistical learning of a novel language.
Shook, Anthony; Marian, Viorica; Bartolotti, James; Schroeder, Scott R
2013-01-01
Musical experience may benefit learning of a new language by increasing the fidelity with which the auditory system encodes sound. In the current study, participants with varying degrees of musical experience were exposed to two statistically defined languages consisting of auditory Morse code sequences that varied in difficulty. We found an advantage for highly skilled musicians, relative to lower-skilled musicians, in learning novel Morse code-based words. Furthermore, in the more difficult learning condition, performance of lower-skilled musicians was mediated by their general cognitive abilities. We suggest that musical experience may improve processing of statistical information and that musicians' enhanced ability to learn statistical probabilities in a novel Morse code language may extend to natural language learning. PMID:23505962
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.
An overview of statistical learning theory.
Vapnik, V N
1999-01-01
Statistical learning theory was introduced in the late 1960's. Until the 1990's it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990's new types of learning algorithms (called support vector machines) based on the developed theory were proposed. This made statistical learning theory not only a tool for the theoretical analysis but also a tool for creating practical algorithms for estimating multidimensional functions. This article presents a very general overview of statistical learning theory including both theoretical and algorithmic aspects of the theory. The goal of this overview is to demonstrate how the abstract learning theory established conditions for generalization which are more general than those discussed in classical statistical paradigms and how the understanding of these conditions inspired new algorithmic approaches to function estimation problems. A more detailed overview of the theory (without proofs) can be found in Vapnik (1995). In Vapnik (1998) one can find detailed description of the theory (including proofs). PMID:18252602
The statistical mechanics of learning a rule
Watkin, T.L.H.; Rau, A. ); Biehl, M. )
1993-04-01
A summary is presented of the statistical mechanical theory of learning a rule with a neural network, a rapidly advancing area which is closely related to other inverse problems frequently encountered by physicists. By emphasizing the relationship between neural networks and strongly interacting physical systems, such as spin glasses, the authors show how learning theory has provided a workshop in which to develop new, exact analytical techniques.
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
The Statistical Determinants of the Speed of Motor Learning.
He, Kang; Liang, You; Abdollahi, Farnaz; Fisher Bittmann, Moria; Kording, Konrad; Wei, Kunlin
2016-09-01
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
An Active Learning Approach to Teaching Statistics.
ERIC Educational Resources Information Center
Dolinsky, Beverly
2001-01-01
Provides suggestions for using active learning as the primary means to teaching statistics in order to create a collaborative environment. Addresses such strategies as using SPSS Base 7.5 for Windows and course periods centered on answering student-generated questions. Discusses various writing intensive assignments. (CMK)
The Automaticity of Visual Statistical Learning
ERIC Educational Resources Information Center
Turk-Browne, Nicholas B.; Junge, Justin; Scholl, Brian J.
2005-01-01
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…
The Statistical Interpretation of Classical Thermodynamic Heating and Expansion Processes
ERIC Educational Resources Information Center
Cartier, Stephen F.
2011-01-01
A statistical model has been developed and applied to interpret thermodynamic processes typically presented from the macroscopic, classical perspective. Through this model, students learn and apply the concepts of statistical mechanics, quantum mechanics, and classical thermodynamics in the analysis of the (i) constant volume heating, (ii)…
ERIC Educational Resources Information Center
Criss, Ellen
2008-01-01
Teacher-educator and researcher Daniel L. Kohut suggests in "Musical Performance: Learning Theory and Pedagogy" that there are many problems that result from the way music teachers often teach. Most teachers focus on the process, not the goal. The Natural Learning Process that Kohut advocates is the same process that young children use when they…
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. PMID:24495840
Irrelevant speech effects and statistical learning.
Neath, Ian; Guérard, Katherine; Jalbert, Annie; Bireta, Tamra J; Surprenant, Aimée M
2009-08-01
Immediate serial recall of visually presented verbal stimuli is impaired by the presence of irrelevant auditory background speech, the so-called irrelevant speech effect. Two of the three main accounts of this effect place restrictions on when it will be observed, limiting its occurrence either to items processed by the phonological loop (the phonological loop hypothesis) or to items that are not too dissimilar from the irrelevant speech (the feature model). A third, the object-oriented episodic record (O-OER) model, requires only that the memory task involves seriation. The present studies test these three accounts by examining whether irrelevant auditory speech will interfere with a task that does not involve the phonological loop, does not use stimuli that are compatible with those to be remembered, but does require seriation. Two experiments found that irrelevant speech led to lower levels of performance in a visual statistical learning task, offering more support for the O-OER model and posing a challenge for the other two accounts. PMID:19370483
Infrared Image Simulation Based On Statistical Learning Theory
NASA Astrophysics Data System (ADS)
Chaochao, Huang; Xiaodi, Wu; Wuqin, Tong
2007-12-01
A real-time simulation algorithm of infrared image based on statistical learning theory is presented. The method includes three contents to achieve real-time simulation of infrared image, such as acquiring the training sample, forecasting the scene temperature field value by statistical learning machine, data processing and data analysis of temperature field. The simulation result shows this algorithm based on ν - support vector regression have better maneuverability and generalization than the other method, and the simulation precision and real-time quality are satisfying.
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…
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. PMID:25071660
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…
Improving Instruction Using Statistical Process Control.
ERIC Educational Resources Information Center
Higgins, Ronald C.; Messer, George H.
1990-01-01
Two applications of statistical process control to the process of education are described. Discussed are the use of prompt feedback to teachers and prompt feedback to students. A sample feedback form is provided. (CW)
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.
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. PMID:23545387
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. PMID:26280268
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…
ERIC Educational Resources Information Center
Garfield, Joan; Ben-Zvi, Dani
2009-01-01
This article describes a model for an interactive, introductory secondary- or tertiary-level statistics course that is designed to develop students' statistical reasoning. This model is called a "Statistical Reasoning Learning Environment" and is built on the constructivist theory of learning.
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…
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
Gaussian processes for machine learning.
Seeger, Matthias
2004-04-01
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigated. Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of uncertainties in our predictions and generic model selection procedures cast as nonlinear optimization problems. Their main drawback of heavy computational scaling has recently been alleviated by the introduction of generic sparse approximations.13,78,31 The mathematical literature on GPs is large and often uses deep concepts which are not required to fully understand most machine learning applications. In this tutorial paper, we aim to present characteristics of GPs relevant to machine learning and to show up precise connections to other "kernel machines" popular in the community. Our focus is on a simple presentation, but references to more detailed sources are provided. PMID:15112367
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…
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
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. PMID:25153964
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…
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
Flexible Visual Statistical Learning: Transfer across Space and Time
ERIC Educational Resources Information Center
Turk-Browne, Nicholas B.; Scholl, Brian J.
2009-01-01
The environment contains considerable information that is distributed across space and time, and the visual system is remarkably sensitive to such information via the operation of visual statistical learning (VSL). Previous VSL studies have focused on establishing what kinds of statistical relationships can be learned but have not fully explored…
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…
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
Statistical weld process monitoring with expert interpretation
Cook, G.E.; Barnett, R.J.; Strauss, A.M.; Thompson, F.M. Jr.
1996-12-31
A statistical weld process monitoring system is described. Using data of voltage, current, wire feed speed, gas flow rate, travel speed, and elapsed arc time collected while welding, the welding statistical process control (SPC) tool provides weld process quality control by implementing techniques of data trending analysis, tolerance analysis, and sequential analysis. For purposes of quality control, the control limits required for acceptance are specified in the weld procedure acceptance specifications. The control charts then provide quality assurance documentation for each weld. The statistical data trending analysis performed by the SPC program is not only valuable as a quality assurance monitoring and documentation system, it is also valuable in providing diagnostic assistance in troubleshooting equipment and material problems. Possible equipment/process problems are identified and matched with features of the SPC control charts. To aid in interpreting the voluminous statistical output generated by the SPC system, a large number of If-Then rules have been devised for providing computer-based expert advice for pinpointing problems based on out-of-limit variations of the control charts. The paper describes the SPC monitoring tool and the rule-based expert interpreter that has been developed for relating control chart trends to equipment/process problems.
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.…
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…
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.
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…
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…
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…
Statistical process control in nursing research.
Polit, Denise F; Chaboyer, Wendy
2012-02-01
In intervention studies in which randomization to groups is not possible, researchers typically use quasi-experimental designs. Time series designs are strong quasi-experimental designs but are seldom used, perhaps because of technical and analytic hurdles. Statistical process control (SPC) is an alternative analytic approach to testing hypotheses about intervention effects using data collected over time. SPC, like traditional statistical methods, is a tool for understanding variation and involves the construction of control charts that distinguish between normal, random fluctuations (common cause variation), and statistically significant special cause variation that can result from an innovation. The purpose of this article is to provide an overview of SPC and to illustrate its use in a study of a nursing practice improvement intervention. PMID:22095634
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…
Statistical Process Control In Photolithography Applications
NASA Astrophysics Data System (ADS)
Pritchard, Lois B.
1987-04-01
Recently there have been numerous papers, articles and books on the benefits and rewards of Statistical Process Control for manufacturing processes. Models are used that quite adequately describe methods appropriate for the factory situation where many discrete and identical items are turned out and where a limited number of parameters are inspected along the line. Photolithographic applications often require different statistical models from the usual factory methods. The difficulties encountered in getting started with SPC lie in determining: 1. what parameters should be tracked 2. what statistical model is appropriate for each of those parameters 3. how to use the models chosen. This paper describes three statistical models that, among them, account for most operations within a photolithographic manufacturing application. The process of determining which model is appropriate is described, along with the basic rules that may be used in making the determination. In addition, the application of each method is shown, and action instructions are covered. Initially the "x-bar, R" model is described. This model is the one most often found in off-the-shelf software packages, and enjoys wide applications in equipment tracking, besides general use process control. Secondly the "x, moving-R" model is described. This is appropriate where a series of measurements of the same parameter is taken on a single item, perhaps at different locations, such as in dimensional uniformity control for wafers or photomasks. In this case, each "x" is a single observation, or a number of measurements of a single observation, as opposed to a mean value taken in a sampling scheme. Thirdly a model for a Poisson distribution is described, which tends to fit defect density data, particulate counts, where count data is accumulated per unit or per unit time. The purpose of the paper is to briefly describe the included models, for those with little or no background in statistics, to enable them to
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
Residence time statistics for N renewal processes.
Burov, S; Barkai, E
2011-10-21
We present a study of residence time statistics for N renewal processes with a long tailed distribution of the waiting time. Such processes describe many nonequilibrium systems ranging from the intensity of N blinking quantum dots to the residence time of N Brownian particles. With numerical simulations and exact calculations, we show sharp transitions for a critical number of degrees of freedom N. In contrast to the expectation, the fluctuations in the limit of N→∞ are nontrivial. We briefly discuss how our approach can be used to detect nonergodic kinetics from the measurements of many blinking chromophores, without the need to reach the single molecule limit. PMID:22107497
Applied behavior analysis and statistical process control?
Hopkins, B L
1995-01-01
This paper examines Pfadt and Wheeler's (1995) suggestions that the methods of statistical process control (SPC) be incorporated into applied behavior analysis. The research strategies of SPC are examined and compared to those of applied behavior analysis. I argue that the statistical methods that are a part of SPC would likely reduce applied behavior analysts' intimate contacts with the problems with which they deal and would, therefore, likely yield poor treatment and research decisions. Examples of these kinds of results and decisions are drawn from the cases and data Pfadt and Wheeler present. This paper also describes and clarifies many common misconceptions about SPC, including W. Edwards Deming's involvement in its development, its relationship to total quality management, and its confusion with various other methods designed to detect sources of unwanted variability. PMID:7592156
Beginning a statistical process control program
Davis, H.D.; Burnett, M. )
1989-01-01
Statistical Process Control (SPC) has in recent years become a hot'' topic in the manufacturing world. It has been touted as the means by which Japanese manufacturers have moved to the forefront of world-class quality, and subsequent financial power. Is SPC a business-saving strategy What is SPC What is the cost of quality and can we afford it Is SPC applicable to the petroleum refining and petrochemical manufacturing industry, or are these manufacturing operations so deterministic by nature that the statistics only show the accuracy and precision of the laboratory work If SPC is worthwhile how do we get started, and what problems can we expect to encounter If we begin an SPC Program, how will it benefit us These questions are addressed by the author. The view presented here is a management perspective with emphasis on rationale and implementation methods.
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.
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 on…
A statistical process control case study.
Ross, Thomas K
2006-01-01
Statistical process control (SPC) charts can be applied to a wide number of health care applications, yet widespread use has not occurred. The greatest obstacle preventing wider use is the lack of quality management training that health care workers receive. The technical nature of the SPC guarantees that without explicit instruction this technique will not come into widespread use. Reviews of health care quality management texts inform the reader that SPC charts should be used to improve delivery processes and outcomes often without discussing how they are created. Conversely, medical research frequently reports the improved outcomes achieved after analyzing SPC charts. This article is targeted between these 2 positions: it reviews the SPC technique and presents a tool and data so readers can construct SPC charts. After tackling the case, it is hoped that the readers will collect their own data and apply the same technique to improve processes in their own organization. PMID:17047496
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…
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…
Understanding Evaluation of Learning Support in Mathematics and Statistics
ERIC Educational Resources Information Center
MacGillivray, Helen; Croft, Tony
2011-01-01
With rapid and continuing growth of learning support initiatives in mathematics and statistics found in many parts of the world, and with the likelihood that this trend will continue, there is a need to ensure that robust and coherent measures are in place to evaluate the effectiveness of these initiatives. The nature of learning support brings…
Infants Learn about Objects from Statistics and People
ERIC Educational Resources Information Center
Wu, Rachel; Gopnik, Alison; Richardson, Daniel C.; Kirkham, Natasha Z.
2011-01-01
In laboratory experiments, infants are sensitive to patterns of visual features that co-occur (e.g., Fiser & Aslin, 2002). Once infants learn the statistical regularities, however, what do they do with that knowledge? Moreover, which patterns do infants learn in the cluttered world outside of the laboratory? Across 4 experiments, we show that…
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…
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…
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. PMID:24905545
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…
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.…
Statistical process management: An essential element of quality improvement
Buckner, M.R.
1992-09-01
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 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 process management: An essential element of quality improvement
Buckner, M.R.
1992-01-01
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.
Thiessen, Erik D; Girard, Sandrine; Erickson, Lucy C
2016-07-01
Infants and children are generally more successful than adults in learning novel languages, a phenomenon referred to as a critical or sensitive period for language acquisition. One explanation for this critical period is the idea that children have access to a set of language learning processes or mechanisms unavailable to adults. From this perspective, developmental change is explained in terms of a discontinuity of learning processes. We suggest that this is not the only possible explanation for developmental change in language learning outcomes. Instead, we propose that the mechanisms underlying language acquisition (in particular, we highlight statistical learning) are largely continuous across the lifespan. From this perspective, developmental change is explained in terms of experience, differences in the input with age, and maturational changes in the cognitive architecture supporting learning, even while the learning process itself operates continuously across developmental time. WIREs Cogn Sci 2016, 7:276-288. doi: 10.1002/wcs.1394 For further resources related to this article, please visit the WIREs website. PMID:27239798
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…
Learning about Education through Statistics. Second Edition.
ERIC Educational Resources Information Center
Geddes, Claire
The National Center for Education Statistics (NCES) gathers data on all aspects of education from across the United States and releases the resulting surveys and studies as survey reports, information compendia, and special reports that focus on specific educational topics. NCES also participates in joint research activities, brings together data…
Probability & Statistics: Modular Learning Exercises. Student Edition
ERIC Educational Resources Information Center
Actuarial Foundation, 2012
2012-01-01
The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The materials are centered on the fictional town of Happy Shores, a coastal community which is at risk for hurricanes. Actuaries at an insurance company figure out the risks and…
Probability & Statistics: Modular Learning Exercises. Teacher Edition
ERIC Educational Resources Information Center
Actuarial Foundation, 2012
2012-01-01
The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The modules also introduce students to real world math concepts and problems that property and casualty actuaries come across in their work. They are designed to be used by teachers and…
Cross-situational statistical word learning in young children.
Suanda, Sumarga H; Mugwanya, Nassali; Namy, Laura L
2014-10-01
Recent empirical work has highlighted the potential role of cross-situational statistical word learning in children's early vocabulary development. In the current study, we tested 5- to 7-year-old children's cross-situational learning by presenting children with a series of ambiguous naming events containing multiple words and multiple referents. Children rapidly learned word-to-object mappings by attending to the co-occurrence regularities across these ambiguous naming events. The current study begins to address the mechanisms underlying children's learning by demonstrating that the diversity of learning contexts affects performance. The implications of the current findings for the role of cross-situational word learning at different points in development are discussed along with the methodological implications of employing school-aged children to test hypotheses regarding the mechanisms supporting early word learning. PMID:25015421
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
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
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…
Aging and the statistical learning of grammatical form classes.
Schwab, Jessica F; Schuler, Kathryn D; Stillman, Chelsea M; Newport, Elissa L; Howard, James H; Howard, Darlene V
2016-08-01
Language learners must place unfamiliar words into categories, often with few explicit indicators about when and how that word can be used grammatically. Reeder, Newport, and Aslin (2013) showed that college students can learn grammatical form classes from an artificial language by relying solely on distributional information (i.e., contextual cues in the input). Here, 2 experiments revealed that healthy older adults also show such statistical learning, though they are poorer than young at distinguishing grammatical from ungrammatical strings. This finding expands knowledge of which aspects of learning vary with aging, with potential implications for second language learning in late adulthood. (PsycINFO Database Record PMID:27294711
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.
What Can Be Learned from Inverse Statistics?
NASA Astrophysics Data System (ADS)
Ahlgren, Peter Toke Heden; Dahl, Henrik; Jensen, Mogens Høgh; Simonsen, Ingve
One stylized fact of financial markets is an asymmetry between the most likely time to profit and to loss. This gain-loss asymmetry is revealed by inverse statistics, a method closely related to empirically finding first passage times. Many papers have presented evidence about the asymmetry, where it appears and where it does not. Also, various interpretations and explanations for the results have been suggested. In this chapter, we review the published results and explanations. We also examine the results and show that some are at best fragile. Similarly, we discuss the suggested explanations and propose a new model based on Gaussian mixtures. Apart from explaining the gain-loss asymmetry, this model also has the potential to explain other stylized facts such as volatility clustering, fat tails, and power law behavior of returns.
FloatBoost learning and statistical face detection.
Li, Stan Z; Zhang, ZhenQiu
2004-09-01
A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the posterior probability. These novel techniques lead to a classifier which requires fewer weak classifiers than AdaBoost yet achieves lower error rates in both training and testing, as demonstrated by extensive experiments. Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported. PMID:15742888
Domain generality vs. modality specificity: The paradox of statistical learning
Frost, Ram; Armstrong, Blair C.; Siegelman, Noam; Christiansen, Morten H.
2015-01-01
Statistical learning is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. Recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal, however, modality and stimulus specificity. An important question is, therefore, how and why a hypothesized domain-general learning mechanism systematically produces such effects. We offer a theoretical framework according to which statistical learning is not a unitary mechanism, but a set of domain-general computational principles, that operate in different modalities and therefore are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility. PMID:25631249
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. PMID:27617967
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…
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…
ERIC Educational Resources Information Center
Anderson, John R.; And Others
The ACT theory of the learning of procedures is described. ACT is a computer simulation program that uses a propositional network to represent knowledge of general facts and a set of productions (condition action rules) to represent knowledge of procedures. There are currently four different mechanisms by which ACT can make additions 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.
Statistical word learning at scale: the baby's view is better.
Yurovsky, Daniel; Smith, Linda B; Yu, Chen
2013-11-01
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 assumptions about the nature of naming event ambiguity, assumptions that have been challenged by recent analyses of natural naming events. This paper shows that learning from ambiguous natural naming events depends on perspective. Natural naming events from parent-child interactions were recorded from both a third-person tripod-mounted camera and from a head-mounted camera that produced a 'child's-eye' view. Following the human simulation paradigm, adults were asked to learn artificial language labels by integrating across the most ambiguous of these naming events. Significant learning was found only from the child's perspective, pointing to the importance of considering statistical learning from an embodied perspective. PMID:24118720
Statistical process control for IMRT dosimetric verification
Breen, Stephen L.; Moseley, Douglas J.; Zhang, Beibei; Sharpe, Michael B.
2008-10-15
Patient-specific measurements are typically used to validate the dosimetry of intensity-modulated radiotherapy (IMRT). To evaluate the dosimetric performance over time of our IMRT process, we have used statistical process control (SPC) concepts to analyze the measurements from 330 head and neck (H and N) treatment plans. The objectives of the present work are to: (i) Review the dosimetric measurements of a large series of consecutive head and neck treatment plans to better understand appropriate dosimetric tolerances; (ii) analyze the results with SPC to develop action levels for measured discrepancies; (iii) develop estimates for the number of measurements that are required to describe IMRT dosimetry in the clinical setting; and (iv) evaluate with SPC a new beam model in our planning system. H and N IMRT cases were planned with the PINNACLE{sup 3} treatment planning system versions 6.2b or 7.6c (Philips Medical Systems, Madison, WI) and treated on Varian (Palo Alto, CA) or Elekta (Crawley, UK) linacs. As part of regular quality assurance, plans were recalculated on a 20-cm-diam cylindrical phantom, and ion chamber measurements were made in high-dose volumes (the PTV with highest dose) and in low-dose volumes (spinal cord organ-at-risk, OR). Differences between the planned and measured doses were recorded as a percentage of the planned dose. Differences were stable over time. Measurements with PINNACLE{sup 3} 6.2b and Varian linacs showed a mean difference of 0.6% for PTVs (n=149, range, -4.3% to 6.6%), while OR measurements showed a larger systematic discrepancy (mean 4.5%, range -4.5% to 16.3%) that was due to well-known limitations of the MLC model in the earlier version of the planning system. Measurements with PINNACLE{sup 3} 7.6c and Varian linacs demonstrated a mean difference of 0.2% for PTVs (n=160, range, -3.0%, to 5.0%) and -1.0% for ORs (range -5.8% to 4.4%). The capability index (ratio of specification range to range of the data) was 1.3 for the PTV
Statistical process control for IMRT dosimetric verification.
Breen, Stephen L; Moseley, Douglas J; Zhang, Beibei; Sharpe, Michael B
2008-10-01
Patient-specific measurements are typically used to validate the dosimetry of intensity-modulated radiotherapy (IMRT). To evaluate the dosimetric performance over time of our IMRT process, we have used statistical process control (SPC) concepts to analyze the measurements from 330 head and neck (H&N) treatment plans. The objectives of the present work are to: (i) Review the dosimetric measurements of a large series of consecutive head and neck treatment plans to better understand appropriate dosimetric tolerances; (ii) analyze the results with SPC to develop action levels for measured discrepancies; (iii) develop estimates for the number of measurements that are required to describe IMRT dosimetry in the clinical setting; and (iv) evaluate with SPC a new beam model in our planning system. H&N IMRT cases were planned with the PINNACLE treatment planning system versions 6.2b or 7.6c (Philips Medical Systems, Madison, WI) and treated on Varian (Palo Alto, CA) or Elekta (Crawley, UK) linacs. As part of regular quality assurance, plans were recalculated on a 20-cm-diam cylindrical phantom, and ion chamber measurements were made in high-dose volumes (the PTV with highest dose) and in low-dose volumes (spinal cord organ-at-risk, OR). Differences between the planned and measured doses were recorded as a percentage of the planned dose. Differences were stable over time. Measurements with PINNACLE3 6.2b and Varian linacs showed a mean difference of 0.6% for PTVs (n=149, range, -4.3% to 6.6%), while OR measurements showed a larger systematic discrepancy (mean 4.5%, range -4.5% to 16.3%) that was due to well-known limitations of the MLC model in the earlier version of the planning system. Measurements with PINNACLE3 7.6c and Varian linacs demonstrated a mean difference of 0.2% for PTVs (n=160, range, -3.0%, to 5.0%) and -1.0% for ORs (range -5.8% to 4.4%). The capability index (ratio of specification range to range of the data) was 1.3 for the PTV data, indicating that almost
Statistical Profiles of Highly-Rated Learning Objects
ERIC Educational Resources Information Center
Cechinel, Cristian; Sanchez-Alonso, Salvador; Garcia-Barriocanal, Elena
2011-01-01
The continuously growth of learning resources available in on-line repositories has raised the concern for the development of automated methods for quality assessment. The current existence of on-line evaluations in such repositories has opened the possibility of searching for statistical profiles of highly-rated resources that can be used as…
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…
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…
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. PMID:27081307
Statistical learning of temporal community structure in the hippocampus
Schapiro, Anna C.; Turk-Browne, Nicholas B.; Norman, Kenneth A.; Botvinick, Matthew M.
2015-01-01
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. PMID:26332666
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
Visual statistical learning in children and young adults: how implicit?
Bertels, Julie; Boursain, Emeline; Destrebecqz, Arnaud; Gaillard, Vinciane
2015-01-01
Visual statistical learning (VSL) is the ability to extract the joint and conditional probabilities of shapes co-occurring during passive viewing of complex visual configurations. Evidence indicates that even infants are sensitive to these regularities (e.g., Kirkham et al., 2002). However, there is continuing debate as to whether VSL is accompanied by conscious awareness of the statistical regularities between sequence elements. Bertels et al. (2012) addressed this question in young adults. Here, we adapted their paradigm to investigate VSL and conscious awareness in children. Using the same version of the paradigm, we also tested young adults so as to directly compare results from both age groups. Fifth graders and undergraduates were exposed to a stream of visual shapes arranged in triplets. Learning of these sequences was then assessed using both direct and indirect measures. In order to assess the extent to which learning occurred explicitly, we also measured confidence through subjective measures in the direct task (i.e., binary confidence judgments). Results revealed that both children and young adults learned the statistical regularities between shapes. In both age groups, participants who performed above chance in the completion task had conscious access to their knowledge. Nevertheless, although adults performed above chance even when they claimed to guess, there was no evidence of implicit knowledge in children. These results suggest that the role of implicit and explicit influences in VSL may follow a developmental trajectory. PMID:25620943
Living and learning food processing
Technology Transfer Automated Retrieval System (TEKTRAN)
This year’s annual event promises to be both exciting and educational for those who wish to learn more about food processing. This column will provide a brief overview of the multitude of scientific sessions that reveal new research related to food processing. In addition to the symposia previewed h...
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…
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
Changing Structures in Midstream: Learning Along the Statistical Garden Path
Gebhart, Andrea L.; Aslin, Richard N.; Newport, Elissa L.
2010-01-01
Previous studies of auditory statistical learning have typically presented learners with sequential structural information that is uniformly distributed across the entire exposure corpus. Here we present learners with nonuniform distributions of structural information by altering the organization of trisyllabic nonsense words at midstream. When this structural change was unmarked by low-level acoustic cues, or even when cued by a pitch change, only the first of the two structures was learned. However, both structures were learned when there was an explicit cue to the midstream change or when exposure to the second structure was tripled in duration. These results demonstrate that successful extraction of the structure in an auditory statistical learning task reduces the ability to learn subsequent structures, unless the presence of two structures is marked explicitly or the exposure to the second is quite lengthy. The mechanisms by which learners detect and use changes in distributional information to maintain sensitivity to multiple structures are discussed from both behavioral and computational perspectives. PMID:20574548
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.
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…
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
Smith, Linda B; Yu, Chen
2013-01-01
Recent evidence shows that infants can learn words and referents by aggregating ambiguous information across situations to discern the underlying word-referent mappings. Here, we use an individual difference approach to understand the role of different kinds of attentional processes in this learning: 12-and 14-month-old infants participated in a cross-situational word-referent learning task in which the learning trials were ordered to create local novelty effects, effects that should not alter the statistical evidence for the underlying correspondences. The main dependent measures were derived from frame-by-frame analyses of eye gaze direction. The fine- grained dynamics of looking behavior implicates different attentional processes that may compete with or support statistical learning. The discussion considers the role of attention in binding heard words to seen objects, individual differences in attention and vocabulary development, and the relation between macro-level theories of word learning and the micro-level dynamic processes that underlie learning. PMID:24403867
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 feedback…
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…
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
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.
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
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.
Statistical learning for alloy design from electronic structure calculations
NASA Astrophysics Data System (ADS)
Broderick, Scott R.
The objective of this thesis is to explore how statistical learning methods can contribute to the interpretation and efficacy of electronic structure calculations. This study develops new applications of statistical learning and data mining methods to both semi-empirical and density functional theory (DFT) calculations. Each of these classes of electronic structure calculations serves as templates for different data driven discovery strategies for materials science applications. In our study of semi-empirical methods, we take advantage of the ability of data mining methods to quantitatively assess high dimensional parameterization schemes. The impact of this work includes the development of accelerated computational schemes for developing reduced order models. Another application is the use of these informatics based techniques to serve as a means for estimating parameters when data for such calculations are not available. Using density of states (DOS) spectra derived from DFT calculations we have demonstrated the classification power of singular value decomposition methods to accurately develop structural and stoichiometric classifications of compounds. Building on this work we have extended this analytical strategy to apply the predictive capacity of informatics methods to develop a new and far more robust modeling approach for DOS spectra, addressing an issue that has gone relatively unchallenged over two decades. By exploring a diverse array of materials systems (metals, ceramics, different crystal structures) this work has laid the foundations for expanding the linkages between statistical learning and statistical thermodynamics. The results of this work provide exciting new opportunities in computational based design of materials that have not been explored before.
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. PMID:14748442
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. PMID:27015348
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
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…
Applying Statistical Process Control to Clinical Data: An Illustration.
ERIC Educational Resources Information Center
Pfadt, Al; And Others
1992-01-01
Principles of statistical process control are applied to a clinical setting through the use of control charts to detect changes, as part of treatment planning and clinical decision-making processes. The logic of control chart analysis is derived from principles of statistical inference. Sample charts offer examples of evaluating baselines and…
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. PMID:25192632
Statistical simulation of multiple Compton backscattering process
NASA Astrophysics Data System (ADS)
Potylitsyn, A. P.; Kolchuzhkin, A. M.
2014-09-01
A number of laboratories are currently developing monochromatic sources of X-rays and gamma quanta based on the Compton backscattering (CBS) of laser photons by relativistic electrons. Modern technologies are capable of providing a concentration of electrons and photons in the interaction point such that each primary electron can emit several hard photons. In contrast to the well-known nonlinear CBS process, in which an initial electron "absorbs" a few laser photons and emits a single hard one, the above-mentioned process can be called a multiple CBS process and is characterized by a mean number of emitted photons. The present paper is devoted to simulating the parameters of a beam of back scattered quanta based on the Monte Carlo technique. It is shown that, even in the case of strong collimation of a resulting photon beam, the radiation monochromaticity may deteriorate because of the contribution coming from the multiple photon emission, which is something that must be considered while designing new CBS sources.
Statistics and Machine Learning based Outlier Detection Techniques for Exoplanets
NASA Astrophysics Data System (ADS)
Goel, Amit; Montgomery, Michele
2015-08-01
Architectures of planetary systems are observable snapshots in time that can indicate formation and dynamic evolution of planets. The observable key parameters that we consider are planetary mass and orbital period. If planet masses are significantly less than their host star masses, then Keplerian Motion is defined as P^2 = a^3 where P is the orbital period in units of years and a is the orbital period in units of Astronomical Units (AU). Keplerian motion works on small scales such as the size of the Solar System but not on large scales such as the size of the Milky Way Galaxy. In this work, for confirmed exoplanets of known stellar mass, planetary mass, orbital period, and stellar age, we analyze Keplerian motion of systems based on stellar age to seek if Keplerian motion has an age dependency and to identify outliers. For detecting outliers, we apply several techniques based on statistical and machine learning methods such as probabilistic, linear, and proximity based models. In probabilistic and statistical models of outliers, the parameters of a closed form probability distributions are learned in order to detect the outliers. Linear models use regression analysis based techniques for detecting outliers. Proximity based models use distance based algorithms such as k-nearest neighbour, clustering algorithms such as k-means, or density based algorithms such as kernel density estimation. In this work, we will use unsupervised learning algorithms with only the proximity based models. In addition, we explore the relative strengths and weaknesses of the various techniques by validating the outliers. The validation criteria for the outliers is if the ratio of planetary mass to stellar mass is less than 0.001. In this work, we present our statistical analysis of the outliers thus detected.
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
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…
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. PMID:26743060
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
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
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. PMID:27406289
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…
Learning Nursing Process: A Group Project.
ERIC Educational Resources Information Center
Gross, Judith W.
1994-01-01
Herbert Thelen's models of group inquiry and cooperative learning were used to teach the nursing process in a group clinical setting. The strategy proved efficient and effective, and students benefited by learning group process and having reduced stress. (JOW)
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…
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…
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…
Computer program performs statistical analysis for random processes
NASA Technical Reports Server (NTRS)
Newberry, M. H.
1966-01-01
Random Vibration Analysis Program /RAVAN/ performs statistical analysis on a number of phenomena associated with flight and captive tests, but can also be used in analyzing data from many other random processes.
Applying statistical process control to the adaptive rate control problem
NASA Astrophysics Data System (ADS)
Manohar, Nelson R.; Willebeek-LeMair, Marc H.; Prakash, Atul
1997-12-01
Due to the heterogeneity and shared resource nature of today's computer network environments, the end-to-end delivery of multimedia requires adaptive mechanisms to be effective. We present a framework for the adaptive streaming of heterogeneous media. We introduce the application of online statistical process control (SPC) to the problem of dynamic rate control. In SPC, the goal is to establish (and preserve) a state of statistical quality control (i.e., controlled variability around a target mean) over a process. We consider the end-to-end streaming of multimedia content over the internet as the process to be controlled. First, at each client, we measure process performance and apply statistical quality control (SQC) with respect to application-level requirements. Then, we guide an adaptive rate control (ARC) problem at the server based on the statistical significance of trends and departures on these measurements. We show this scheme facilitates handling of heterogeneous media. Last, because SPC is designed to monitor long-term process performance, we show that our online SPC scheme could be used to adapt to various degrees of long-term (network) variability (i.e., statistically significant process shifts as opposed to short-term random fluctuations). We develop several examples and analyze its statistical behavior and guarantees.
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
Jeste, Shafali S.; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J.; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F. N.; Johnson, Scott P.
2015-01-01
Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism…
Implicit Statistical Learning Is Directly Associated with the Acquisition of Syntax
ERIC Educational Resources Information Center
Kidd, Evan
2012-01-01
This article reports on an individual differences study that investigated the role of implicit statistical learning in the acquisition of syntax in children. One hundred children ages 4 years 5 months through 6 years 11 months completed a test of implicit statistical learning, a test of explicit declarative learning, and standardized tests of…
ERIC Educational Resources Information Center
Wu, Yazhou; Zhang, Ling; Liu, Ling; Zhang, Yanqi; Liu, Xiaoyu; Yi, Dong
2015-01-01
It is clear that the teaching of medical statistics needs to be improved, yet areas for priority are unclear as medical students' learning and application of statistics at different levels is not well known. Our goal is to assess the attitudes of medical students toward the learning and application of medical statistics, and discover their…
Supporting Learning Process with Concept Map Scripts.
ERIC Educational Resources Information Center
Rautama, Erkki; Sutinen, Erkki; Tarhio, Jorma
1997-01-01
Describes a framework for computer-aided concept mapping that provides the means to easily trace the learning process. Presents the construction of a concept map as a script which consists of elementary operations. This approach can be applied in presentation tools, in evaluating the learning process, and in computer-aided learning. (Author/AEF)
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.
NASA Astrophysics Data System (ADS)
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.
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…
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
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
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…
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…
Comparing associative, statistical, and inferential reasoning accounts of human contingency learning
Pineño, Oskar; Miller, Ralph R.
2007-01-01
For more than two decades, researchers have contrasted the relative merits of associative and statistical theories as accounts of human contingency learning. This debate, still far from resolution, has led to further refinement of models within each family of theories. More recently, a third theoretical view has joined the debate: the inferential reasoning account. The explanations of these three accounts differ critically in many aspects, such as level of analysis and their emphasis on different steps within the information-processing sequence. Also, each account has important advantages (as well as critical flaws) and emphasizes experimental evidence that poses problems to the others. Some hybrid models of human contingency learning have attempted to reconcile certain features of these accounts, thereby benefiting from some of the unique advantages of different families of accounts. A comparison of these families of accounts will help us appreciate the challenges that research on human contingency learning will face over the coming years. PMID:17366303
Statistical 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. PMID:25451311
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. PMID:11376640
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 Is Not Affected by a Prior Bout of Physical Exercise.
Stevens, David J; Arciuli, Joanne; Anderson, David I
2016-05-01
This study examined the effect of a prior bout of exercise on implicit cognition. Specifically, we examined whether a prior bout of moderate intensity exercise affected performance on a statistical learning task in healthy adults. A total of 42 participants were allocated to one of three conditions-a control group, a group that exercised for 15 min prior to the statistical learning task, and a group that exercised for 30 min prior to the statistical learning task. The participants in the exercise groups cycled at 60% of their respective V˙O2 max. Each group demonstrated significant statistical learning, with similar levels of learning among the three groups. Contrary to previous research that has shown that a prior bout of exercise can affect performance on explicit cognitive tasks, the results of the current study suggest that the physiological stress induced by moderate-intensity exercise does not affect implicit cognition as measured by statistical learning. PMID:26084984
Yield enhancement in micromechanical sensor fabrication using statistical process control
NASA Astrophysics Data System (ADS)
Borenstein, Jeffrey T.; Preble, Douglas M.
1997-09-01
Statistical process control (SPC) has gained wide acceptance in recent years as an essential tool for yield improvement in the microelectronics industry. In both manufacturing and research and development settings, statistical methods are extremely useful in process control and optimization. Here we describe the recent implementation of SPC in the micromachining fabrication process at Draper. A wide array of micromachined silicon sensors, including gyroscopes, accelerometers, and microphones, are routinely fabricated at Draper, often with rapidly changing designs and processes. In spite of Draper's requirements for rapid turnaround and relatively small, short production runs, SPC has turned out to be a critical component of the product development process. This paper describes the multipronged SPC approach we have developed and tailored to the particular requirements of an R & D micromachining process line. Standard tools such as Pareto charts, histograms, and cause-and-effect diagrams have been deployed to troubleshoot yield and performance problems in the micromachining process, and several examples of their use are described. More rigorous approaches, such as the use of control charts for variables and attributes, have been instituted with considerable success. The software package CornerstoneR was selected to handle the SPC program at Draper. We describe the highly automated process now in place for monitoring key processes, including diffusion, oxidation, photolithography, and etching. In addition to the process monitoring, gauge capability is applied to critical metrology tools on a regular basis. Applying these tools in the process line has resulted in sharply improved yields and shortened process cycles.
ERIC Educational Resources Information Center
Miller, John
1994-01-01
Presents an approach to document numbering, document titling, and process measurement which, when used with fundamental techniques of statistical process control, reveals meaningful process-element variation as well as nominal productivity models. (SR)
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
Testing the Limits of Statistical Learning for Word Segmentation
Johnson, Elizabeth K.; Tyler, Michael D.
2009-01-01
Past research has demonstrated that infants can rapidly extract syllable distribution information from an artificial language and use this knowledge to infer likely word boundaries in speech. However, artificial languages are extremely simplified with respect to natural language. In this study, we ask whether infants’ ability to track transitional probabilities between syllables in an artificial language can scale up to the challenge of natural language. We do so by testing both 5.5- and 8-month-olds’ ability to segment an artificial language containing four words of uniform length (all CVCV) or four words of varying length (two CVCV, two CVCVCV). The transitional probability cues to word boundaries were held equal across the two languages. Both age groups segmented the language containing words of uniform length, demonstrating that even 5.5-month-olds are extremely sensitive to the conditional probabilities in their environment. However, neither age group succeeded in segmenting the language containing words of varying length, despite the fact that the transitional probability cues defining word boundaries were equally strong in the two languages. We conclude that infants’ statistical learning abilities may not be as robust as earlier studies have suggested. PMID:20136930
77 FR 46096 - Statistical Process Controls for Blood Establishments; Public Workshop
Federal Register 2010, 2011, 2012, 2013, 2014
2012-08-02
... HUMAN SERVICES Food and Drug Administration Statistical Process Controls for Blood Establishments... and Drug Administration (FDA) is announcing a public workshop entitled: ``Statistical Process Controls... statistical process controls to validate and monitor manufacturing processes in blood establishments....
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.
Statistical Process Control: Going to the Limit for Quality.
ERIC Educational Resources Information Center
Training, 1987
1987-01-01
Defines the concept of statistical process control, a quality control method used especially in manufacturing. Generally, concept users set specific standard levels that must be met. Makes the point that although employees work directly with the method, management is responsible for its success within the plant. (CH)
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…
Asymmetric exclusion process and extremal statistics of random sequences.
Bundschuh, R
2002-03-01
A mapping is established between sequence alignment, one of the most commonly used tools of computational biology, at a certain choice of scoring parameters and the asymmetric exclusion process, one of the few exactly solvable models of nonequilibrium physics. The statistical significance of sequence alignments is characterized through studying the total hopping current of the discrete time and space version of the asymmetric exclusion process. PMID:11909113
A journey to statistical process control in the development environment
Hanna, M.; Langston, D.
1996-12-31
Over the past 10 years many organizations have undertaken {open_quotes}process reengineering{close_quotes} activities in an attempt to increase their productivity and quality. Unfortunately, the launching point for these reengineering efforts has been based upon the belief that organizational processes either do not exist or they are grossly inefficient. It is the position of the authors that these beliefs are typically unfounded. All ongoing organizations have processes. These processes are effective, based upon the fact they are producing products (or services) that are being purchased. Therefore, the issue is not to invent or reengineer new processes, rather it is to increase the efficiency of the existing ones. This paper outlines a process (or organizational journey) for continually improving process based upon quantitative management techniques and statistical process control methods.
"Dear Fresher …"--How Online Questionnaires Can Improve Learning and Teaching Statistics
ERIC Educational Resources Information Center
Bebermeier, Sarah; Nussbeck, Fridtjof W.; Ontrup, Greta
2015-01-01
Lecturers teaching statistics are faced with several challenges supporting students' learning in appropriate ways. A variety of methods and tools exist to facilitate students' learning on statistics courses. The online questionnaires presented in this report are a new, slightly different computer-based tool: the central aim was to support students…
A Constructivist Approach in a Blended E-Learning Environment for Statistics
ERIC Educational Resources Information Center
Poelmans, Stephan; Wessa, Patrick
2015-01-01
In this study, we report on the students' evaluation of a self-constructed constructivist e-learning environment for statistics, the compendium platform (CP). The system was built to endorse deeper learning with the incorporation of statistical reproducibility and peer review practices. The deployment of the CP, with interactive workshops and…
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 of Probabilistic Nonadjacent Dependencies by Multiple-Cue Integration
ERIC Educational Resources Information Center
van den Bos, Esther; Christiansen, Morten H.; Misyak, Jennifer B.
2012-01-01
Previous studies have indicated that dependencies between nonadjacent elements can be acquired by statistical learning when each element predicts only one other element (deterministic dependencies). The present study investigates statistical learning of probabilistic nonadjacent dependencies, in which each element predicts several other elements…
Statistical Learning in 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…
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…
Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis.
Obeid, Rita; Brooks, Patricia J; Powers, Kasey L; Gillespie-Lynch, Kristen; Lum, Jarrad A G
2016-01-01
Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI) and Autism Spectrum Disorder (ASD). Using meta-analysis, we examined statistical learning in SLI (14 studies, 15 comparisons) and ASD (13 studies, 20 comparisons) to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, and Probabilistic Classification). Individuals with SLI showed deficits in statistical learning relative to age-matched controls. In contrast, statistical learning was intact in individuals with ASD relative to controls. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping social-communicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman and Pierpont, 2005), impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD. PMID:27602006
Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis
Obeid, Rita; Brooks, Patricia J.; Powers, Kasey L.; Gillespie-Lynch, Kristen; Lum, Jarrad A. G.
2016-01-01
Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI) and Autism Spectrum Disorder (ASD). Using meta-analysis, we examined statistical learning in SLI (14 studies, 15 comparisons) and ASD (13 studies, 20 comparisons) to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, and Probabilistic Classification). Individuals with SLI showed deficits in statistical learning relative to age-matched controls. In contrast, statistical learning was intact in individuals with ASD relative to controls. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping social-communicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman and Pierpont, 2005), impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD. PMID:27602006
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…
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
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. PMID:19239395
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,…
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. PMID:11531550
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…
Ma, Ning; Yu, Angela J.
2015-01-01
Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making. PMID:26321966
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.…
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…
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…
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…
Teaching Statistics Online in a Blended Learning Environment
ERIC Educational Resources Information Center
Rynearson, Kimberly; Kerr, Marcel S.
2005-01-01
Several versions of a Web-based graduate-level course in statistics are described. In the final version, the experiential aspects of a face-to-face course in statistics are maintained through frequent interaction between the instructor and students using digital video lectures that depict real-time statistical computations. The use of text-based…
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. PMID:27014147
Tool compensation using statistical process control on complex milling operations
Reilly, J.M.
1994-03-01
In today`s competitive manufacturing environment, many companies increasingly rely on numerical control (NC) mills to produce products at a reasonable cost. Typically, this is done by producing as many features as possible at each machining operation to minimize the total number of shop hours invested per part. Consequently, the number of cutting tools involved in one operation can become quite large since NC mills have the capacity to use in excess of 100 cutting tools. As the number of cutting tools increases, the difficulty of applying optimum tool compensation grows exponentially, quickly overwhelming machine operators and engineers. A systematic method of managing tool compensation is required. The name statistical process control (SPC) suggests a technique in which statistics are used to stabilize and control a machining operation. Feedback and control theory, the study of the stabilization of electronic and mechanical systems, states that control can be established by way of a feedback network. If these concepts were combined, SPC would stabilize and control manufacturing operations through the incorporation of statistically processed feedback. In its simplest application, SPC has been used as a tool to analyze inspection data. In its most mature application, SPC can be the link that applies process feedback. The approach involves: (1) identifying the significant process variables adjusted by the operator; (2) developing mathematical relationships that convert strategic part measurements into variable adjustments; and (3) implementing SPC charts that record required adjustment to each variable.
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…
Visual statistical learning based on the perceptual and semantic information of objects.
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 triplets vs. unrelated foils) and decided whether the first or second sequence was more familiar based on the familiarization phase. In Experiment 1A, the test sequences comprised line drawings; in Experiment 1B, they comprised word stimuli representing each line drawing. The results showed that performance for statistically related triplets was greater than chance. In Experiments 2 and 3 containing the forward ABC and backward CBA triplets in the test, the results showed the importance of temporal order, especially in line drawings. In Experiment 4, in which the forward triplets were pitted against the backward triplets, we showed that temporal order is still important for the expression of VSL with word stimuli. Finally, in Experiment 5, we replicated the results of Experiments 2 and 3 even with the images of visual objects. These results suggest the parallel processes on the visual features and semantic information of objects in VSL. PMID:22686848
Voronoi and void statistics for superhomogeneous point processes.
Gabrielli, Andrea; Torquato, Salvatore
2004-10-01
We study the Voronoi and void statistics of superhomogeneous (or hyperuniform) point patterns in which the infinite-wavelength density fluctuations vanish. Superhomogeneous or hyperuniform point patterns arise in one-component plasmas, primordial density fluctuations in the Universe, and jammed hard-particle packings. We specifically analyze a certain one-dimensional model by studying size fluctuations and correlations of the associated Voronoi cells. We derive exact results for the complete joint statistics of the size of two Voronoi cells. We also provide a sum rule that the correlation matrix for the Voronoi cells must obey in any space dimension. In contrast to the conventional picture of superhomogeneous systems, we show that infinitely large Voronoi cells or voids can exist in superhomogeneous point processes in any dimension. We also present two heuristic conditions to identify and classify any superhomogeneous point process in terms of the asymptotic behavior of the void size distribution. PMID:15600395
Statistical process control for hospitals: methodology, user education, and challenges.
Matthes, Nikolas; Ogunbo, Samuel; Pennington, Gaither; Wood, Nell; Hart, Marilyn K; Hart, Robert F
2007-01-01
The health care industry is slowly embracing the use of statistical process control (SPC) to monitor and study causes of variation in health care processes. While the statistics and principles underlying the use of SPC are relatively straightforward, there is a need to be cognizant of the perils that await the user who is not well versed in the key concepts of SPC. This article introduces the theory behind SPC methodology, describes successful tactics for educating users, and discusses the challenges associated with encouraging adoption of SPC among health care professionals. To illustrate these benefits and challenges, this article references the National Hospital Quality Measures, presents critical elements of SPC curricula, and draws examples from hospitals that have successfully embedded SPC into their overall approach to performance assessment and improvement. PMID:17627215
Statistical Inference for Point Process Models of Rainfall
NASA Astrophysics Data System (ADS)
Smith, James A.; Karr, Alan F.
1985-01-01
In this paper we develop maximum likelihood procedures for parameter estimation and model selection that apply to a large class of point process models that have been used to model rainfall occurrences, including Cox processes, Neyman-Scott processes, and renewal processes. The statistical inference procedures are based on the stochastic intensity λ(t) = lims→0,s>0 (1/s)E[N(t + s) - N(t)|N(u), u < t]. The likelihood function of a point process is shown to have a simple expression in terms of the stochastic intensity. The main result of this paper is a recursive procedure for computing stochastic intensities; the procedure is applicable to a broad class of point process models, including renewal Cox process with Markovian intensity processes and an important class of Neyman-Scott processes. The model selection procedure we propose, which is based on likelihood ratios, allows direct comparison of two classes of point processes to determine which provides a better model for a given data set. The estimation and model selection procedures are applied to two data sets of simulated Cox process arrivals and a data set of daily rainfall occurrences in the Potomac River basin.
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.
Holistic Processing from Learned Attention to Parts
Chua, Kao-Wei; Richler, Jennifer J.; Gauthier, Isabel
2016-01-01
Attention helps us focus on what is most relevant to our goals, and prior work shows that aspects of attention can be learned. Learned inattention to parts can abolish holistic processing of faces, but it is unknown whether learned attention to parts is sufficient to cause a change from part-based to holistic processing with objects. Here, we trained subjects to individuate non-face objects (Greebles) from two categories, Ploks and Glips. Diagnostic information was in complementary halves for the two categories. Holistic processing was then tested with Plok-Glip composites that combined the kind of part that was diagnostic or non-diagnostic during training. Exposure to Greeble parts resulted in general failures of selective attention for non-diagnostic composites, but face-like holistic processing was only observed for diagnostic composites. These results demonstrate a novel link between learned attentional control and the acquisition of holistic processing. PMID:25775049
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…
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…
Linking sounds to meanings: Infant statistical learning in a natural language
Hay, Jessica F.; Pelucchi, Bruna; Estes, Katharine Graf; Saffran, Jenny R.
2011-01-01
The processes of infant word segmentation and infant word learning have largely been studied separately. However, the ease with which potential word forms are segmented from fluent speech seems likely to influence subsequent mappings between words and their referents. To explore this process, we tested the link between the statistical coherence of sequences presented in fluent speech and infants’ subsequent use of those sequences as labels for novel objects. Notably, the materials were drawn from a natural language unfamiliar to the infants (Italian). The results of three experiments suggest that there is a close relationship between the statistics of the speech stream and subsequent mapping of labels to referents. Mapping was facilitated when the labels contained high transitional probabilities in the forward and/or backward direction (Experiment 1). When no transitional probability information was available (Experiment 2), or when the internal transitional probabilities of the labels were low in both directions (Experiment 3), infants failed to link the labels to their referents. Word learning appears to be strongly influenced by infants’ prior experience with the distribution of sounds that make up words in natural languages. PMID:21762650
ERIC Educational Resources Information Center
Yurdugül, Halil; Menzi Çetin, Nihal
2015-01-01
Problem Statement: Learners can access and participate in online learning environments regardless of time and geographical barriers. This brings up the umbrella concept of learner autonomy that contains self-directed learning, self-regulated learning and the studying process. Motivation and learning strategies are also part of this umbrella…
Evidence for online processing during causal learning.
Liu, Pei-Pei; Luhmann, Christian C
2015-03-01
Many models of learning describe both the end product of learning (e.g., causal judgments) and the cognitive mechanisms that unfold on a trial-by-trial basis. However, the methods employed in the literature typically provide only indirect evidence about the unfolding cognitive processes. Here, we utilized a simultaneous secondary task to measure cognitive processing during a straightforward causal-learning task. The results from three experiments demonstrated that covariation information is not subject to uniform cognitive processing. Instead, we observed systematic variation in the processing dedicated to individual pieces of covariation information. In particular, observations that are inconsistent with previously presented covariation information appear to elicit greater cognitive processing than do observations that are consistent with previously presented covariation information. In addition, the degree of cognitive processing appears to be driven by learning per se, rather than by nonlearning processes such as memory and attention. Overall, these findings suggest that monitoring learning processes at a finer level may provide useful psychological insights into the nature of learning. PMID:25488021
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. PMID:26711494
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
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.…
Negotiation as a Learning Process
ERIC Educational Resources Information Center
Cross, John G.
1977-01-01
Presents a theoretical model of collective bargaining based on the premise that the expectations and learning ability of the negotiators play a central role in bargaining. Discusses some implications of the model. Available from: Sage Publications, Inc., 275 South Beverly Drive, Beverly Hills, California 90212. (JG)
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
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