On-Line Individual Differences in Statistical Learning Predict Language Processing
Misyak, Jennifer B.; Christiansen, Morten H.; Tomblin, J. Bruce
2010-01-01
Considerable individual differences in language ability exist among normally developing children and adults. Whereas past research have attributed such differences to variations in verbal working memory or experience with language, we test the hypothesis that individual differences in statistical learning may be associated with differential language performance. We employ a novel paradigm for studying statistical learning on-line, combining a serial-reaction time task with artificial grammar learning. This task offers insights into both the timecourse of and individual differences in statistical learning. Experiment 1 charts the micro-level trajectory for statistical learning of nonadjacent dependencies and provides an on-line index of individual differences therein. In Experiment 2, these differences are then shown to predict variations in participants’ on-line processing of long-distance dependencies involving center-embedded relative clauses. The findings suggest that individual differences in the ability to learn from experience through statistical learning may contribute to variations in linguistic performance. PMID:21833201
Franco, Ana; Gaillard, Vinciane; Cleeremans, Axel; Destrebecqz, Arnaud
2015-12-01
Statistical learning can be used to extract the words from continuous speech. Gómez, Bion, and Mehler (Language and Cognitive Processes, 26, 212-223, 2011) proposed an online measure of statistical learning: They superimposed auditory clicks on a continuous artificial speech stream made up of a random succession of trisyllabic nonwords. Participants were instructed to detect these clicks, which could be located either within or between words. The results showed that, over the length of exposure, reaction times (RTs) increased more for within-word than for between-word clicks. This result has been accounted for by means of statistical learning of the between-word boundaries. However, even though statistical learning occurs without an intention to learn, it nevertheless requires attentional resources. Therefore, this process could be affected by a concurrent task such as click detection. In the present study, we evaluated the extent to which the click detection task indeed reflects successful statistical learning. Our results suggest that the emergence of RT differences between within- and between-word click detection is neither systematic nor related to the successful segmentation of the artificial language. Therefore, instead of being an online measure of learning, the click detection task seems to interfere with the extraction of statistical regularities.
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…
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…
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…
Dissociable behavioural outcomes of visual statistical learning
Turk-Browne, Nicholas B.; Seitz, Aaron R.
2016-01-01
Statistical learning refers to the extraction of probabilistic relationships between stimuli and is increasingly used as a method to understand learning processes. However, numerous cognitive processes are sensitive to the statistical relationships between stimuli and any one measure of learning may conflate these processes; to date little research has focused on differentiating these processes. To understand how multiple processes underlie statistical learning, here we compared, within the same study, operational measures of learning from different tasks that may be differentially sensitive to these processes. In Experiment 1, participants were visually exposed to temporal regularities embedded in a stream of shapes. Their task was to periodically detect whether a shape, whose contrast was staircased to a threshold level, was present or absent. Afterwards, they completed a search task, where statistically predictable shapes were found more quickly. We used the search task to label shape pairs as “learned” or “non-learned”, and then used these labels to analyse the detection task. We found a dissociation between learning on the search task and the detection task where only non-learned pairs showed learning effects in the detection task. This finding was replicated in further experiments with recognition memory (Experiment 2) and associative learning tasks (Experiment 3). Taken together, these findings are consistent with the view that statistical learning may comprise a family of processes that can produce dissociable effects on different aspects of behaviour. PMID:27478399
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 methods in language processing.
Abney, Steven
2011-05-01
The term statistical methods here refers to a methodology that has been dominant in computational linguistics since about 1990. It is characterized by the use of stochastic models, substantial data sets, machine learning, and rigorous experimental evaluation. The shift to statistical methods in computational linguistics parallels a movement in artificial intelligence more broadly. Statistical methods have so thoroughly permeated computational linguistics that almost all work in the field draws on them in some way. There has, however, been little penetration of the methods into general linguistics. The methods themselves are largely borrowed from machine learning and information theory. We limit attention to that which has direct applicability to language processing, though the methods are quite general and have many nonlinguistic applications. Not every use of statistics in language processing falls under statistical methods as we use the term. Standard hypothesis testing and experimental design, for example, are not covered in this article. WIREs Cogni Sci 2011 2 315-322 DOI: 10.1002/wcs.111 For further resources related to this article, please visit the WIREs website.
Writing to Learn Statistics in an Advanced Placement Statistics Course
ERIC Educational Resources Information Center
Northrup, Christian Glenn
2012-01-01
This study investigated the use of writing in a statistics classroom to learn if writing provided a rich description of problem-solving processes of students as they solved problems. Through analysis of 329 written samples provided by students, it was determined that writing provided a rich description of problem-solving processes and enabled…
Statistics for Learning Genetics
ERIC Educational Resources Information Center
Charles, Abigail Sheena
2012-01-01
This study investigated the knowledge and skills that biology students may need to help them understand statistics/mathematics as it applies to genetics. The data are based on analyses of current representative genetics texts, practicing genetics professors' perspectives, and more directly, students' perceptions of, and performance in,…
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…
Self-Regulated Learning Strategies in Relation with Statistics Anxiety
ERIC Educational Resources Information Center
Kesici, Sahin; Baloglu, Mustafa; Deniz, M. Engin
2011-01-01
Dealing with students' attitudinal problems related to statistics is an important aspect of statistics instruction. Employing the appropriate learning strategies may have a relationship with anxiety during the process of statistics learning. Thus, the present study investigated multivariate relationships between self-regulated learning strategies…
Statistical Learning Is Related to Early Literacy-Related Skills
ERIC Educational Resources Information Center
Spencer, Mercedes; Kaschak, Michael P.; Jones, John L.; Lonigan, Christopher J.
2015-01-01
It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one's environment, plays a role in young children's acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from…
Functional Differences between Statistical Learning with and without Explicit Training
ERIC Educational Resources Information Center
Batterink, Laura J.; Reber, Paul J.; Paller, Ken A.
2015-01-01
Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and…
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…
Implicit and explicit contributions to statistical learning
Batterink, Laura J.; Reber, Paul J.; Neville, Helen J.; Paller, Ken A.
2015-01-01
Statistical learning allows learners to detect regularities in the environment and appears to emerge automatically as a consequence of experience. Statistical learning paradigms bear many similarities to those of artificial grammar learning and other types of implicit learning. However, whether learning effects in statistical learning tasks are driven by implicit knowledge has not been thoroughly examined. The present study addressed this gap by examining the role of implicit and explicit knowledge within the context of a typical auditory statistical learning paradigm. Learners were exposed to a continuous stream of repeating nonsense words. Learning was tested (a) directly via a forced-choice recognition test combined with a remember/know procedure and (b) indirectly through a novel reaction time (RT) test. Behavior and brain potentials revealed statistical learning effects with both tests. On the recognition test, accurate responses were associated with subjective feelings of stronger recollection, and learned nonsense words relative to nonword foils elicited an enhanced late positive potential indicative of explicit knowledge. On the RT test, both RTs and P300 amplitudes differed as a function of syllable position, reflecting facilitation attributable to statistical learning. Explicit stimulus recognition did not correlate with RT or P300 effects on the RT test. These results provide evidence that explicit knowledge is accrued during statistical learning, while bringing out the possibility that dissociable implicit representations are acquired in parallel. The commonly used recognition measure primarily reflects explicit knowledge, and thus may underestimate the total amount of knowledge produced by statistical learning. Indirect measures may be more sensitive indices of learning, capturing knowledge above and beyond what is reflected by recognition accuracy. PMID:26034344
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.
Statistical quality control for VLSIC fabrication processes
Mozumder, P.K.
1989-01-01
As the complexity of VLSICs increase and the device dimension shrink, random fluctuations become the main reason limiting the par metric yield. Whenever a new process is developed, the initial yield are low. The rate of climbing the learning curve is slow, i.e., the time necessary to bring the yield above an economically acceptable value can be unacceptably long, resulting in lost revenue and competitive edge in the market. The slow rates of climbing the learning curve and the low initial yields can be countered by using design methodologies that take into account the random fluctuations in the fabrication processes, and using statistical on-line and off-line control during the wafer fabrication. An integrated CAD-CAM approach with profit maximization as the objective is necessary to design and fabricate present day VLSICs. In this thesis the author proposes a methodology for monitoring and statistically controlling VLSIC manufacturing processes as part of an integrated CAD-CAM system. Present day statistical quality control systems fail to function satisfactorily due to lack of in-situ and in-line data, and absence of statistical techniques that take into account the multi-dimensionality of the data. A concerted effort has to be made to increase the number of in-situ parameters that are measured during the fabrication process using new generation equipment and sensors. Algorithms for identifying the minimal set of observable in-situ and in-line parameters that have to be measured to monitor the fabrication process are presented. The methodology for statistical quality control is based on the exploration of the multivariate distribution of the observed in-process parameters in the region of acceptability specified by the customer. Criteria for comparing the distributions of the normal process to that of the process under control are used to make the quality control decisions.
The unrealized promise of infant statistical word-referent learning
Smith, Linda B.; Suanda, Sumarga H.; Yu, Chen
2014-01-01
Recent theory and experiments offer a new solution as to how infant learners may break into word learning, by using cross-situational statistics to find the underlying word-referent mappings. Computational models demonstrate the in-principle plausibility of this statistical learning solution and experimental evidence shows that infants can aggregate and make statistically appropriate decisions from word-referent co-occurrence data. We review these contributions and then identify the gaps in current knowledge that prevent a confident conclusion about whether cross-situational learning is the mechanism through which infants break into word learning. We propose an agenda to address that gap that focuses on detailing the statistics in the learning environment and the cognitive processes that make use of those statistics. PMID:24637154
Representational Versatility in Learning Statistics
ERIC Educational Resources Information Center
Graham, Alan T.; Thomas, Michael O. J.
2005-01-01
Statistical data can be represented in a number of qualitatively different ways, the choice depending on the following three conditions: the concepts to be investigated; the nature of the data; and the purpose for which they were collected. This paper begins by setting out frameworks that describe the nature of statistical thinking in schools, and…
Functional differences between statistical learning with and without explicit training
Reber, Paul J.; Paller, Ken A.
2015-01-01
Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and prepare for incoming input. In this study, we ask whether the function of statistical learning may be enhanced through supplementary explicit training, in which underlying regularities are explicitly taught rather than simply abstracted through exposure. Learners were randomly assigned either to an explicit group or an implicit group. All learners were exposed to a continuous stream of repeating nonsense words. Prior to this implicit training, learners in the explicit group received supplementary explicit training on the nonsense words. Statistical learning was assessed through a speeded reaction-time (RT) task, which measured the extent to which learners used acquired statistical knowledge to optimize online processing. Both RTs and brain potentials revealed significant differences in online processing as a function of training condition. RTs showed a crossover interaction; responses in the explicit group were faster to predictable targets and marginally slower to less predictable targets relative to responses in the implicit group. P300 potentials to predictable targets were larger in the explicit group than in the implicit group, suggesting greater recruitment of controlled, effortful processes. Taken together, these results suggest that information abstracted through passive exposure during statistical learning may be processed more automatically and with less effort than information that is acquired explicitly. PMID:26472644
Statistical Inference at Work: Statistical Process Control as an Example
ERIC Educational Resources Information Center
Bakker, Arthur; Kent, Phillip; Derry, Jan; Noss, Richard; Hoyles, Celia
2008-01-01
To characterise statistical inference in the workplace this paper compares a prototypical type of statistical inference at work, statistical process control (SPC), with a type of statistical inference that is better known in educational settings, hypothesis testing. Although there are some similarities between the reasoning structure involved in…
Screencast Tutorials Enhance Student Learning of Statistics
ERIC Educational Resources Information Center
Lloyd, Steven A.; Robertson, Chuck L.
2012-01-01
Although the use of computer-assisted instruction has rapidly increased, there is little empirical research evaluating these technologies, specifically within the context of teaching statistics. The authors assessed the effect of screencast tutorials on learning outcomes, including statistical knowledge, application, and interpretation. Students…
Rhythmic Grouping Biases Constrain Infant Statistical Learning
ERIC Educational Resources Information Center
Hay, Jessica F.; Saffran, Jenny R.
2012-01-01
Linguistic stress and sequential statistical cues to word boundaries interact during speech segmentation in infancy. However, little is known about how the different acoustic components of stress constrain statistical learning. The current studies were designed to investigate whether intensity and duration each function independently as cues to…
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 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.
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
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
2016-01-01
It has recently been suggested that movement variability directly increases the speed of motor learning. Here we use computational modeling of motor adaptation to show that variability can have a broad range of effects on learning, both negative and positive. Experimentally, we also find contributing and decelerating effects. Lastly, through a meta-analysis of published papers, we verify that across a wide range of experiments, movement variability has no statistical relation with learning rate. While motor learning is a complex process that can be modeled, further research is needed to understand the relative importance of the involved factors. PMID:27606808
The Statistical 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
Learning the Language of Statistics: Challenges and Teaching Approaches
ERIC Educational Resources Information Center
Dunn, Peter K.; Carey, Michael D.; Richardson, Alice M.; McDonald, Christine
2016-01-01
Learning statistics requires learning the language of statistics. Statistics draws upon words from general English, mathematical English, discipline-specific English and words used primarily in statistics. This leads to many linguistic challenges in teaching statistics and the way in which the language is used in statistics creates an extra layer…
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)…
Statistical learning under incidental versus intentional conditions.
Arciuli, Joanne; Torkildsen, Janne von Koss; Stevens, David J; Simpson, Ian C
2014-01-01
Statistical learning (SL) studies have shown that participants are able to extract regularities in input they are exposed to without any instruction to do so. This and other findings, such as the fact that participants are often unable to verbalize their acquired knowledge, suggest that SL can occur implicitly or incidentally. Interestingly, several studies using the related paradigms of artificial grammar learning and serial response time tasks have shown that explicit instructions can aid learning under certain conditions. Within the SL literature, however, very few studies have contrasted incidental and intentional learning conditions. The aim of the present study was to investigate the effect of having prior knowledge of the statistical regularities in the input when undertaking a task of visual sequential SL. Specifically, we compared the degree of SL exhibited by participants who were informed (intentional group) versus those who were uninformed (incidental group) about the presence of embedded triplets within a familiarization stream. Somewhat surprisingly, our results revealed that there were no statistically significant differences (and only a small effect size) in the amount of SL exhibited between the intentional versus the incidental groups. We discuss the ways in which this result can be interpreted and suggest that short presentation times for stimuli in the familiarization stream in our study may have limited the opportunity for explicit learning. This suggestion is in line with recent research revealing a statistically significant difference (and a large effect size) between intentional versus incidental groups using a very similar visual sequential SL task, but with longer presentation times. Finally, we outline a number of directions for future research. PMID:25071692
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
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.
Learning algorithms for perceptrons from statistical physics
NASA Astrophysics Data System (ADS)
Gordon, Mirta B.; Peretto, Pierre; Berchier, Dominique
1993-02-01
Learning algorithms for perceptrons are deduced from statistical mechanics. Thermodynamical quantities are used as cost functions which may be extremalized by gradient dynamics to find the synaptic efficacies that store the learning set of patterns. The learning rules so obtained are classified in two categories, following the statistics used to derive the cost functions, namely, Boltzmann statistics, and Fermi statistics. In the limits of zero or infinite temperatures some of the rules behave like already known algorithms, but new strategies for learning are obtained at finite temperatures, which minimize the number of errors on the training set. Nous déduisons des algorithmes d'apprentissage pour des perceptrons à partir de considérations de mécanique statistique. Des quantités thermodynamiques sont considérées comme des fonctions de coût, dont on obtient, par une dynamique de gradient, les efficacités synaptiques qui apprennent l'ensemble d'apprentissage. Les règles ainsi obtenues sont classées en deux catégories suivant les statistiques, de Boltzmann ou de Fermi, utilisées pour dériver les fonctions de coût. Dans les limites de températures nulle ou infinie, la plupart des règles trouvées tendent vers les algorithmes connus, mais à température finie on trouve des stratégies nouvelles, qui minimisent le nombre d'erreurs dans l'ensemble d'apprentissage.
Word Recognition Reflects Dimension-Based Statistical Learning
ERIC Educational Resources Information Center
Idemaru, Kaori; Holt, Lori L.
2011-01-01
Speech processing requires sensitivity to long-term regularities of the native language yet demands listeners to flexibly adapt to perturbations that arise from talker idiosyncrasies such as nonnative accent. The present experiments investigate whether listeners exhibit "dimension-based statistical learning" of correlations between acoustic…
A system for learning statistical motion patterns.
Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve
2006-09-01
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction. PMID:16929731
Which statistics should tropical biologists learn?
Loaiza Velásquez, Natalia; González Lutz, María Isabel; Monge-Nájera, Julián
2011-09-01
Tropical biologists study the richest and most endangered biodiversity in the planet, and in these times of climate change and mega-extinctions, the need for efficient, good quality research is more pressing than in the past. However, the statistical component in research published by tropical authors sometimes suffers from poor quality in data collection; mediocre or bad experimental design and a rigid and outdated view of data analysis. To suggest improvements in their statistical education, we listed all the statistical tests and other quantitative analyses used in two leading tropical journals, the Revista de Biología Tropical and Biotropica, during a year. The 12 most frequent tests in the articles were: Analysis of Variance (ANOVA), Chi-Square Test, Student's T Test, Linear Regression, Pearson's Correlation Coefficient, Mann-Whitney U Test, Kruskal-Wallis Test, Shannon's Diversity Index, Tukey's Test, Cluster Analysis, Spearman's Rank Correlation Test and Principal Component Analysis. We conclude that statistical education for tropical biologists must abandon the old syllabus based on the mathematical side of statistics and concentrate on the correct selection of these and other procedures and tests, on their biological interpretation and on the use of reliable and friendly freeware. We think that their time will be better spent understanding and protecting tropical ecosystems than trying to learn the mathematical foundations of statistics: in most cases, a well designed one-semester course should be enough for their basic requirements.
Context influences conscious appraisal of cross situational statistical learning
Poepsel, Timothy J.; Weiss, Daniel J.
2014-01-01
Previous research in cross-situational statistical learning has established that people can track statistical information across streams in order to map nonce words to their referent objects (Yu and Smith, 2007). Under some circumstances, learners are able to acquire multiple mappings for a single object (e.g., Yurovsky and Yu, 2008). Here we explore whether having a contextual cue associated with a new mapping may facilitate this process, or the conscious awareness of learning. Using a cross-situational statistical learning paradigm, in which learners could form both 1:1 and 2:1 word–object mappings over two phases of learning, we collected confidence ratings during familiarization and provided a retrospective test to gage learning. In Condition 1, there were no contextual cues to indicate a change in mappings (baseline). Conditions 2 and 3 added contextual cues (a change in speaker voice or explicit instructions, respectively) to the second familiarization phase to determine their effects on the trajectory of learning. While contextual cues did not facilitate acquisition of 2:1 mappings as assessed by retrospective measures, confidence ratings for these mappings were significantly higher in contextual cue conditions compared to the baseline condition with no cues. These results suggest that contextual cues corresponding to changes in the input may influence the conscious awareness of learning. 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…
Community Service-Learning in Statistics: Course Design and Assessment
ERIC Educational Resources Information Center
Hydorn, Debra L.
2007-01-01
Service-learning projects are a useful method for students to learn both the practice and value of statistical methods. Effective service learning, however, depends on several factors and can be implemented according to a variety of models. In this article, different models for incorporating service-learning in statistics courses are presented…
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.
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.
Learning Support and Students Studying Mathematics and Statistics
ERIC Educational Resources Information Center
MacGillivray, Helen
2009-01-01
Learning support in mathematics and statistics has arisen and developed in response to student needs in many universities. After discussing background to such support, this article gives a brief overview of learning support in mathematics and statistics in Australian universities as found in an Australian Learning and Teaching Council project and…
Employer Learning and Statistical Discrimination. National Longitudinal Surveys Discussion Paper.
ERIC Educational Resources Information Center
Altonji, Joseph G.; Pierret, Charles R.
The relationship between employer learning and statistical discrimination was explored through a statistical analysis that included a test for statistical discrimination or "rational" stereotyping in environments where agents learn over time. The test is used to study the working hypothesis that, because firms have only limited information about…
Concurrent movement impairs incidental but not intentional statistical learning.
Stevens, David J; Arciuli, Joanne; Anderson, David I
2015-07-01
The effect of concurrent movement on incidental versus intentional statistical learning was examined in two experiments. In Experiment 1, participants learned the statistical regularities embedded within familiarization stimuli implicitly, whereas in Experiment 2 they were made aware of the embedded regularities and were instructed explicitly to learn these regularities. Experiment 1 demonstrated that while the control group were able to learn the statistical regularities, the resistance-free cycling group and the exercise group did not demonstrate learning. This is in contrast with the findings of Experiment 2, where all three groups demonstrated significant levels of learning. The results suggest that the movement demands, rather than the physiological stress, interfered with statistical learning. We suggest movement activates the striatum, which is not only responsible for motor control but also plays a role in incidental learning.
Statistics Anxiety, Trait Anxiety, Learning Behavior, and Academic Performance
ERIC Educational Resources Information Center
Macher, Daniel; Paechter, Manuela; Papousek, Ilona; Ruggeri, Kai
2012-01-01
The present study investigated the relationship between statistics anxiety, individual characteristics (e.g., trait anxiety and learning strategies), and academic performance. Students enrolled in a statistics course in psychology (N = 147) filled in a questionnaire on statistics anxiety, trait anxiety, interest in statistics, mathematical…
Chiou, Chei-Chang; Wang, Yu-Min; Lee, Li-Tze
2014-08-01
Statistical knowledge is widely used in academia; however, statistics teachers struggle with the issue of how to reduce students' statistics anxiety and enhance students' statistics learning. This study assesses the effectiveness of a "one-minute paper strategy" in reducing students' statistics-related anxiety and in improving students' statistics-related achievement. Participants were 77 undergraduates from two classes enrolled in applied statistics courses. An experiment was implemented according to a pretest/posttest comparison group design. The quasi-experimental design showed that the one-minute paper strategy significantly reduced students' statistics anxiety and improved students' statistics learning achievement. The strategy was a better instructional tool than the textbook exercise for reducing students' statistics anxiety and improving students' statistics achievement.
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.
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.
Yurovsky, Daniel; Fricker, Damian C; Yu, Chen; Smith, Linda B
2014-02-01
A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word's meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: partial knowledge of one word-object mapping can speed up the acquisition of other word-object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word-object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word's meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data.
The role of partial knowledge in statistical word learning
Fricker, Damian C.; Yu, Chen; Smith, Linda B.
2013-01-01
A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word's meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: Partial knowledge of one word–object mapping can speed up the acquisition of other word–object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word–object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word's meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data. PMID:23702980
The role of partial knowledge in statistical word learning.
Yurovsky, Daniel; Fricker, Damian C; Yu, Chen; Smith, Linda B
2014-02-01
A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word's meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: partial knowledge of one word-object mapping can speed up the acquisition of other word-object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word-object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word's meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data. 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…
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…
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.…
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 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.
Collaboration in Learning and Teaching Statistics
ERIC Educational Resources Information Center
Roseth, Cary J.; Garfield, Joan B.; Ben-Zvi, Dani
2008-01-01
This paper provides practical examples of how statistics educators may apply a cooperative framework to classroom teaching and teacher collaboration. Building on the premise that statistics instruction ought to resemble statistical practice, an inherently cooperative enterprise, our purpose is to highlight specific ways in which cooperative…
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
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…
iMinerva: A Mathematical Model of Distributional Statistical Learning
ERIC Educational Resources Information Center
Thiessen, Erik D.; Pavlik, Philip I., Jr.
2013-01-01
Statistical learning refers to the ability to identify structure in the input based on its statistical properties. For many linguistic structures, the relevant statistical features are distributional: They are related to the frequency and variability of exemplars in the input. These distributional regularities have been suggested to play a role in…
Some Variables in Relation to Students' Anxiety in Learning Statistics.
ERIC Educational Resources Information Center
Sutarso, Toto
The purpose of this study was to investigate some variables that relate to students' anxiety in learning statistics. The variables included sex, class level, students' achievement, school, mathematical background, previous statistics courses, and race. The instrument used was the 24-item Students' Attitudes Toward Statistics (STATS), which was…
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
Residence Time Statistics for N Renewal Processes
NASA Astrophysics Data System (ADS)
Burov, S.; Barkai, E.
2011-10-01
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.
Point process statistics in atom probe tomography.
Philippe, T; Duguay, S; Grancher, G; Blavette, D
2013-09-01
We present a review of spatial point processes as statistical models that we have designed for the analysis and treatment of atom probe tomography (APT) data. As a major advantage, these methods do not require sampling. The mean distance to nearest neighbour is an attractive approach to exhibit a non-random atomic distribution. A χ(2) test based on distance distributions to nearest neighbour has been developed to detect deviation from randomness. Best-fit methods based on first nearest neighbour distance (1 NN method) and pair correlation function are presented and compared to assess the chemical composition of tiny clusters. Delaunay tessellation for cluster selection has been also illustrated. These statistical tools have been applied to APT experiments on microelectronics materials.
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.
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
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
Experience and grammatical agreement: Statistical learning shapes number agreement production
Haskell, Todd R.; Thornton, Robert; MacDonald, Maryellen C.
2009-01-01
A robust result in research on the production of grammatical agreement is that speakers are more likely to produce an erroneous verb with phrases such as the key to the cabinets, with a singular noun followed by a plural one, than with phrases such as the keys to the cabinet, where a plural noun is followed by a singular. These asymmetries are thought to reflect core language production processes. Previous accounts have attributed error patterns to a syntactic number feature present on plurals but not singulars. An alternative approach is presented in which a process similar to structural priming contributes to the error asymmetry via speakers' past experiences with related agreement constructions. A corpus analysis and two agreement production studies test this account. The results suggest that agreement production is shaped by statistical learning from past language experience. Implications for accounts of agreement are discussed. PMID:19942213
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.
Right Hemisphere Dominance in Visual Statistical Learning
ERIC Educational Resources Information Center
Roser, Matthew E.; Fiser, Jozsef; Aslin, Richard N.; Gazzaniga, Michael S.
2011-01-01
Several studies report a right hemisphere advantage for visuospatial integration and a left hemisphere advantage for inferring conceptual knowledge from patterns of covariation. The present study examined hemispheric asymmetry in the implicit learning of new visual feature combinations. A split-brain patient and normal control participants viewed…
Teaching, Learning and Assessing Statistical Problem Solving
ERIC Educational Resources Information Center
Marriott, John; Davies, Neville; Gibson, Liz
2009-01-01
In this paper we report the results from a major UK government-funded project, started in 2005, to review statistics and handling data within the school mathematics curriculum for students up to age 16. As a result of a survey of teachers we developed new teaching materials that explicitly use a problem-solving approach for the teaching and…
Probability & Statistics: Modular Learning Exercises. Teacher Edition
ERIC Educational Resources Information Center
Actuarial Foundation, 2012
2012-01-01
The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The modules also introduce students to real world math concepts and problems that property and casualty actuaries come across in their work. They are designed to be used by teachers and…
Probability & Statistics: Modular Learning Exercises. Student Edition
ERIC Educational Resources Information Center
Actuarial Foundation, 2012
2012-01-01
The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The materials are centered on the fictional town of Happy Shores, a coastal community which is at risk for hurricanes. Actuaries at an insurance company figure out the risks and…
Statistical Learning as a Basis for Social Understanding in Children
ERIC Educational Resources Information Center
Ruffman, Ted; Taumoepeau, Mele; Perkins, Chris
2012-01-01
Many authors have argued that infants understand goals, intentions, and beliefs. We posit that infants' success on such tasks might instead reveal an understanding of behaviour, that infants' proficient statistical learning abilities might enable such insights, and that maternal talk scaffolds children's learning about the social world as well. We…
Memory Constraints on Infants' Cross-Situational Statistical Learning
ERIC Educational Resources Information Center
Vlach, Haley A.; Johnson, Scott P.
2013-01-01
Infants are able to map linguistic labels to referents in the world by tracking co-occurrence probabilities across learning events, a behavior often termed "cross-situational statistical learning." This study builds upon existing research by examining infants' developing ability to aggregate and retrieve word-referent pairings over time. 16- and…
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…
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.
1992-09-01
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
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.
Distant Melodies: Statistical Learning of Nonadjacent Dependencies in Tone Sequences
ERIC Educational Resources Information Center
Creel, Sarah C.; Newport, Elissa L.; Aslin, Richard N.
2004-01-01
Human listeners can keep track of statistical regularities among temporally adjacent elements in both speech and musical streams. However, for speech streams, when statistical regularities occur among nonadjacent elements, only certain types of patterns are acquired. Here, using musical tone sequences, the authors investigate nonadjacent learning.…
Isolated Words Enhance Statistical Language Learning in Infancy
ERIC Educational Resources Information Center
Lew-Williams, Casey; Pelucchi, Bruna; Saffran, Jenny R.
2011-01-01
Infants are adept at tracking statistical regularities to identify word boundaries in pause-free speech. However, researchers have questioned the relevance of statistical learning mechanisms to language acquisition, since previous studies have used simplified artificial languages that ignore the variability of real language input. The experiments…
Batch Statistical Process Monitoring Approach to a Cocrystallization Process.
Sarraguça, Mafalda C; Ribeiro, Paulo R S; Santos, Adenilson O Dos; Lopes, João A
2015-12-01
Cocrystals are defined as crystalline structures composed of two or more compounds that are solid at room temperature held together by noncovalent bonds. Their main advantages are the increase of solubility, bioavailability, permeability, stability, and at the same time retaining active pharmaceutical ingredient bioactivity. The cocrystallization between furosemide and nicotinamide by solvent evaporation was monitored on-line using near-infrared spectroscopy (NIRS) as a process analytical technology tool. The near-infrared spectra were analyzed using principal component analysis. Batch statistical process monitoring was used to create control charts to perceive the process trajectory and define control limits. Normal and non-normal operating condition batches were performed and monitored with NIRS. The use of NIRS associated with batch statistical process models allowed the detection of abnormal variations in critical process parameters, like the amount of solvent or amount of initial components present in the cocrystallization.
Problem Based Learning and the scientific process
NASA Astrophysics Data System (ADS)
Schuchardt, Daniel Shaner
This research project was developed to inspire students to constructively use problem based learning and the scientific process to learn middle school science content. The student population in this study consisted of male and female seventh grade students. Students were presented with authentic problems that are connected to physical and chemical properties of matter. The intent of the study was to have students use the scientific process of looking at existing knowledge, generating learning issues or questions about the problems, and then developing a course of action to research and design experiments to model resolutions to the authentic problems. It was expected that students would improve their ability to actively engage with others in a problem solving process to achieve a deeper understanding of Michigan's 7th Grade Level Content Expectations, the Next Generation Science Standards, and a scientific process. Problem based learning was statistically effective in students' learning of the scientific process. Students statistically showed improvement on pre to posttest scores. The teaching method of Problem Based Learning was effective for seventh grade science students at Dowagiac Middle School.
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.
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…
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
Continuum Statistics of the Airy2 Process
NASA Astrophysics Data System (ADS)
Corwin, Ivan; Quastel, Jeremy; Remenik, Daniel
2013-01-01
We develop an exact determinantal formula for the probability that the Airy_2 process is bounded by a function g on a finite interval. As an application, we provide a direct proof that {sup({A}2(x)-x^2)} is distributed as a GOE random variable. Both the continuum formula and the GOE result have applications in the study of the end point of an unconstrained directed polymer in a disordered environment. We explain Johansson's (Commun. Math. Phys. 242(1-2):277-329, 2003) observation that the GOE result follows from this polymer interpretation and exact results within that field. In a companion paper (Moreno Flores et al. in Commun. Math. Phys. 2012) these continuum statistics are used to compute the distribution of the endpoint of directed polymers.
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 Mechanics of Node-Perturbation Learning for Nonlinear Perceptron
NASA Astrophysics Data System (ADS)
Hara, Kazuyuki; Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato
2013-05-01
Node-perturbation learning is a type of statistical gradient descent algorithm that can be applied to problems where the objective function is not explicitly formulated, including reinforcement learning. Node-perturbation learning with M linear perceptrons has previously been analyzed using the methods of statistical mechanics. It was shown that cross-talk noise, which originates from the error of the other outputs, increases the generalization error as the number of outputs increases. On the other hand, a nonlinear perceptron has several advantages over a linear perceptron, such as the ability to use nonlinear outputs, learnability, storage capacity, and so forth. However, node-perturbation for a nonlinear perceptron has yet to be analyzed theoretically. In this paper, we derive a learning rule of node-perturbation learning for a nonlinear perceptron within the framework of REINFORCE learning and analyze the learning behavior by using statistical mechanical methods. From the results, we found that the signal and cross-talk terms of the order parameter Q have different forms for a nonlinear perceptron. Moreover, the increase in the generalization error with increasing number of outputs is less than for a linear perceptron.
Statistical learning is lasting and consistent over time.
Arciuli, Joanne; Simpson, Ian Craig
2012-05-31
Implicit detection of statistical regularities is thought to be a ubiquitous facet of cognition; yet, we know little about statistical learning (SL) over time. A recent study showed that visual SL can be observed at 24 h post stimulus (Kim et al., 2009 [14]). Here we sought to obtain a finer-grained picture of visual SL over time. We employed an embedded triplet paradigm and delayed presentation of the surprise test phase, in relation to the initial familiarisation phase, across five time periods: 30 min, 1 h, 2 h, 4 h and 24 h. Results revealed a significant degree of SL at each delay period. Moreover, the degree of SL was consistent across the five delay periods. These results suggest that visual SL is remarkably consistent over time. It does not appear to be fragile and does not appear to be enhanced by sleep in healthy adults. This robustness is desirable in a mechanism thought to underpin a broad range of mental activities including language processing. Future research might use the methodology we report here to examine whether similarly stable levels of SL can be observed in individuals with language impairment, such as those with SLI and dyslexia, compared with typical peers.
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
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.
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
Infant Directed Speech Enhances Statistical Learning in Newborn Infants: An ERP Study.
Bosseler, Alexis N; Teinonen, Tuomas; Tervaniemi, Mari; Huotilainen, Minna
2016-01-01
Statistical learning and the social contexts of language addressed to infants are hypothesized to play important roles in early language development. Previous behavioral work has found that the exaggerated prosodic contours of infant-directed speech (IDS) facilitate statistical learning in 8-month-old infants. Here we examined the neural processes involved in on-line statistical learning and investigated whether the use of IDS facilitates statistical learning in sleeping newborns. Event-related potentials (ERPs) were recorded while newborns were exposed to12 pseudo-words, six spoken with exaggerated pitch contours of IDS and six spoken without exaggerated pitch contours (ADS) in ten alternating blocks. We examined whether ERP amplitudes for syllable position within a pseudo-word (word-initial vs. word-medial vs. word-final, indicating statistical word learning) and speech register (ADS vs. IDS) would interact. The ADS and IDS registers elicited similar ERP patterns for syllable position in an early 0-100 ms component but elicited different ERP effects in both the polarity and topographical distribution at 200-400 ms and 450-650 ms. These results provide the first evidence that the exaggerated pitch contours of IDS result in differences in brain activity linked to on-line statistical learning in sleeping newborns.
Infant Directed Speech Enhances Statistical Learning in Newborn Infants: An ERP Study
Teinonen, Tuomas; Tervaniemi, Mari; Huotilainen, Minna
2016-01-01
Statistical learning and the social contexts of language addressed to infants are hypothesized to play important roles in early language development. Previous behavioral work has found that the exaggerated prosodic contours of infant-directed speech (IDS) facilitate statistical learning in 8-month-old infants. Here we examined the neural processes involved in on-line statistical learning and investigated whether the use of IDS facilitates statistical learning in sleeping newborns. Event-related potentials (ERPs) were recorded while newborns were exposed to12 pseudo-words, six spoken with exaggerated pitch contours of IDS and six spoken without exaggerated pitch contours (ADS) in ten alternating blocks. We examined whether ERP amplitudes for syllable position within a pseudo-word (word-initial vs. word-medial vs. word-final, indicating statistical word learning) and speech register (ADS vs. IDS) would interact. The ADS and IDS registers elicited similar ERP patterns for syllable position in an early 0–100 ms component but elicited different ERP effects in both the polarity and topographical distribution at 200–400 ms and 450–650 ms. These results provide the first evidence that the exaggerated pitch contours of IDS result in differences in brain activity linked to on-line statistical learning in sleeping newborns. PMID:27617967
Statistical 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 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
Linking Sounds to Meanings: Infant Statistical Learning in a Natural Language
ERIC Educational Resources Information Center
Hay, Jessica F.; Pelucchi, Bruna; Estes, Katharine Graf; Saffran, Jenny R.
2011-01-01
The processes of infant word segmentation and infant word learning have largely been studied separately. However, the ease with which potential word forms are segmented from fluent speech seems likely to influence subsequent mappings between words and their referents. To explore this process, we tested the link between the statistical coherence of…
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…
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…
Measuring University Students' Approaches to Learning Statistics: An Invariance Study
ERIC Educational Resources Information Center
Chiesi, Francesca; Primi, Caterina; Bilgin, Ayse Aysin; Lopez, Maria Virginia; del Carmen Fabrizio, Maria; Gozlu, Sitki; Tuan, Nguyen Minh
2016-01-01
The aim of the current study was to provide evidence that an abbreviated version of the Approaches and Study Skills Inventory for Students (ASSIST) was invariant across different languages and educational contexts in measuring university students' learning approaches to statistics. Data were collected on samples of university students attending…
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
Human Motion Retrieval Based on Statistical Learning and Bayesian Fusion
Xiao, Qinkun; Song, Ren
2016-01-01
A novel motion retrieval approach based on statistical learning and Bayesian fusion is presented. The approach includes two primary stages. (1) In the learning stage, fuzzy clustering is utilized firstly to get the representative frames of motions, and the gesture features of the motions are extracted to build a motion feature database. Based on the motion feature database and statistical learning, the probability distribution function of different motion classes is obtained. (2) In the motion retrieval stage, the query motion feature is extracted firstly according to stage (1). Similarity measurements are then conducted employing a novel method that combines category-based motion similarity distances with similarity distances based on canonical correlation analysis. The two motion distances are fused using Bayesian estimation, and the retrieval results are ranked according to the fused values. The effectiveness of the proposed method is verified experimentally. PMID:27732673
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
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
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.
Statistical properties of several models of fractional random point processes
NASA Astrophysics Data System (ADS)
Bendjaballah, C.
2011-08-01
Statistical properties of several models of fractional random point processes have been analyzed from the counting and time interval statistics points of view. Based on the criterion of the reduced variance, it is seen that such processes exhibit nonclassical properties. The conditions for these processes to be treated as conditional Poisson processes are examined. Numerical simulations illustrate part of the theoretical calculations.
Retirement as a Learning Process
ERIC Educational Resources Information Center
Hodkinson, Phil; Ford, Geoff; Hodkinson, Heather; Hawthorn, Ruth
2008-01-01
This article draws upon a major qualitative empirical research investigation in Great Britain to explore the relationships between retirement and learning. Though retirement is frequently viewed as an event leading to a life stage, our data show that it can perhaps be best understood as a lengthy process. This process begins well before actual…
Living and learning food processing
Technology Transfer Automated Retrieval System (TEKTRAN)
This year’s annual event promises to be both exciting and educational for those who wish to learn more about food processing. This column will provide a brief overview of the multitude of scientific sessions that reveal new research related to food processing. In addition to the symposia previewed h...
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…
Effective Formative E-Assessment of Student Learning: A Study on a Statistics Course
ERIC Educational Resources Information Center
Hodgson, Paula; Pang, Marco Y. C.
2012-01-01
The process of formative assessment in universities has the potential to engage students in reflection and to take greater ownership of their learning. We report on a study involving 104 students taking a statistics course in a degree programme in rehabilitation science in Hong Kong. The assessment strategy was redesigned to include a weekly…
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…
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.
Statistical learning analysis in neuroscience: aiming for transparency.
Hanke, Michael; Halchenko, Yaroslav O; Haxby, James V; Pollmann, Stefan
2010-01-01
Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires "neuroscience-aware" technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities. PMID:20582270
Physics-based statistical learning approach to mesoscopic model selection.
Taverniers, Søren; Haut, Terry S; Barros, Kipton; Alexander, Francis J; Lookman, Turab
2015-11-01
In materials science and many other research areas, models are frequently inferred without considering their generalization to unseen data. We apply statistical learning using cross-validation to obtain an optimally predictive coarse-grained description of a two-dimensional kinetic nearest-neighbor Ising model with Glauber dynamics (GD) based on the stochastic Ginzburg-Landau equation (sGLE). The latter is learned from GD "training" data using a log-likelihood analysis, and its predictive ability for various complexities of the model is tested on GD "test" data independent of the data used to train the model on. Using two different error metrics, we perform a detailed analysis of the error between magnetization time trajectories simulated using the learned sGLE coarse-grained description and those obtained using the GD model. We show that both for equilibrium and out-of-equilibrium GD training trajectories, the standard phenomenological description using a quartic free energy does not always yield the most predictive coarse-grained model. Moreover, increasing the amount of training data can shift the optimal model complexity to higher values. Our results are promising in that they pave the way for the use of statistical learning as a general tool for materials modeling and discovery.
Physics-based statistical learning approach to mesoscopic model selection
NASA Astrophysics Data System (ADS)
Taverniers, Søren; Haut, Terry S.; Barros, Kipton; Alexander, Francis J.; Lookman, Turab
2015-11-01
In materials science and many other research areas, models are frequently inferred without considering their generalization to unseen data. We apply statistical learning using cross-validation to obtain an optimally predictive coarse-grained description of a two-dimensional kinetic nearest-neighbor Ising model with Glauber dynamics (GD) based on the stochastic Ginzburg-Landau equation (sGLE). The latter is learned from GD "training" data using a log-likelihood analysis, and its predictive ability for various complexities of the model is tested on GD "test" data independent of the data used to train the model on. Using two different error metrics, we perform a detailed analysis of the error between magnetization time trajectories simulated using the learned sGLE coarse-grained description and those obtained using the GD model. We show that both for equilibrium and out-of-equilibrium GD training trajectories, the standard phenomenological description using a quartic free energy does not always yield the most predictive coarse-grained model. Moreover, increasing the amount of training data can shift the optimal model complexity to higher values. Our results are promising in that they pave the way for the use of statistical learning as a general tool for materials modeling and discovery.
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.
Characterizing Year 11 Students' Evaluation of a Statistical Process
ERIC Educational Resources Information Center
Pfannkuch, Maxine
2005-01-01
Evaluating the statistical process is considered a higher order skill and has received little emphasis in instruction. This study analyses thirty 15-year-old students' responses to two statistics assessment tasks, which required evaluation of a statistical investigation. The SOLO taxonomy is used as a framework to develop a hierarchy of responses.…
Plastics processing: statistics, current practices, and evaluation.
Cooke, F
1993-11-01
The health care industry uses a huge quantity of plastic materials each year. Much of the machinery currently used, or supplied, for plastics processing is unsuitable for use in a clean environment. In this article, the author outlines the reasons for the current situation and urges companies to re-examine their plastic-processing methods, whether performed in-house or subcontracted out. Some of the factors that should be considered when evaluating plastics-processing equipment are outlined to assist companies in remaining competitive and complying with impending EC regulations on clean room standards for manufacturing areas.
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.
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.
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
Statistical learning of speech, not music, in congenital amusia
Peretz, Isabelle; Saffran, Jenny; Schön, Daniele; Gosselin, Nathalie
2013-01-01
The acquisition of both speech and music uses general principles: learners extract statistical regularities present in the environment. Yet, individuals who suffer from congenital amusia (commonly called tone-deafness) have experienced lifelong difficulties in acquiring basic musical skills, while their language abilities appear essentially intact. One possible account for this dissociation between music and speech is that amusics lack normal experience with music. If given appropriate exposure, amusics might be able to acquire basic musical abilities. To test this possibility, a group of 11 adults with congenital amusia, and their matched controls, were exposed to a continuous stream of syllables or tones for 21-minute. Their task was to try to identify three-syllable nonsense words or three-tone motifs having an identical statistical structure. The results of five experiments show that amusics can learn novel words as easily as controls, whereas they systematically fail on musical materials. Thus, inappropriate musical exposure cannot fully account for the musical disorder. Implications of the results for the domain specificity of statistical learning are discussed. PMID:22524380
Effects of Reflection Prompts on Learning Outcomes and Learning Behaviour in Statistics Education
ERIC Educational Resources Information Center
Stark, Robin; Krause, Ulrike-Marie
2009-01-01
Starting from difficulties that students display when they deal with correlation analysis, an e-learning environment ("Koralle") was developed. The design was inspired by principles of situated and example-based learning. In order to facilitate reflective processes and thus enhance learning outcomes, reflection prompts were integrated into the…
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…
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
Statistical kinetics of processive molecular motors
NASA Astrophysics Data System (ADS)
Schnitzer, Mark Jacob
1999-10-01
We describe new theoretical and experimental tools for studying biological motor proteins at the single molecule scale. These tools enable measurements of molecular fuel economies, thereby providing insight into the pathways for conversion of biochemical energy into mechanical work. Kinesin is an ATP-dependent motor that moves processively along microtubules in discrete steps of 8 nm. How many molecules of ATP are hydrolysed per step? To determine this coupling ratio, we develop a fluctuation analysis, which relates the variance in records of mechanical displacement to the number of rate-limiting biochemical transitions in the engine cycle. Using fluctuation analysis and optical trapping interferometry, we determine that near zero load, single molecules of kinesin hydrolyse one ATP nucleotide per 8-nm step. To study kinesin behavior under load, we use a molecular force clamp, capable of maintaining constant loads on single kinesin motors moving processively. Analysis of records of motion under variable ATP concentrations and loads reveals that kinesin is a `tightly- coupled' motor, maintaining the 1:1 coupling ratio up to loads of ~ 5 pN. Moreover, a Michaelis-Menten analysis of velocity shows that the kinesin cycle contains at least two load- dependent transitions. The rate of one of these transitions affects ATP affinity, while the other does not. Therefore, the kinesin stall force must depend on the ATP concentration, as is demonstrated experimentally. These findings rule out existing theoretical models of kinesin motility. We develop a simple theoretical formalism describing a tightly-coupled mechanism for movement. This `energy-landscape' formalism quantitatively accounts for motile properties of RNA polymerase (RNAP), the enzyme that transcribes DNA into RNA. The shapes of RNAP force-velocity curves indicate that biochemical steps limiting transcription rates at low loads do not generate movement. Modeling suggests that high loads may halt RNAP by promoting a
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
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.
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
Another look at statistical learning theory and regularization.
Cherkassky, Vladimir; Ma, Yunqian
2009-09-01
The paper reviews and highlights distinctions between function-approximation (FA) and VC theory and methodology, mainly within the setting of regression problems and a squared-error loss function, and illustrates empirically the differences between the two when data is sparse and/or input distribution is non-uniform. In FA theory, the goal is to estimate an unknown true dependency (or 'target' function) in regression problems, or posterior probability P(y/x) in classification problems. In VC theory, the goal is to 'imitate' unknown target function, in the sense of minimization of prediction risk or good 'generalization'. That is, the result of VC learning depends on (unknown) input distribution, while that of FA does not. This distinction is important because regularization theory originally introduced under clearly stated FA setting [Tikhonov, N. (1963). On solving ill-posed problem and method of regularization. Doklady Akademii Nauk USSR, 153, 501-504; Tikhonov, N., & V. Y. Arsenin (1977). Solution of ill-posed problems. Washington, DC: W. H. Winston], has been later used under risk-minimization or VC setting. More recently, several authors [Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Advances in Computational Mathematics, 13, 1-50; Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference and prediction. Springer; Poggio, T. and Smale, S., (2003). The mathematics of learning: Dealing with data. Notices of the AMS, 50 (5), 537-544] applied constructive methodology based on regularization framework to learning dependencies from data (under VC-theoretical setting). However, such regularization-based learning is usually presented as a purely constructive methodology (with no clearly stated problem setting). This paper compares FA/regularization and VC/risk minimization methodologies in terms of underlying theoretical assumptions. The control of model
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.
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
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…
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…
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…
Domain general learning: Infants use social and non-social cues when learning object statistics
Barry, Ryan A.; Graf Estes, Katharine; Rivera, Susan M.
2015-01-01
Previous research has shown that infants can learn from social cues. But is a social cue more effective at directing learning than a non-social cue? This study investigated whether 9-month-old infants (N = 55) could learn a visual statistical regularity in the presence of a distracting visual sequence when attention was directed by either a social cue (a person) or a non-social cue (a rectangle). The results show that both social and non-social cues can guide infants’ attention to a visual shape sequence (and away from a distracting sequence). The social cue more effectively directed attention than the non-social cue during the familiarization phase, but the social cue did not result in significantly stronger learning than the non-social cue. The findings suggest that domain general attention mechanisms allow for the comparable learning seen in both conditions. PMID:25999879
Prediction during statistical learning, and implications for the implicit/explicit divide
Dale, Rick; Duran, Nicholas D.; Morehead, J. Ryan
2012-01-01
Accounts of statistical learning, both implicit and explicit, often invoke predictive processes as central to learning, yet practically all experiments employ non-predictive measures during training. We argue that the common theoretical assumption of anticipation and prediction needs clearer, more direct evidence for it during learning. We offer a novel experimental context to explore prediction, and report results from a simple sequential learning task designed to promote predictive behaviors in participants as they responded to a short sequence of simple stimulus events. Predictive tendencies in participants were measured using their computer mouse, the trajectories of which served as a means of tapping into predictive behavior while participants were exposed to very short and simple sequences of events. A total of 143 participants were randomly assigned to stimulus sequences along a continuum of regularity. Analysis of computer-mouse trajectories revealed that (a) participants almost always anticipate events in some manner, (b) participants exhibit two stable patterns of behavior, either reacting to vs. predicting future events, (c) the extent to which participants predict relates to performance on a recall test, and (d) explicit reports of perceiving patterns in the brief sequence correlates with extent of prediction. We end with a discussion of implicit and explicit statistical learning and of the role prediction may play in both kinds of learning. PMID:22723817
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
Thompson, Carla J.
2009-01-01
Since educational statistics is a core or general requirement of all students enrolled in graduate education programs, the need for high quality student engagement and appropriate authentic learning experiences is critical for promoting student interest and student success in the course. Based in authentic learning theory and engagement theory…
ERIC Educational Resources Information Center
McLoughlin, M. Padraig M. M.
2008-01-01
The author of this paper submits the thesis that learning requires doing; only through inquiry is learning achieved, and hence this paper proposes a programme of use of a modified Moore method in a Probability and Mathematical Statistics (PAMS) course sequence to teach students PAMS. Furthermore, the author of this paper opines that set theory…
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…
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.
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…
Social Processes and Pedagogy in Online Learning
ERIC Educational Resources Information Center
Hewson, Lindsay; Hughes, Chris
2005-01-01
Online learning environments offer efficient ways of interconnecting group members and satisfying their communicative needs. However, learning does not proceed through shared communication alone; all groups imply social processes and learning groups demand an additional pedagogical intention. Popular online learning systems satisfactorily enable…
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…
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…
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.
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.
Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels
Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J.
2014-01-01
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378
Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J
2014-01-01
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378
Learning through Action: Parallel Learning Processes in Children and Adults
ERIC Educational Resources Information Center
Ethridge, Elizabeth A.; Branscomb, Kathryn R.
2009-01-01
Experiential learning has become an essential part of many educational settings from infancy through adulthood. While the effectiveness of active learning has been evaluated in youth and adult settings, few known studies have compared the learning processes of children and adults within the same project. This article contrasts the active learning…
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
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.
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
Statistical learning of music- and language-like sequences and tolerance for spectral shifts.
Daikoku, Tatsuya; Yatomi, Yutaka; Yumoto, Masato
2015-02-01
In our previous study (Daikoku, Yatomi, & Yumoto, 2014), we demonstrated that the N1m response could be a marker for the statistical learning process of pitch sequence, in which each tone was ordered by a Markov stochastic model. The aim of the present study was to investigate how the statistical learning of music- and language-like auditory sequences is reflected in the N1m responses based on the assumption that both language and music share domain generality. By using vowel sounds generated by a formant synthesizer, we devised music- and language-like auditory sequences in which higher-ordered transitional rules were embedded according to a Markov stochastic model by controlling fundamental (F0) and/or formant frequencies (F1-F2). In each sequence, F0 and/or F1-F2 were spectrally shifted in the last one-third of the tone sequence. Neuromagnetic responses to the tone sequences were recorded from 14 right-handed normal volunteers. In the music- and language-like sequences with pitch change, the N1m responses to the tones that appeared with higher transitional probability were significantly decreased compared with the responses to the tones that appeared with lower transitional probability within the first two-thirds of each sequence. Moreover, the amplitude difference was even retained within the last one-third of the sequence after the spectral shifts. However, in the language-like sequence without pitch change, no significant difference could be detected. The pitch change may facilitate the statistical learning in language and music. Statistically acquired knowledge may be appropriated to process altered auditory sequences with spectral shifts. The relative processing of spectral sequences may be a domain-general auditory mechanism that is innate to humans.
Statistical 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
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
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 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…
The Process of Learning from Information.
ERIC Educational Resources Information Center
Kuhlthau, Carol Collier
1995-01-01
Presents the process of learning from information as the key concept for the library media center in the information age school. The Information Search Process Approach is described as a model for developing information skills fundamental to information literacy, and process learning is discussed. (Author/LRW)
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…
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
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,…
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 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…
"Dear Fresher …"--How Online Questionnaires Can Improve Learning and Teaching Statistics
ERIC Educational Resources Information Center
Bebermeier, Sarah; Nussbeck, Fridtjof W.; Ontrup, Greta
2015-01-01
Lecturers teaching statistics are faced with several challenges supporting students' learning in appropriate ways. A variety of methods and tools exist to facilitate students' learning on statistics courses. The online questionnaires presented in this report are a new, slightly different computer-based tool: the central aim was to support students…
A Constructivist Approach in a Blended E-Learning Environment for Statistics
ERIC Educational Resources Information Center
Poelmans, Stephan; Wessa, Patrick
2015-01-01
In this study, we report on the students' evaluation of a self-constructed constructivist e-learning environment for statistics, the compendium platform (CP). The system was built to endorse deeper learning with the incorporation of statistical reproducibility and peer review practices. The deployment of the CP, with interactive workshops and…
Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis.
Obeid, Rita; Brooks, Patricia J; Powers, Kasey L; Gillespie-Lynch, Kristen; Lum, Jarrad A G
2016-01-01
Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI) and Autism Spectrum Disorder (ASD). Using meta-analysis, we examined statistical learning in SLI (14 studies, 15 comparisons) and ASD (13 studies, 20 comparisons) to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, and Probabilistic Classification). Individuals with SLI showed deficits in statistical learning relative to age-matched controls. In contrast, statistical learning was intact in individuals with ASD relative to controls. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping social-communicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman and Pierpont, 2005), impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD.
Statistical Learning in Specific Language Impairment and Autism Spectrum Disorder: A Meta-Analysis
Obeid, Rita; Brooks, Patricia J.; Powers, Kasey L.; Gillespie-Lynch, Kristen; Lum, Jarrad A. G.
2016-01-01
Impairments in statistical learning might be a common deficit among individuals with Specific Language Impairment (SLI) and Autism Spectrum Disorder (ASD). Using meta-analysis, we examined statistical learning in SLI (14 studies, 15 comparisons) and ASD (13 studies, 20 comparisons) to evaluate this hypothesis. Effect sizes were examined as a function of diagnosis across multiple statistical learning tasks (Serial Reaction Time, Contextual Cueing, Artificial Grammar Learning, Speech Stream, Observational Learning, and Probabilistic Classification). Individuals with SLI showed deficits in statistical learning relative to age-matched controls. In contrast, statistical learning was intact in individuals with ASD relative to controls. Effect sizes did not vary as a function of task modality or participant age. Our findings inform debates about overlapping social-communicative difficulties in children with SLI and ASD by suggesting distinct underlying mechanisms. In line with the procedural deficit hypothesis (Ullman and Pierpont, 2005), impaired statistical learning may account for phonological and syntactic difficulties associated with SLI. In contrast, impaired statistical learning fails to account for the social-pragmatic difficulties associated with ASD. PMID:27602006
Statistical Learning in 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.
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.
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
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…
Fostering Students' Statistical Literacy through Significant Learning Experience
ERIC Educational Resources Information Center
Krishnan, Saras
2015-01-01
A major objective of statistics education is to develop students' statistical literacy that enables them to be educated users of data in context. Teaching statistics in today's educational settings is not an easy feat because teachers have a huge task in keeping up with the demands of the new generation of learners. The present day students have…
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.
U-processes and preference learning.
Li, Hong; Ren, Chuanbao; Li, Luoqing
2014-12-01
Preference learning has caused great attention in machining learning. In this letter we propose a learning framework for pairwise loss based on empirical risk minimization of U-processes via Rademacher complexity. We first establish a uniform version of Bernstein inequality of U-processes of degree 2 via the entropy methods. Then we estimate the bound of the excess risk by using the Bernstein inequality and peeling skills. Finally, we apply the excess risk bound to the pairwise preference and derive the convergence rates of pairwise preference learning algorithms with squared loss and indicator loss by using the empirical risk minimization with respect to U-processes. PMID:25248084
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…
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
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
ERIC Educational Resources Information Center
Wessa, Patrick
2009-01-01
This paper discusses the implementation of a new e-learning environment that supports non-rote learning of exploratory and inductive statistics within the pedagogical paradigm of social constructivism. The e-learning system is based on a new computational framework that allows us to create an electronic research environment where students are…
Statistical Word Learning at Scale: The Baby's View Is Better
ERIC Educational Resources Information Center
Yurovsky, Daniel; Smith, Linda B.; Yu, Chen
2013-01-01
A key question in early word learning is how children cope with the uncertainty in natural naming events. One potential mechanism for uncertainty reduction is cross-situational word learning--tracking word/object co-occurrence statistics across naming events. But empirical and computational analyses of cross-situational learning have made strong…
International Collaborative Learning--The Facilitation Process.
ERIC Educational Resources Information Center
Clear, A. G.
International collaborative learning is becoming more viable through a variety of Internet enabled software products. Group Support Systems appear to offer promise. But it is not well understood how to facilitate the teaching and learning process in electronic environments. If education is to involve an interactive process of collaborative inquiry…
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.
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
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…
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…
Signal Processing For Chemical Sensing: Statistics or Biological Inspiration
NASA Astrophysics Data System (ADS)
Marco, Santiago
2011-09-01
Current analytical instrumentation and continuous sensing can provide huge amounts of data. Automatic signal processing and information evaluation is needed to overcome drowning in data. Today, statistical techniques are typically used to analyse and extract information from continuous signals. However, it is very interesting to note that biology (insects and vertebrates) has found alternative solutions for chemical sensing and information processing. This is a brief introduction to the developments in the European Project: Bio-ICT NEUROCHEM: Biologically Inspired Computation for Chemical Sensing (grant no. 216916) Fp7 project devoted to biomimetic olfactory systems.
Statistical Learning of 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…
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.…
Statistical representation of a spray as a point process
Subramaniam, S.
2000-10-01
The statistical representation of a spray as a finite point process is investigated. One objective is to develop a better understanding of how single-point statistical information contained in descriptions such as the droplet distribution function (ddf), relates to the probability density functions (pdfs) associated with the droplets themselves. Single-point statistical information contained in the droplet distribution function (ddf) is shown to be related to a sequence of single surrogate-droplet pdfs, which are in general different from the physical single-droplet pdfs. It is shown that the ddf contains less information than the fundamental single-point statistical representation of the spray, which is also described. The analysis shows which events associated with the ensemble of spray droplets can be characterized by the ddf, and which cannot. The implications of these findings for the ddf approach to spray modeling are discussed. The results of this study also have important consequences for the initialization and evolution of direct numerical simulations (DNS) of multiphase flows, which are usually initialized on the basis of single-point statistics such as the droplet number density in physical space. If multiphase DNS are initialized in this way, this implies that even the initial representation contains certain implicit assumptions concerning the complete ensemble of realizations, which are invalid for general multiphase flows. Also the evolution of a DNS initialized in this manner is shown to be valid only if an as yet unproven commutation hypothesis holds true. Therefore, it is questionable to what extent DNS that are initialized in this manner constitute a direct simulation of the physical droplets. Implications of these findings for large eddy simulations of multiphase flows are also discussed. (c) 2000 American Institute of Physics.
Service-Learning in Introductory Statistics at Kalamazoo College
ERIC Educational Resources Information Center
Nordmoe, Eric D.
2007-01-01
Kalamazoo College is a selective, liberal arts college located in Kalamazoo, Michigan, with total enrollment of approximately 1200 students. The primary introductory statistics course at Kalamazoo College is Applied Statistics I (Math 260). Working in small groups of 3 or 4 members each, students were required to formulate a question of interest,…
Speech Segmentation by Statistical Learning Depends on Attention
ERIC Educational Resources Information Center
Toro, Juan M.; Sinnett, Scott; Soto-Faraco, Salvador
2005-01-01
We addressed the hypothesis that word segmentation based on statistical regularities occurs without the need of attention. Participants were presented with a stream of artificial speech in which the only cue to extract the words was the presence of statistical regularities between syllables. Half of the participants were asked to passively listen…
Distance Learning for Teacher Professional Development in Statistics Education
ERIC Educational Resources Information Center
Meletiou-Mavrotheris, Maria; Mavrotheris, Efstathios; Paparistodemou, Efi
2011-01-01
We provide an overview of "EarlyStatistics," an online professional development course in statistics education targeting European elementary and middle school teachers. The course facilitates intercultural collaboration of teachers using contemporary technological and educational tools. An online information base offers access to all of the course…
Competent statistical programmer: Need of business process outsourcing industry.
Khan, Imran
2014-07-01
Over the last two decades Business Process Outsourcing (BPO) has evolved as much mature practice. India is looked as preferred destination for pharmaceutical outsourcing over a cost arbitrage. Among the biometrics outsourcing, statistical programming and analysis required very niche skill for service delivery. The demand and supply ratios are imbalance due to high churn out rate and less supply of competent programmer. Industry is moving from task delivery to ownership and accountability. The paradigm shift from an outsourcing to consulting is triggering the need for competent statistical programmer. Programmers should be trained in technical, analytical, problem solving, decision making and soft skill as the expectations from the customer are changing from task delivery to accountability of the project. This paper will highlight the common issue SAS programming service industry is facing and skills the programmers need to develop to cope up with these changes. PMID:24987578
Competent statistical programmer: Need of business process outsourcing industry.
Khan, Imran
2014-07-01
Over the last two decades Business Process Outsourcing (BPO) has evolved as much mature practice. India is looked as preferred destination for pharmaceutical outsourcing over a cost arbitrage. Among the biometrics outsourcing, statistical programming and analysis required very niche skill for service delivery. The demand and supply ratios are imbalance due to high churn out rate and less supply of competent programmer. Industry is moving from task delivery to ownership and accountability. The paradigm shift from an outsourcing to consulting is triggering the need for competent statistical programmer. Programmers should be trained in technical, analytical, problem solving, decision making and soft skill as the expectations from the customer are changing from task delivery to accountability of the project. This paper will highlight the common issue SAS programming service industry is facing and skills the programmers need to develop to cope up with these changes.
Competent statistical programmer: Need of business process outsourcing industry
Khan, Imran
2014-01-01
Over the last two decades Business Process Outsourcing (BPO) has evolved as much mature practice. India is looked as preferred destination for pharmaceutical outsourcing over a cost arbitrage. Among the biometrics outsourcing, statistical programming and analysis required very niche skill for service delivery. The demand and supply ratios are imbalance due to high churn out rate and less supply of competent programmer. Industry is moving from task delivery to ownership and accountability. The paradigm shift from an outsourcing to consulting is triggering the need for competent statistical programmer. Programmers should be trained in technical, analytical, problem solving, decision making and soft skill as the expectations from the customer are changing from task delivery to accountability of the project. This paper will highlight the common issue SAS programming service industry is facing and skills the programmers need to develop to cope up with these changes. PMID:24987578
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
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
Statistical image processing in the Virtual Observatory context
NASA Astrophysics Data System (ADS)
Louys, M.; Bonnarel, F.; Schaaff, A.; Pestel, C.
2009-07-01
In an inter-disciplinary collaborative project, we have designed a framework to execute statistical image analysis techniques for multiwavelength astronomical images. This paper describes an interactive tool, AIDA_WF , which helps the astronomer to design and describe image processing workflows. This tool allows designing and executing processing steps arranged in a workflow. Blocks can be either local or remote distributed computations via web services built according to the UWS (Universal Worker Service) currently defined in the VO domain. Processing blocks are modelled with input and output parameters. Validation of input images content and parameters is included and performed using the VO Characterisation Data model. This allows first checking of inputs prior to sending the job on remote computing nodes in a distributed or grid context. The workflows can be saved and documented, and collected as well for further re-use.
On the joint statistics of stable random processes
NASA Astrophysics Data System (ADS)
Hopcraft, K. I.; Jakeman, E.
2011-10-01
A utilitarian continuous bi-variate random process whose first-order probability density function is a stable random variable is constructed. Results paralleling some of those familiar from the theory of Gaussian noise are derived. In addition to the joint-probability density for the process, these include fractional moments and structure functions. Although the correlation functions for stable processes other than Gaussian do not exist, we show that there is coherence between values adopted by the process at different times, which identifies a characteristic evolution with time. The distribution of the derivative of the process, and the joint-density function of the value of the process and its derivative measured at the same time are evaluated. These enable properties to be calculated analytically such as level crossing statistics and those related to the random telegraph wave. When the stable process is fractal, the proportion of time it spends at zero is finite and some properties of this quantity are evaluated, an optical interpretation for which is provided.
Fast Quantum Algorithm for Predicting Descriptive Statistics of Stochastic Processes
NASA Technical Reports Server (NTRS)
Williams Colin P.
1999-01-01
Stochastic processes are used as a modeling tool in several sub-fields of physics, biology, and finance. Analytic understanding of the long term behavior of such processes is only tractable for very simple types of stochastic processes such as Markovian processes. However, in real world applications more complex stochastic processes often arise. In physics, the complicating factor might be nonlinearities; in biology it might be memory effects; and in finance is might be the non-random intentional behavior of participants in a market. In the absence of analytic insight, one is forced to understand these more complex stochastic processes via numerical simulation techniques. In this paper we present a quantum algorithm for performing such simulations. In particular, we show how a quantum algorithm can predict arbitrary descriptive statistics (moments) of N-step stochastic processes in just O(square root of N) time. That is, the quantum complexity is the square root of the classical complexity for performing such simulations. This is a significant speedup in comparison to the current state of the art.
Rudd, James; Moore, Jason H; Urbanowicz, Ryan J
2013-11-01
Permutation-based statistics for evaluating the significance of class prediction, predictive attributes, and patterns of association have only appeared within the learning classifier system (LCS) literature since 2012. While still not widely utilized by the LCS research community, formal evaluations of test statistic confidence are imperative to large and complex real world applications such as genetic epidemiology where it is standard practice to quantify the likelihood that a seemingly meaningful statistic could have been obtained purely by chance. LCS algorithms are relatively computationally expensive on their own. The compounding requirements for generating permutation-based statistics may be a limiting factor for some researchers interested in applying LCS algorithms to real world problems. Technology has made LCS parallelization strategies more accessible and thus more popular in recent years. In the present study we examine the benefits of externally parallelizing a series of independent LCS runs such that permutation testing with cross validation becomes more feasible to complete on a single multi-core workstation. We test our python implementation of this strategy in the context of a simulated complex genetic epidemiological data mining problem. Our evaluations indicate that as long as the number of concurrent processes does not exceed the number of CPU cores, the speedup achieved is approximately linear. PMID:24358057
Statistical process control using optimized neural networks: a case study.
Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid
2014-09-01
The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. PMID:24210290
Designing Instruction That Supports Cognitive Learning Processes
Clark, Ruth; Harrelson, Gary L.
2002-01-01
Objective: To provide an overview of current cognitive learning processes, including a summary of research that supports the use of specific instructional methods to foster those processes. We have developed examples in athletic training education to help illustrate these methods where appropriate. Data Sources: Sources used to compile this information included knowledge base and oral and didactic presentations. Data Synthesis: Research in educational psychology within the past 15 years has provided many principles for designing instruction that mediates the cognitive processes of learning. These include attention, management of cognitive load, rehearsal in working memory, and retrieval of new knowledge from long-term memory. By organizing instruction in the context of tasks performed by athletic trainers, transfer of learning and learner motivation are enhanced. Conclusions/Recommendations: Scientific evidence supports instructional methods that can be incorporated into lesson design and improve learning by managing cognitive load in working memory, stimulating encoding into long-term memory, and supporting transfer of learning. PMID:12937537
Holistic processing from learned attention to parts.
Chua, Kao-Wei; Richler, Jennifer J; Gauthier, Isabel
2015-08-01
Attention helps us focus on what is most relevant to our goals, and prior work has shown 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. We trained subjects to individuate nonface objects (Greebles) from 2 categories: Ploks and Glips. Diagnostic information was in complementary halves for the 2 categories. Holistic processing was then tested with Plok-Glip composites that combined the kind of part that was diagnostic or nondiagnostic during training. Exposure to Greeble parts resulted in general failures of selective attention for nondiagnostic composites, but face-like holistic processing was only observed for diagnostic composites. These results demonstrated a novel link between learned attentional control and the acquisition of holistic processing.
Statistical process control program at a ceramics vendor facility
Enke, G.M.
1992-12-01
Development of a statistical process control (SPC) program at a ceramics vendor location was deemed necessary to improve product quality, reduce manufacturing flowtime, and reduce quality costs borne by AlliedSignal Inc., Kansas City Division (KCD), and the vendor. Because of the lack of available KCD manpower and the required time schedule for the project, it was necessary for the SPC program to be implemented by an external contractor. Approximately a year after the program had been installed, the original baseline was reviewed so that the success of the project could be determined.
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)
What can we learn from noise? - Mesoscopic nonequilibrium statistical physics.
Kobayashi, Kensuke
2016-01-01
Mesoscopic systems - small electric circuits working in quantum regime - offer us a unique experimental stage to explorer quantum transport in a tunable and precise way. The purpose of this Review is to show how they can contribute to statistical physics. We introduce the significance of fluctuation, or equivalently noise, as noise measurement enables us to address the fundamental aspects of a physical system. The significance of the fluctuation theorem (FT) in statistical physics is noted. We explain what information can be deduced from the current noise measurement in mesoscopic systems. As an important application of the noise measurement to statistical physics, we describe our experimental work on the current and current noise in an electron interferometer, which is the first experimental test of FT in quantum regime. Our attempt will shed new light in the research field of mesoscopic quantum statistical physics. PMID:27477456
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…
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.
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
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
2016-01-11
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.
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…
Clinical process learning to improve critical thinking.
Thornhill, S K; Wafer, M S
1997-01-01
A six-step process-learning strategy model serves as a framework for nursing students to critically analyze situations encountered during their clinical practice experience. Stephen Brookfield's four components of critical thinking and culturalization themes relate well to how nurses learn and experience critical thinking and serves as the model's organizing framework. This learning strategy has implications for all nurse educators because it incorporates the realities of nursing practice, merges nursing education with practice, involves students in affective, cognitive, and psychomotor domains of learning, and prepares graduates to function in dynamic and complex health care systems.
Vertical compression algorithms for sequentially processed statistical files
Batory, D.S.
1984-01-01
Horizontal data compression eliminates redundancies or regularities that occur within individual records. Suppression of trailing blanks and leading zeros are examples. Vertical compression eliminates regularities that occur across consecutively stored records. Prefix and suffix compression are examples. Two new vertical compression algorithms, VRE and HVRE, are presented in this paper. They are based on a combination of character matrix transposition (where rows are initially identified with records) and horizontal compression algorithms (run-length encoding and Huffman encoding). Experimental and theoretical results are presented that show the performance of VRE and HVRE is superior to that of some reputable commercial algorithms. Specifically, these are the compression algorithms of the ADABAS, IDMS and SHRINK/2 data management systems. VRE and HVRE are best suited for compressing statistical files which are sequentially processed and batch updated. They may also be used for file archival and for compressing randomly processed files as well.
Utilizing effective statistical process control limits for critical dimension metrology
NASA Astrophysics Data System (ADS)
Buser, Joel T.
2002-12-01
To accurately control critical dimension (CD) metrology in a standard real-time solution across a multi-site operation there is a need to collect measure-to-measure and day-to-day variation across all sites. Each individual site's needs, technologies, and resources can affect the final solution. A preferred statistical process control (SPC) solution for testing measure-to-measure and day-to-day variation is the traditional Mean and Range chart. However, replicating the full measurement process needed for the Mean and Range chart in real-time can strain resources. To solve this problem, an initially proposed measurement methodology was to isolate a point of interest, measure the CD feature n number of times, and continue to the next feature; however, the interdependencies in measure-to-measure variation caused by this methodology resulted in exceedingly narrow control limits. This paper explains how traditional solutions to narrow control limits are statistically problematic and explores the approach of computing control limits for the Mean chart utilizing the moving range of sample means to estimate sigma instead of the traditional range method. Tool monitoring data from multiple CD metrology tools are reported and compared against control limits calculated by the traditional approach, engineering limits, and the suggested approach. The data indicate that the suggested approach is the most accurate of the three solutions.
Infants' statistical learning: 2- and 5-month-olds' segmentation of continuous visual sequences.
Slone, Lauren Krogh; Johnson, Scott P
2015-05-01
Past research suggests that infants have powerful statistical learning abilities; however, studies of infants' visual statistical learning offer differing accounts of the developmental trajectory of and constraints on this learning. To elucidate this issue, the current study tested the hypothesis that young infants' segmentation of visual sequences depends on redundant statistical cues to segmentation. A sample of 20 2-month-olds and 20 5-month-olds observed a continuous sequence of looming shapes in which unit boundaries were defined by both transitional probability and co-occurrence frequency. Following habituation, only 5-month-olds showed evidence of statistically segmenting the sequence, looking longer to a statistically improbable shape pair than to a probable pair. These results reaffirm the power of statistical learning in infants as young as 5 months but also suggest considerable development of statistical segmentation ability between 2 and 5 months of age. Moreover, the results do not support the idea that infants' ability to segment visual sequences based on transitional probabilities and/or co-occurrence frequencies is functional at the onset of visual experience, as has been suggested previously. Rather, this type of statistical segmentation appears to be constrained by the developmental state of the learner. Factors contributing to the development of statistical segmentation ability during early infancy, including memory and attention, are discussed. PMID:25757016
Statistical learning in a natural language by 8-month-old infants.
Pelucchi, Bruna; Hay, Jessica F; Saffran, Jenny R
2009-01-01
Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants' ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition. PMID:19489896
Statistical Learning in a Natural Language by 8-Month-Old Infants
Pelucchi, Bruna; Hay, Jessica F.; Saffran, Jenny R.
2013-01-01
Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants’ ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition. PMID:19489896
Statistical learning of recurring sound patterns encodes auditory objects in songbird forebrain
Lu, Kai; Vicario, David S.
2014-01-01
Auditory neurophysiology has demonstrated how basic acoustic features are mapped in the brain, but it is still not clear how multiple sound components are integrated over time and recognized as an object. We investigated the role of statistical learning in encoding the sequential features of complex sounds by recording neuronal responses bilaterally in the auditory forebrain of awake songbirds that were passively exposed to long sound streams. These streams contained sequential regularities, and were similar to streams used in human infants to demonstrate statistical learning for speech sounds. For stimulus patterns with contiguous transitions and with nonadjacent elements, single and multiunit responses reflected neuronal discrimination of the familiar patterns from novel patterns. In addition, discrimination of nonadjacent patterns was stronger in the right hemisphere than in the left, and may reflect an effect of top-down modulation that is lateralized. Responses to recurring patterns showed stimulus-specific adaptation, a sparsening of neural activity that may contribute to encoding invariants in the sound stream and that appears to increase coding efficiency for the familiar stimuli across the population of neurons recorded. As auditory information about the world must be received serially over time, recognition of complex auditory objects may depend on this type of mnemonic process to create and differentiate representations of recently heard sounds. PMID:25246563
All words are not created equal: Expectations about word length guide infant statistical learning
Lew-Williams, Casey; Saffran, Jenny R.
2011-01-01
Infants have been described as ‘statistical learners’ capable of extracting structure (such as words) from patterned input (such as language). Here, we investigated whether prior knowledge influences how infants track transitional probabilities in word segmentation tasks. Are infants biased by prior experience when engaging in sequential statistical learning? In a laboratory simulation of learning across time, we exposed 9- and 10-month-old infants to a list of either bisyllabic or trisyllabic nonsense words, followed by a pause-free speech stream composed of a different set of bisyllabic or trisyllabic nonsense words. Listening times revealed successful segmentation of words from fluent speech only when words were uniformly bisyllabic or trisyllabic throughout both phases of the experiment. Hearing trisyllabic words during the pre-exposure phase derailed infants’ abilities to segment speech into bisyllabic words, and vice versa. We conclude that prior knowledge about word length equips infants with perceptual expectations that facilitate efficient processing of subsequent language input. PMID:22088408
Statistical Inference-Based Cache Management for Mobile Learning
ERIC Educational Resources Information Center
Li, Qing; Zhao, Jianmin; Zhu, Xinzhong
2009-01-01
Supporting efficient data access in the mobile learning environment is becoming a hot research problem in recent years, and the problem becomes tougher when the clients are using light-weight mobile devices such as cell phones whose limited storage space prevents the clients from holding a large cache. A practical solution is to store the cache…
Extended Statistical Learning as an Account for Slow Vocabulary Growth
ERIC Educational Resources Information Center
Stokes, Stephanie F.; Kern, Sophie; dos Santos, Christophe
2012-01-01
Stokes (2010) compared the lexicons of English-speaking late talkers (LT) with those of their typically developing (TD) peers on neighborhood density (ND) and word frequency (WF) characteristics and suggested that LTs employed learning strategies that differed from those of their TD peers. This research sought to explore the cross-linguistic…
Cognitive Effects from Process Learning with Computer-Based Simulations.
ERIC Educational Resources Information Center
Breuer, Klaus; Kummer, Ruediger
1990-01-01
Discusses content learning versus process learning, describes process learning with computer-based simulations, and highlights an empirical study on the effects of process learning with problem-oriented, computer-managed simulations in technical vocational education classes in West Germany. Process learning within a model of the cognitive system…
Learning process in public goods games
NASA Astrophysics Data System (ADS)
Amado, André; Huang, Weini; Campos, Paulo R. A.; Ferreira, Fernando Fagundes
2015-07-01
We propose an individual-based model to describe the effects of memory and learning in the evolution of cooperation in a public goods game (PGG) in a well-mixed population. Individuals are endowed with a set of strategies, and in every round of the game they use one strategy out of this set based on their memory and learning process. The payoff of a player using a given strategy depends on the public goods enhancement factor r and the collective action of all players. We investigate the distribution of used strategies as well as the distribution of information patterns. The outcome depends on the learning process, which can be dynamic or static. In the dynamic learning process, the players can switch their strategies along the whole game, and use the strategy providing the highest payoff at current time step. In the static learning process, there is a training period where the players randomly explore different strategies out of their strategy sets. In the rest of the game, players only use the strategy providing the highest payoff during the training period. In the dynamic learning process, we observe a transition from a non-cooperative regime to a regime where the level of cooperation reaches about 50 %. As in the standard PGG, in the static learning process there is a transition from the non-cooperative regime to a regime where the level of cooperation can be higher than 50% at r = N. In both learning processes the transition becomes smoother as the memory size of individuals increases, which means that the lack of information is a key ingredient causing the defection.
Statistical distributions of earthquake numbers: consequence of branching process
NASA Astrophysics Data System (ADS)
Kagan, Yan Y.
2010-03-01
We discuss various statistical distributions of earthquake numbers. Previously, we derived several discrete distributions to describe earthquake numbers for the branching model of earthquake occurrence: these distributions are the Poisson, geometric, logarithmic and the negative binomial (NBD). The theoretical model is the `birth and immigration' population process. The first three distributions above can be considered special cases of the NBD. In particular, a point branching process along the magnitude (or log seismic moment) axis with independent events (immigrants) explains the magnitude/moment-frequency relation and the NBD of earthquake counts in large time/space windows, as well as the dependence of the NBD parameters on the magnitude threshold (magnitude of an earthquake catalogue completeness). We discuss applying these distributions, especially the NBD, to approximate event numbers in earthquake catalogues. There are many different representations of the NBD. Most can be traced either to the Pascal distribution or to the mixture of the Poisson distribution with the gamma law. We discuss advantages and drawbacks of both representations for statistical analysis of earthquake catalogues. We also consider applying the NBD to earthquake forecasts and describe the limits of the application for the given equations. In contrast to the one-parameter Poisson distribution so widely used to describe earthquake occurrence, the NBD has two parameters. The second parameter can be used to characterize clustering or overdispersion of a process. We determine the parameter values and their uncertainties for several local and global catalogues, and their subdivisions in various time intervals, magnitude thresholds, spatial windows, and tectonic categories. The theoretical model of how the clustering parameter depends on the corner (maximum) magnitude can be used to predict future earthquake number distribution in regions where very large earthquakes have not yet occurred.
Temporal and Statistical Information in Causal Structure Learning
ERIC Educational Resources Information Center
McCormack, Teresa; Frosch, Caren; Patrick, Fiona; Lagnado, David
2015-01-01
Three experiments examined children's and adults' abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a 3-variable mechanical…
An Experimental Approach to Teaching and Learning Elementary Statistical Mechanics
ERIC Educational Resources Information Center
Ellis, Frank B.; Ellis, David C.
2008-01-01
Introductory statistical mechanics is studied for a simple two-state system using an inexpensive and easily built apparatus. A large variety of demonstrations, suitable for students in high school and introductory university chemistry courses, are possible. This article details demonstrations for exothermic and endothermic reactions, the dynamic…
Teacher to Teacher: Learn about Statistics--Math League Baseball.
ERIC Educational Resources Information Center
Rosenberg, Mitchell
1994-01-01
Presents an introductory activity in statistics in which a mathematics class is divided into groups to "draft" teams of major league baseball players. Describes the drafting procedure, charting the team, scoring, roster changes, and gathering and evaluating data. Includes draft list, daily box scores, and cumulative box scores. (MKR)
Students Learn Statistics When They Assume a Statistician's Role.
ERIC Educational Resources Information Center
Sullivan, Mary M.
Traditional elementary statistics instruction for non-majors has focused on computation. Rarely have students had an opportunity to interact with real data sets or to use questioning to drive data analysis, common activities among professional statisticians. Inclusion of data gathering and analysis into whole class and small group activities…
Peer-Assisted Learning in Research Methods and Statistics
ERIC Educational Resources Information Center
Stone, Anna; Meade, Claire; Watling, Rosamond
2012-01-01
Feedback from students on a Level 1 Research Methods and Statistics module, studied as a core part of a BSc Psychology programme, highlighted demand for additional tutorials to help them to understand basic concepts. Students in their final year of study commonly request work experience to enhance their employability. All students on the Level 1…
Global Statistical Learning in a Visual Search Task
ERIC Educational Resources Information Center
Jones, John L.; Kaschak, Michael P.
2012-01-01
Locating a target in a visual search task is facilitated when the target location is repeated on successive trials. Global statistical properties also influence visual search, but have often been confounded with local regularities (i.e., target location repetition). In two experiments, target locations were not repeated for four successive trials,…
Learning to Read the Numbers: A Critical Orientation toward Statistics
ERIC Educational Resources Information Center
Whitin, Phyllis; Whitin, David J.
2008-01-01
Being a critical reader of data is an integral part of being fully literate in today's information age. In this article the authors underscore the interdisciplinary importance of this stance by drawing upon theoretical perspectives from both the fields of language and mathematics. They argue that all texts, including statistical ones, must be…
Statistical Analysis Tools for Learning in Engineering Laboratories.
ERIC Educational Resources Information Center
Maher, Carolyn A.
1990-01-01
Described are engineering programs that have used automated data acquisition systems to implement data collection and analyze experiments. Applications include a biochemical engineering laboratory, heat transfer performance, engineering materials testing, mechanical system reliability, statistical control laboratory, thermo-fluid laboratory, and a…
United States Middle School Students' Perspectives on Learning Statistics
ERIC Educational Resources Information Center
Dwyer, Jerry; Moorhouse, Kim; Colwell, Malinda J.
2009-01-01
This paper describes an intervention at the 8th grade level where university mathematics researchers presented a series of lessons on introductory concepts in probability and statistics. Pre- and post-tests, and interviews were conducted to examine whether or not students at this grade level can understand these concepts. Students showed a…
Reliable probabilities through statistical post-processing of ensemble predictions
NASA Astrophysics Data System (ADS)
Van Schaeybroeck, Bert; Vannitsem, Stéphane
2013-04-01
We develop post-processing or calibration approaches based on linear regression that make ensemble forecasts more reliable. We enforce climatological reliability in the sense that the total variability of the prediction is equal to the variability of the observations. Second, we impose ensemble reliability such that the spread around the ensemble mean of the observation coincides with the one of the ensemble members. In general the attractors of the model and reality are inhomogeneous. Therefore ensemble spread displays a variability not taken into account in standard post-processing methods. We overcome this by weighting the ensemble by a variable error. The approaches are tested in the context of the Lorenz 96 model (Lorenz 1996). The forecasts become more reliable at short lead times as reflected by a flatter rank histogram. Our best method turns out to be superior to well-established methods like EVMOS (Van Schaeybroeck and Vannitsem, 2011) and Nonhomogeneous Gaussian Regression (Gneiting et al., 2005). References [1] Gneiting, T., Raftery, A. E., Westveld, A., Goldman, T., 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Weather Rev. 133, 1098-1118. [2] Lorenz, E. N., 1996: Predictability - a problem partly solved. Proceedings, Seminar on Predictability ECMWF. 1, 1-18. [3] Van Schaeybroeck, B., and S. Vannitsem, 2011: Post-processing through linear regression, Nonlin. Processes Geophys., 18, 147.
Predicting Student Success in a Psychological Statistics Course Emphasizing Collaborative Learning
ERIC Educational Resources Information Center
Gorvine, Benjamin J.; Smith, H. David
2015-01-01
This study describes the use of a collaborative learning approach in a psychological statistics course and examines the factors that predict which students benefit most from such an approach in terms of learning outcomes. In a course format with a substantial group work component, 166 students were surveyed on their preference for individual…
The Role of Statistical Learning in the Acquisition of Motion Event Construal in a Second Language
ERIC Educational Resources Information Center
Treffers-Daller, Jeanine; Calude, Andreea
2015-01-01
Learning to talk about motion in a second language is very difficult because it involves restructuring deeply entrenched patterns from the first language. In this paper we argue that statistical learning can explain why L2 learners are only partially successful in restructuring their second language grammars. We explore to what extent L2 learners…
Milic, Natasa M.; Trajkovic, Goran Z.; Bukumiric, Zoran M.; Cirkovic, Andja; Nikolic, Ivan M.; Milin, Jelena S.; Milic, Nikola V.; Savic, Marko D.; Corac, Aleksandar M.; Marinkovic, Jelena M.; Stanisavljevic, Dejana M.
2016-01-01
Background Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. Methods This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013–14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Results Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001). Conclusion This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient
Links to sources of cancer-related statistics, including the Surveillance, Epidemiology and End Results (SEER) Program, SEER-Medicare datasets, cancer survivor prevalence data, and the Cancer Trends Progress Report.
An appeal to undergraduate wildlife programs: send scientists to learn statistics
Kendall, W.L.; Gould, W.R.
2002-01-01
Undergraduate wildlife students taking introductory statistics too often are poorly prepared and insufficiently motivated to learn statistics. We have also encountered too many wildlife professionals, even with graduate degrees, who exhibit an aversion to thinking statistically, either relying too heavily on statisticians or avoiding statistics altogether. We believe part of the reason for these problems is that wildlife majors are insufficiently grounded in the scientific method and analytical thinking before they take statistics. We suggest that a partial solution is to assure wildlife majors are trained in the scientific method at the very beginning of their academic careers.
Fostering Self-Concept and Interest for Statistics through Specific Learning Environments
ERIC Educational Resources Information Center
Sproesser, Ute; Engel, Joachim; Kuntze, Sebastian
2016-01-01
Supporting motivational variables such as self-concept or interest is an important goal of schooling as they relate to learning and achievement. In this study, we investigated whether specific interest and self-concept related to the domains of statistics and mathematics can be fostered through a four-lesson intervention focusing on statistics.…
ERIC Educational Resources Information Center
Romeu, Jorge Luis
2008-01-01
This article discusses our teaching approach in graduate level Engineering Statistics. It is based on the use of modern technology, learning groups, contextual projects, simulation models, and statistical and simulation software to entice student motivation. The use of technology to facilitate group projects and presentations, and to generate,…
The Effect of Project Based Learning on the Statistical Literacy Levels of Student 8th Grade
ERIC Educational Resources Information Center
Koparan, Timur; Güven, Bülent
2014-01-01
This study examines the effect of project based learning on 8th grade students' statistical literacy levels. A performance test was developed for this aim. Quasi-experimental research model was used in this article. In this context, the statistics were taught with traditional method in the control group and it was taught using project based…
Students' Perspectives of Using Cooperative Learning in a Flipped Statistics Classroom
ERIC Educational Resources Information Center
Chen, Liwen; Chen, Tung-Liang; Chen, Nian-Shing
2015-01-01
Statistics has been recognised as one of the most anxiety-provoking subjects to learn in the higher education context. Educators have continuously endeavoured to find ways to integrate digital technologies and innovative pedagogies in the classroom to eliminate the fear of statistics. The purpose of this study is to systematically identify…
The Effect on the 8th Grade Students' Attitude towards Statistics of Project Based Learning
ERIC Educational Resources Information Center
Koparan, Timur; Güven, Bülent
2014-01-01
This study investigates the effect of the project based learning approach on 8th grade students' attitude towards statistics. With this aim, an attitude scale towards statistics was developed. Quasi-experimental research model was used in this study. Following this model in the control group the traditional method was applied to teach statistics…
Sex Differences in the Relation between Statistics Anxiety and Cognitive/Learning Strategies
ERIC Educational Resources Information Center
Rodarte-Luna, Bertha; Sherry, Alissa
2008-01-01
Three hundred twenty three students were recruited in order to investigate sex differences on measures of statistics anxiety and learning strategies. Data was analyzed using descriptive discriminant analysis and canonical correlation analysis. Findings indicated that sex differences on these measures were statistically significant, but with small…
ERIC Educational Resources Information Center
Averitt, Sallie D.
This instructor guide, which was developed for use in a manufacturing firm's advanced technical preparation program, contains the materials required to present a learning module that is designed to prepare trainees for the program's statistical process control module by improving their basic math skills in working with line graphs and teaching…
Cross-Domain Statistical-Sequential Dependencies Are Difficult to Learn.
Walk, Anne M; Conway, Christopher M
2016-01-01
Recent studies have demonstrated participants' ability to learn cross-modal associations during statistical learning tasks. However, these studies are all similar in that the cross-modal associations to be learned occur simultaneously, rather than sequentially. In addition, the majority of these studies focused on learning across sensory modalities but not across perceptual categories. To test both cross-modal and cross-categorical learning of sequential dependencies, we used an artificial grammar learning task consisting of a serial stream of auditory and/or visual stimuli containing both within- and cross-domain dependencies. Experiment 1 examined within-modal and cross-modal learning across two sensory modalities (audition and vision). Experiment 2 investigated within-categorical and cross-categorical learning across two perceptual categories within the same sensory modality (e.g., shape and color; tones and non-words). Our results indicated that individuals demonstrated learning of the within-modal and within-categorical but not the cross-modal or cross-categorical dependencies. These results stand in contrast to the previous demonstrations of cross-modal statistical learning, and highlight the presence of modality constraints that limit the effectiveness of learning in a multimodal environment. PMID:26941696
Applicability of statistical learning algorithms in groundwater quality modeling
NASA Astrophysics Data System (ADS)
Khalil, Abedalrazq; Almasri, Mohammad N.; McKee, Mac; Kaluarachchi, Jagath J.
2005-05-01
Four algorithms are outlined, each of which has interesting features for predicting contaminant levels in groundwater. Artificial neural networks (ANN), support vector machines (SVM), locally weighted projection regression (LWPR), and relevance vector machines (RVM) are utilized as surrogates for a relatively complex and time-consuming mathematical model to simulate nitrate concentration in groundwater at specified receptors. Nitrates in the application reported in this paper are due to on-ground nitrogen loadings from fertilizers and manures. The practicability of the four learning machines in this work is demonstrated for an agriculture-dominated watershed where nitrate contamination of groundwater resources exceeds the maximum allowable contaminant level at many locations. Cross-validation and bootstrapping techniques are used for both training and performance evaluation. Prediction results of the four learning machines are rigorously assessed using different efficiency measures to ensure their generalization ability. Prediction results show the ability of learning machines to build accurate models with strong predictive capabilities and hence constitute a valuable means for saving effort in groundwater contamination modeling and improving model performance.
ERIC Educational Resources Information Center
Liu, Ming-Tsung; Yu, Pao-Ta
2011-01-01
A personalized e-learning service provides learning content to fit learners' individual differences. Learning achievements are influenced by cognitive as well as non-cognitive factors such as mood, motivation, interest, and personal styles. This paper proposes the Learning Caution Indexes (LCI) to detect aberrant learning patterns. The philosophy…
Statistical process control based chart for information systems security
NASA Astrophysics Data System (ADS)
Khan, Mansoor S.; Cui, Lirong
2015-07-01
Intrusion detection systems have a highly significant role in securing computer networks and information systems. To assure the reliability and quality of computer networks and information systems, it is highly desirable to develop techniques that detect intrusions into information systems. We put forward the concept of statistical process control (SPC) in computer networks and information systems intrusions. In this article we propose exponentially weighted moving average (EWMA) type quality monitoring scheme. Our proposed scheme has only one parameter which differentiates it from the past versions. We construct the control limits for the proposed scheme and investigate their effectiveness. We provide an industrial example for the sake of clarity for practitioner. We give comparison of the proposed scheme with EWMA schemes and p chart; finally we provide some recommendations for the future work.
Application of statistical process control to qualitative molecular diagnostic assays.
O'Brien, Cathal P; Finn, Stephen P
2014-01-01
Modern pathology laboratories and in particular high throughput laboratories such as clinical chemistry have developed a reliable system for statistical process control (SPC). Such a system is absent from the majority of molecular laboratories and where present is confined to quantitative assays. As the inability to apply SPC to an assay is an obvious disadvantage this study aimed to solve this problem by using a frequency estimate coupled with a confidence interval calculation to detect deviations from an expected mutation frequency. The results of this study demonstrate the strengths and weaknesses of this approach and highlight minimum sample number requirements. Notably, assays with low mutation frequencies and detection of small deviations from an expected value require greater sample numbers to mitigate a protracted time to detection. Modeled laboratory data was also used to highlight how this approach might be applied in a routine molecular laboratory. This article is the first to describe the application of SPC to qualitative laboratory data. PMID:25988159
A Statistical Process Control Method for Semiconductor Manufacturing
NASA Astrophysics Data System (ADS)
Kubo, Tomoaki; Ino, Tomomi; Minami, Kazuhiro; Minami, Masateru; Homma, Tetsuya
To maintain stable operation of semiconductor fabrication lines, statistical process control (SPC) methods are recognized to be effective. However, in semiconductor fabrication lines, there exist a huge number of process state signals to be monitored, and these signals contain both normally and non-normally distributed data. Therefore, if we try to apply SPC methods to those signals, we need one which satisfies three requirements: 1) It can deal with both normally distributed data, and non-normally distributed data, 2) It can be set up automatically, 3) It can be easily understood by engineers and technicians. In this paper, we propose a new SPC method which satisfies these three requirements at the same time. This method uses similar rules to the Shewhart chart, but can deal with non-normally distributed data by introducing “effective standard deviations”. Usefulness of this method is demonstrated by comparing false alarm ratios to that of the Shewhart chart method. In the demonstration, we use various kinds of artificially generated data, and real data observed in a chemical vapor deposition (CVD) process tool in a semiconductor fabrication line.
Statistical process control testing of electronic security equipment
Murray, D.W.; Spencer, D.D.
1994-06-01
Statistical Process Control testing of manufacturing processes began back in the 1940`s with the development of Process Control Charts by Dr. Walter A. Shewart. Sandia National Laboratories has developed an application of the SPC method for performance testing of electronic security equipment. This paper documents the evaluation of this testing methodology applied to electronic security equipment and an associated laptop computer-based system for obtaining and analyzing the test data. Sandia developed this SPC sensor performance testing method primarily for use on portal metal detectors, but, has evaluated it for testing of an exterior intrusion detection sensor and other electronic security devices. This method is an alternative to the traditional binomial (alarm or no-alarm) performance testing. The limited amount of information in binomial data drives the number of tests necessary to meet regulatory requirements to unnecessarily high levels. For example, a requirement of a 0.85 probability of detection with a 90% confidence requires a minimum of 19 alarms out of 19 trials. By extracting and analyzing measurement (variables) data whenever possible instead of the more typical binomial data, the user becomes more informed about equipment health with fewer tests (as low as five per periodic evaluation).
The Abnormal vs. Normal ECG Classification Based on Key Features and Statistical Learning
NASA Astrophysics Data System (ADS)
Dong, Jun; Tong, Jia-Fei; Liu, Xia
As cardiovascular diseases appear frequently in modern society, the medicine and health system should be adjusted to meet the new requirements. Chinese government has planned to establish basic community medical insurance system (BCMIS) before 2020, where remote medical service is one of core issues. Therefore, we have developed the "remote network hospital system" which includes data server and diagnosis terminal by the aid of wireless detector to sample ECG. To improve the efficiency of ECG processing, in this paper, abnormal vs. normal ECG classification approach based on key features and statistical learning is presented, and the results are analyzed. Large amount of normal ECG could be filtered by computer automatically and abnormal ECG is left to be diagnosed specially by physicians.
GeoSegmenter: A statistically learned Chinese word segmenter for the geoscience domain
NASA Astrophysics Data System (ADS)
Huang, Lan; Du, Youfu; Chen, Gongyang
2015-03-01
Unlike English, the Chinese language has no space between words. Segmenting texts into words, known as the Chinese word segmentation (CWS) problem, thus becomes a fundamental issue for processing Chinese documents and the first step in many text mining applications, including information retrieval, machine translation and knowledge acquisition. However, for the geoscience subject domain, the CWS problem remains unsolved. Although a generic segmenter can be applied to process geoscience documents, they lack the domain specific knowledge and consequently their segmentation accuracy drops dramatically. This motivated us to develop a segmenter specifically for the geoscience subject domain: the GeoSegmenter. We first proposed a generic two-step framework for domain specific CWS. Following this framework, we built GeoSegmenter using conditional random fields, a principled statistical framework for sequence learning. Specifically, GeoSegmenter first identifies general terms by using a generic baseline segmenter. Then it recognises geoscience terms by learning and applying a model that can transform the initial segmentation into the goal segmentation. Empirical experimental results on geoscience documents and benchmark datasets showed that GeoSegmenter could effectively recognise both geoscience terms and general terms.
Statistical method for detecting structural change in the growth process.
Ninomiya, Yoshiyuki; Yoshimoto, Atsushi
2008-03-01
Due to competition among individual trees and other exogenous factors that change the growth environment, each tree grows following its own growth trend with some structural changes in growth over time. In the present article, a new method is proposed to detect a structural change in the growth process. We formulate the method as a simple statistical test for signal detection without constructing any specific model for the structural change. To evaluate the p-value of the test, the tube method is developed because the regular distribution theory is insufficient. Using two sets of tree diameter growth data sampled from planted forest stands of Cryptomeria japonica in Japan, we conduct an analysis of identifying the effect of thinning on the growth process as a structural change. Our results demonstrate that the proposed method is useful to identify the structural change caused by thinning. We also provide the properties of the method in terms of the size and power of the test. PMID:17608782
Error-driven learning in statistical summary perception.
Fan, Judith E; Turk-Browne, Nicholas B; Taylor, Jordan A
2016-02-01
We often interact with multiple objects at once, such as when balancing food and beverages on a dining tray. The success of these interactions relies upon representing not only individual objects, but also statistical summary features of the group (e.g., center-of-mass). Although previous research has established that humans can readily and accurately extract such statistical summary features, how this ability is acquired and refined through experience currently remains unaddressed. Here we ask if training and task feedback can improve summary perception. During training, participants practiced estimating the centroid (i.e., average location) of an array of objects on a touchscreen display. Before and after training, they completed a transfer test requiring perceptual discrimination of the centroid. Across 4 experiments, we manipulated the information in task feedback and how participants interacted with the objects during training. We found that vector error feedback, which conveys error both in terms of distance and direction, was the only form of feedback that improved perceptual discrimination of the centroid on the transfer test. Moreover, this form of feedback was effective only when coupled with reaching movements toward the visual objects. Taken together, these findings suggest that sensory-prediction error-signaling the mismatch between expected and actual consequences of an action-may play a previously unrecognized role in tuning perceptual representations. (PsycINFO Database Record
Error-driven learning in statistical summary perception.
Fan, Judith E; Turk-Browne, Nicholas B; Taylor, Jordan A
2016-02-01
We often interact with multiple objects at once, such as when balancing food and beverages on a dining tray. The success of these interactions relies upon representing not only individual objects, but also statistical summary features of the group (e.g., center-of-mass). Although previous research has established that humans can readily and accurately extract such statistical summary features, how this ability is acquired and refined through experience currently remains unaddressed. Here we ask if training and task feedback can improve summary perception. During training, participants practiced estimating the centroid (i.e., average location) of an array of objects on a touchscreen display. Before and after training, they completed a transfer test requiring perceptual discrimination of the centroid. Across 4 experiments, we manipulated the information in task feedback and how participants interacted with the objects during training. We found that vector error feedback, which conveys error both in terms of distance and direction, was the only form of feedback that improved perceptual discrimination of the centroid on the transfer test. Moreover, this form of feedback was effective only when coupled with reaching movements toward the visual objects. Taken together, these findings suggest that sensory-prediction error-signaling the mismatch between expected and actual consequences of an action-may play a previously unrecognized role in tuning perceptual representations. (PsycINFO Database Record PMID:26389617
Understanding the Advising Learning Process Using Learning Taxonomies
ERIC Educational Resources Information Center
Muehleck, Jeanette K.; Smith, Cathleen L.; Allen, Janine M.
2014-01-01
To better understand the learning that transpires in advising, we used Anderson et al.'s (2001) revision of Bloom's (1956) taxonomy and Krathwohl, Bloom, and Masia's (1964) affective taxonomy to analyze eight student-reported advising outcomes from Smith and Allen (2014). Using the cognitive processes and knowledge domains of Anderson et al.'s…
Body Learning: Examining the Processes of Skill Learning in Dance
ERIC Educational Resources Information Center
Bailey, Richard; Pickard, Angela
2010-01-01
This paper was stimulated by the authors' attempt to understand the process of skill learning in dance. Its stimulus was a period of fieldwork based at the Royal Ballet School in London, and subsequent discussions with the school's teachers and with academic colleagues about how it was that the young dancers developed their characteristic set of…
Unifying K-12 Learning Processes: Integrating Curricula through Learning
ERIC Educational Resources Information Center
Bosse, Michael J.; Fogarty, Elizabeth A.
2011-01-01
This study was designed to examine whether a set of cross-curricular learning processes could be found in the respective K-12 US national standards for math, language arts, foreign language, science, social studies, fine arts, and technology. Using a qualitative research methodology, the standards from the national associations for these content…
Using cognitive learning theory to design effective on-line statistics tutorials.
Romero, V L; Berger, D E; Healy, M R; Aberson, C L
2000-05-01
Careful attention to principles of learning can improve the design of Web-based lessons and tutorials. Tutorials from the Web Interface for Statistics Education (WISE; http:¿wise.cgu.edu) demonstrate how specific principles can be integrated into Web design to enhance learning in two areas. First, the impact of students' poor self-regulation abilities on Web-based learning is considered. Second, evidence that specific types of visual presentations improve learning is discussed. Finally, the need for empirical evaluation is emphasized. Specific research and examples from the WISE project are used to illustrate each of these points. PMID:10875169
Role of Symbolic Coding and Rehearsal Processes in Observational Learning
ERIC Educational Resources Information Center
Bandura, Albert; Jeffery, Robert W.
1973-01-01
Results were interpreted supporting a social learning view of observational learning that emphasizes contral processing of response information in the acquisition phase and motor reproduction and incentive processes in the overt enactment of what has been learned. (Author)
Statistical-Mechanical Analysis of Pre-training and Fine Tuning in Deep Learning
NASA Astrophysics Data System (ADS)
Ohzeki, Masayuki
2015-03-01
In this paper, we present a statistical-mechanical analysis of deep learning. We elucidate some of the essential components of deep learning — pre-training by unsupervised learning and fine tuning by supervised learning. We formulate the extraction of features from the training data as a margin criterion in a high-dimensional feature-vector space. The self-organized classifier is then supplied with small amounts of labelled data, as in deep learning. Although we employ a simple single-layer perceptron model, rather than directly analyzing a multi-layer neural network, we find a nontrivial phase transition that is dependent on the number of unlabelled data in the generalization error of the resultant classifier. In this sense, we evaluate the efficacy of the unsupervised learning component of deep learning. The analysis is performed by the replica method, which is a sophisticated tool in statistical mechanics. We validate our result in the manner of deep learning, using a simple iterative algorithm to learn the weight vector on the basis of belief propagation.
Wang, Tianlin; Saffran, Jenny R.
2014-01-01
While research shows that adults attend to both segmental and suprasegmental regularities in speech, including syllabic transitional probabilities as well as stress and intonational patterns, little is known about how statistical learning operates given input from tonal languages. In the current study, we designed an artificial tone language to address several questions: can adults track regularities in a tonal language? Is learning enhanced by previous exposure to tone-marking languages? Does bilingualism affect learning in this task? To address these questions, we contrasted the performance of English monolingual adults (Experiment 1), Mandarin monolingual and Mandarin–English bilingual adults (Experiment 2), and non-tonal bilingual adults (Experiment 3) in a statistical learning task using an artificial tone language. The pattern of results suggests that while prior exposure to tonal languages did not lead to significant improvements in performance, bilingual experience did enhance learning outcomes. This study represents the first demonstration of statistical learning of an artificial tone language and suggests a complex interplay between prior language experience and subsequent language learning. PMID:25232344
Content-Based VLE Designs Improve Learning Efficiency in Constructivist Statistics Education
Wessa, Patrick; De Rycker, Antoon; Holliday, Ian Edward
2011-01-01
Background We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific–purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. Objectives The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Methods Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. Results The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under
Awake, Offline Processing during Associative Learning
Nestor, Adrian; Tarr, Michael J.; Creswell, J. David
2016-01-01
Offline processing has been shown to strengthen memory traces and enhance learning in the absence of conscious rehearsal or awareness. Here we evaluate whether a brief, two-minute offline processing period can boost associative learning and test a memory reactivation account for these offline processing effects. After encoding paired associates, subjects either completed a distractor task for two minutes or were immediately tested for memory of the pairs in a counterbalanced, within-subjects functional magnetic resonance imaging study. Results showed that brief, awake, offline processing improves memory for associate pairs. Moreover, multi-voxel pattern analysis of the neuroimaging data suggested reactivation of encoded memory representations in dorsolateral prefrontal cortex during offline processing. These results signify the first demonstration of awake, active, offline enhancement of associative memory and suggest that such enhancement is accompanied by the offline reactivation of encoded memory representations. PMID:27119345
ERIC Educational Resources Information Center
Liu, T.-C.; Lin, Y.-C.; Kinshuk
2010-01-01
Simulation-based computer assisted learning (CAL) is recommended to help students understand important statistical concepts, although the current systems are still far from ideal. Simulation-Assisted Learning Statistics (SALS) is a simulation-based CAL that is developed with a learning model that is based on cognitive conflict theory to correct…
ERIC Educational Resources Information Center
Yousef, Darwish Abdulrahman
2016-01-01
Purpose: Although there are many studies addressing the learning styles of business students as well as students of other disciplines, there are few studies which address the learning style preferences of statistics students. The purpose of this study is to explore the learning style preferences of statistics students at a United Arab Emirates…
The penumbra of learning: a statistical theory of synaptic tagging and capture.
Gershman, Samuel J
2014-01-01
Learning in humans and animals is accompanied by a penumbra: Learning one task benefits from learning an unrelated task shortly before or after. At the cellular level, the penumbra of learning appears when weak potentiation of one synapse is amplified by strong potentiation of another synapse on the same neuron during a critical time window. Weak potentiation sets a molecular tag that enables the synapse to capture plasticity-related proteins synthesized in response to strong potentiation at another synapse. This paper describes a computational model which formalizes synaptic tagging and capture in terms of statistical learning mechanisms. According to this model, synaptic strength encodes a probabilistic inference about the dynamically changing association between pre- and post-synaptic firing rates. The rate of change is itself inferred, coupling together different synapses on the same neuron. When the inputs to one synapse change rapidly, the inferred rate of change increases, amplifying learning at other synapses.
ERIC Educational Resources Information Center
Neumann, David L.; Neumann, Michelle M.; Hood, Michelle
2011-01-01
The discipline of statistics seems well suited to the integration of technology in a lecture as a means to enhance student learning and engagement. Technology can be used to simulate statistical concepts, create interactive learning exercises, and illustrate real world applications of statistics. The present study aimed to better understand the…
Daltrozzo, Jerome; Conway, Christopher M.
2014-01-01
Statistical-sequential learning (SL) is the ability to process patterns of environmental stimuli, such as spoken language, music, or one’s motor actions, that unfold in time. The underlying neurocognitive mechanisms of SL and the associated cognitive representations are still not well understood as reflected by the heterogeneity of the reviewed cognitive models. The purpose of this review is: (1) to provide a general overview of the primary models and theories of SL, (2) to describe the empirical research – with a focus on the event-related potential (ERP) literature – in support of these models while also highlighting the current limitations of this research, and (3) to present a set of new lines of ERP research to overcome these limitations. The review is articulated around three descriptive dimensions in relation to SL: the level of abstractness of the representations learned through SL, the effect of the level of attention and consciousness on SL, and the developmental trajectory of SL across the life-span. We conclude with a new tentative model that takes into account these three dimensions and also point to several promising new lines of SL research. PMID:24994975
ERIC Educational Resources Information Center
Liou, Hsien-Chin; Chang, Jason S; Chen, Hao-Jan; Lin, Chih-Cheng; Liaw, Meei-Ling; Gao, Zhao-Ming; Jang, Jyh-Shing Roger; Yeh, Yuli; Chuang, Thomas C.; You, Geeng-Neng
2006-01-01
This paper describes the development of an innovative web-based environment for English language learning with advanced data-driven and statistical approaches. The project uses various corpora, including a Chinese-English parallel corpus ("Sinorama") and various natural language processing (NLP) tools to construct effective English learning tasks…
Statistical Analysis of CMC Constituent and Processing Data
NASA Technical Reports Server (NTRS)
Fornuff, Jonathan
2004-01-01
observed using statistical analysis software. The ultimate purpose of this study is to determine what variations in material processing can lead to the most critical changes in the materials property. The work I have taken part in this summer explores, in general, the key properties needed In this study SiC/SiC composites of varying architectures, utilizing a boron-nitride (BN)
Rapid Serial Auditory Presentation: A New Measure of Statistical Learning in Speech Segmentation.
Franco, Ana; Eberlen, Julia; Destrebecqz, Arnaud; Cleeremans, Axel; Bertels, Julie
2015-01-01
The Rapid Serial Visual Presentation procedure is a method widely used in visual perception research. In this paper we propose an adaptation of this method which can be used with auditory material and enables assessment of statistical learning in speech segmentation. Adult participants were exposed to an artificial speech stream composed of statistically defined trisyllabic nonsense words. They were subsequently instructed to perform a detection task in a Rapid Serial Auditory Presentation (RSAP) stream in which they had to detect a syllable in a short speech stream. Results showed that reaction times varied as a function of the statistical predictability of the syllable: second and third syllables of each word were responded to faster than first syllables. This result suggests that the RSAP procedure provides a reliable and sensitive indirect measure of auditory statistical learning. PMID:26592534
Teaching and Learning: A Collaborative Process.
ERIC Educational Resources Information Center
Goldberg, Merryl R.
1990-01-01
Explains the teaching-research method of instruction that employs the teacher and students as collaborative partners in the learning process. States that students attain knowledge through assimilating experiences in ways that are most meaningful for them. Case studies are included. (GG)
Digital Signal Processing and Machine Learning
NASA Astrophysics Data System (ADS)
Li, Yuanqing; Ang, Kai Keng; Guan, Cuntai
Any brain-computer interface (BCI) system must translate signals from the users brain into messages or commands (see Fig. 1). Many signal processing and machine learning techniques have been developed for this signal translation, and this chapter reviews the most common ones. Although these techniques are often illustrated using electroencephalography (EEG) signals in this chapter, they are also suitable for other brain signals.
Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning
Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego
2016-01-01
Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273
Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego
2016-06-17
Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.
Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego
2016-01-01
Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273
Statistical mechanics of fragmentation processes of ice and rock bodies
NASA Astrophysics Data System (ADS)
Bashkirov, A. G.; Vityazev, A. V.
1996-09-01
It is a well-known experimental fact that impact fragmentation, specifically of ice and rock bodies, causes a two-step ("knee"-shaped) power distribution of fragment masses with exponent values within the limits -4 and -1.5 (here and henceforth the differential distribution is borne in mind). A new theoretical approach is proposed to determine the exponent values, a minimal fracture mass, and properties of the knee. As a basis for construction of non-equilibrium statistical mechanics of condensed matter fragmentation the maximum-entropy variational principle is used. In contrast to the usual approach founded on the Boltzmann entropy the more general Tsallis entropy allowing stationary solutions not only in the exponential Boltzmann-Gibbs form but in the form of the power (fractal) law distribution as well is invoked. Relying on the analysis of a lot of published experiments a parameter β is introduced to describe an inhomogeneous distribution of the impact energy over the target. It varies from 0 (for an utterly inhomogeneous distribution of the impact energy) to 1 (for a homogeneous distribution). The lower limit of fragment masses is defined as a characteristic fragment mass for which the energy of fragment formation is minimal. This mass value depends crucially on the value of β. It is shown that for β≪1 only small fragments can be formed, and the maximal permitted fragment (of mass m1) is the upper boundary of the first stage of the fracture process and the point where the knee takes place. The second stage may be realized after a homogeneous redistribution of the remainder of the impact energy over the remainder of the target (when β→1). Here, the formation of great fragments is permitted only and the smallest of them (of mass m2) determines a lower boundary of the second stage. Different forms of the knee can be observed depending on relations between m1 and m2.
Retrieval dynamics and retention in cross-situational statistical word learning
Vlach, Haley A.; Sandhofer, Catherine M.
2013-01-01
Previous research on cross-situational word learning has demonstrated that learners are able to reduce ambiguity in mapping words to referents by tracking co-occurrence probabilities across learning events. In the current experiments, we examined whether learners are able to retain mappings over time. The results revealed that learners are able to retain mappings for up to one week later. However, there were interactions between the amount of retention and the different learning conditions. Interestingly, the strongest retention was associated with a learning condition that engendered retrieval dynamics that initially challenged the learner, but eventually lead to more successful retrieval toward the end of learning. The ease/difficulty of retrieval is a critical process underlying cross-situational word learning and is a powerful example of how learning dynamics affect long-term learning outcomes. PMID:24117698
Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods
ERIC Educational Resources Information Center
Soroush, Masoud; Weinberger, Charles B.
2010-01-01
This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…
A Systems Approach to the Teaching-Learning Process.
ERIC Educational Resources Information Center
Belgard, Maria R.
This paper introduces the concept of educational systems analysis, shows how it can be applied to the teaching-learning process, and indicates how the teaching-learning process, as a system, can be optimized by using operations research techniques. The teaching-learning process is viewed as a highly complex learning control system with the purpose…
Connectionist perspectives on language learning, representation and processing.
Joanisse, Marc F; McClelland, James L
2015-01-01
The field of formal linguistics was founded on the premise that language is mentally represented as a deterministic symbolic grammar. While this approach has captured many important characteristics of the world's languages, it has also led to a tendency to focus theoretical questions on the correct formalization of grammatical rules while also de-emphasizing the role of learning and statistics in language development and processing. In this review we present a different approach to language research that has emerged from the parallel distributed processing or 'connectionist' enterprise. In the connectionist framework, mental operations are studied by simulating learning and processing within networks of artificial neurons. With that in mind, we discuss recent progress in connectionist models of auditory word recognition, reading, morphology, and syntactic processing. We argue that connectionist models can capture many important characteristics of how language is learned, represented, and processed, as well as providing new insights about the source of these behavioral patterns. Just as importantly, the networks naturally capture irregular (non-rule-like) patterns that are common within languages, something that has been difficult to reconcile with rule-based accounts of language without positing separate mechanisms for rules and exceptions.
Methods of learning in statistical education: Design and analysis of a randomized trial
NASA Astrophysics Data System (ADS)
Boyd, Felicity Turner
Background. Recent psychological and technological advances suggest that active learning may enhance understanding and retention of statistical principles. A randomized trial was designed to evaluate the addition of innovative instructional methods within didactic biostatistics courses for public health professionals. Aims. The primary objectives were to evaluate and compare the addition of two active learning methods (cooperative and internet) on students' performance; assess their impact on performance after adjusting for differences in students' learning style; and examine the influence of learning style on trial participation. Methods. Consenting students enrolled in a graduate introductory biostatistics course were randomized to cooperative learning, internet learning, or control after completing a pretest survey. The cooperative learning group participated in eight small group active learning sessions on key statistical concepts, while the internet learning group accessed interactive mini-applications on the same concepts. Controls received no intervention. Students completed evaluations after each session and a post-test survey. Study outcome was performance quantified by examination scores. Intervention effects were analyzed by generalized linear models using intent-to-treat analysis and marginal structural models accounting for reported participation. Results. Of 376 enrolled students, 265 (70%) consented to randomization; 69, 100, and 96 students were randomized to the cooperative, internet, and control groups, respectively. Intent-to-treat analysis showed no differences between study groups; however, 51% of students in the intervention groups had dropped out after the second session. After accounting for reported participation, expected examination scores were 2.6 points higher (of 100 points) after completing one cooperative learning session (95% CI: 0.3, 4.9) and 2.4 points higher after one internet learning session (95% CI: 0.0, 4.7), versus
Twelve-Month-Old Infants Benefit From Prior Experience in Statistical Learning
Lany, Jill; Gómez, Rebecca L.
2010-01-01
A decade of research suggests that infants readily detect patterns in their environment, but it is unclear how such learning changes with experience. We tested how prior experience influences sensitivity to statistical regularities in an artificial language. Although 12-month-old infants learn adjacent relationships between word categories, they do not track nonadjacent relationships until 15 months. We asked whether 12-month-old infants could generalize experience with adjacent dependencies to nonadjacent ones. Infants were familiarized to an artificial language either containing or lacking adjacent dependencies between word categories and were subsequently habituated to novel nonadjacent dependencies. Prior experience with adjacent dependencies resulted in enhanced learning of the nonadjacent dependencies. Female infants showed better discrimination than males did, which is consistent with earlier reported sex differences in verbal memory capacity. The findings suggest that prior experience can bootstrap infants’ learning of difficult language structure and that learning mechanisms are powerfully affected by experience. PMID:19121132
A Discussion of the Statistical Investigation Process in the Australian Curriculum
ERIC Educational Resources Information Center
McQuade, Vivienne
2013-01-01
Statistics and statistical literacy can be found in the Learning Areas of Mathematics, Geography, Science, History and the upcoming Business and Economics, as well as in the General Capability of Numeracy and all three Crosscurriculum priorities. The Australian Curriculum affords many exciting and varied entry points for the teaching of…
Gao, Xinbo; Gao, Fei; Tao, Dacheng; Li, Xuelong
2013-12-01
Universal blind image quality assessment (IQA) metrics that can work for various distortions are of great importance for image processing systems, because neither ground truths are available nor the distortion types are aware all the time in practice. Existing state-of-the-art universal blind IQA algorithms are developed based on natural scene statistics (NSS). Although NSS-based metrics obtained promising performance, they have some limitations: 1) they use either the Gaussian scale mixture model or generalized Gaussian density to predict the nonGaussian marginal distribution of wavelet, Gabor, or discrete cosine transform coefficients. The prediction error makes the extracted features unable to reflect the change in nonGaussianity (NG) accurately. The existing algorithms use the joint statistical model and structural similarity to model the local dependency (LD). Although this LD essentially encodes the information redundancy in natural images, these models do not use information divergence to measure the LD. Although the exponential decay characteristic (EDC) represents the property of natural images that large/small wavelet coefficient magnitudes tend to be persistent across scales, which is highly correlated with image degradations, it has not been applied to the universal blind IQA metrics; and 2) all the universal blind IQA metrics use the same similarity measure for different features for learning the universal blind IQA metrics, though these features have different properties. To address the aforementioned problems, we propose to construct new universal blind quality indicators using all the three types of NSS, i.e., the NG, LD, and EDC, and incorporating the heterogeneous property of multiple kernel learning (MKL). By analyzing how different distortions affect these statistical properties, we present two universal blind quality assessment models, NSS global scheme and NSS two-step scheme. In the proposed metrics: 1) we exploit the NG of natural images
A comprehensive analysis of the IMRT dose delivery process using statistical process control (SPC)
Gerard, Karine; Grandhaye, Jean-Pierre; Marchesi, Vincent; Kafrouni, Hanna; Husson, Francois; Aletti, Pierre
2009-04-15
The aim of this study is to introduce tools to improve the security of each IMRT patient treatment by determining action levels for the dose delivery process. To achieve this, the patient-specific quality control results performed with an ionization chamber--and which characterize the dose delivery process--have been retrospectively analyzed using a method borrowed from industry: Statistical process control (SPC). The latter consisted in fulfilling four principal well-structured steps. The authors first quantified the short term variability of ionization chamber measurements regarding the clinical tolerances used in the cancer center ({+-}4% of deviation between the calculated and measured doses) by calculating a control process capability (C{sub pc}) index. The C{sub pc} index was found superior to 4, which implies that the observed variability of the dose delivery process is not biased by the short term variability of the measurement. Then, the authors demonstrated using a normality test that the quality control results could be approximated by a normal distribution with two parameters (mean and standard deviation). Finally, the authors used two complementary tools--control charts and performance indices--to thoroughly analyze the IMRT dose delivery process. Control charts aim at monitoring the process over time using statistical control limits to distinguish random (natural) variations from significant changes in the process, whereas performance indices aim at quantifying the ability of the process to produce data that are within the clinical tolerances, at a precise moment. The authors retrospectively showed that the analysis of three selected control charts (individual value, moving-range, and EWMA control charts) allowed efficient drift detection of the dose delivery process for prostate and head-and-neck treatments before the quality controls were outside the clinical tolerances. Therefore, when analyzed in real time, during quality controls, they should
A comprehensive analysis of the IMRT dose delivery process using statistical process control (SPC).
Gérard, Karine; Grandhaye, Jean-Pierre; Marchesi, Vincent; Kafrouni, Hanna; Husson, François; Aletti, Pierre
2009-04-01
The aim of this study is to introduce tools to improve the security of each IMRT patient treatment by determining action levels for the dose delivery process. To achieve this, the patient-specific quality control results performed with an ionization chamber--and which characterize the dose delivery process--have been retrospectively analyzed using a method borrowed from industry: Statistical process control (SPC). The latter consisted in fulfilling four principal well-structured steps. The authors first quantified the short-term variability of ionization chamber measurements regarding the clinical tolerances used in the cancer center (+/- 4% of deviation between the calculated and measured doses) by calculating a control process capability (C(pc)) index. The C(pc) index was found superior to 4, which implies that the observed variability of the dose delivery process is not biased by the short-term variability of the measurement. Then, the authors demonstrated using a normality test that the quality control results could be approximated by a normal distribution with two parameters (mean and standard deviation). Finally, the authors used two complementary tools--control charts and performance indices--to thoroughly analyze the IMRT dose delivery process. Control charts aim at monitoring the process over time using statistical control limits to distinguish random (natural) variations from significant changes in the process, whereas performance indices aim at quantifying the ability of the process to produce data that are within the clinical tolerances, at a precise moment. The authors retrospectively showed that the analysis of three selected control charts (individual value, moving-range, and EWMA control charts) allowed efficient drift detection of the dose delivery process for prostate and head-and-neck treatments before the quality controls were outside the clinical tolerances. Therefore, when analyzed in real time, during quality controls, they should improve the
The application of statistical process control to the development of CIS-based photovoltaics
NASA Astrophysics Data System (ADS)
Wieting, R. D.
1996-01-01
This paper reviews the application of Statistical Process Control (SPC) as well as other statistical methods to the development of thin film CuInSe2-based module fabrication processes. These methods have rigorously demonstrated the reproducibility of a number of individual process steps in module fabrication and led to the identification of previously unrecognized sources of process variation. A process exhibiting good statistical control with 11.4% mean module efficiency has been demonstrated.
Statistics in Action: The Story of a Successful Service-Learning Project
ERIC Educational Resources Information Center
DeHart, Mary; Ham, Jim
2011-01-01
The purpose of this article is to share the stories of an Introductory Statistics service-learning project in which students from both New Jersey and Michigan design and conduct phone surveys that lead to publication in local newspapers; to discuss the pedagogical benefits and challenges of the project; and to provide information for those who…
ERIC Educational Resources Information Center
Koparan, Timur; Güven, Bülent
2015-01-01
The point of this study is to define the effect of project-based learning approach on 8th Grade secondary-school students' statistical literacy levels for data representation. To achieve this goal, a test which consists of 12 open-ended questions in accordance with the views of experts was developed. Seventy 8th grade secondary-school students, 35…
The Impact of Animation Interactivity on Novices' Learning of Introductory Statistics
ERIC Educational Resources Information Center
Wang, Pei-Yu; Vaughn, Brandon K.; Liu, Min
2011-01-01
This study examined the impact of animation interactivity on novices' learning of introductory statistics. The interactive animation program used in this study was created with Adobe Flash following Mayer's multimedia design principles as well as Kristof and Satran's interactivity theory. This study was guided by three main questions: 1) Is there…
A Web-Based Learning Tool Improves Student Performance in Statistics: A Randomized Masked Trial
ERIC Educational Resources Information Center
Gonzalez, Jose A.; Jover, Lluis; Cobo, Erik; Munoz, Pilar
2010-01-01
Background: e-status is a web-based tool able to generate different statistical exercises and to provide immediate feedback to students' answers. Although the use of Information and Communication Technologies (ICTs) is becoming widespread in undergraduate education, there are few experimental studies evaluating its effects on learning. Method: All…
Reaching Adults for Lifelong Learning. III. Directory of Reporting Programs and Statistical Tables.
ERIC Educational Resources Information Center
Paisley, Matilda B.; And Others
Data from programs which participated in a study of lifelong learning programs in the United States are contained in this volume. A directory of 949 reporting adult education programs is followed by statistical tables with results from promotion questionnaires. Institutions are compared by size and type. (RS)
Hart, Carl R; Reznicek, Nathan J; Wilson, D Keith; Pettit, Chris L; Nykaza, Edward T
2016-05-01
Many outdoor sound propagation models exist, ranging from highly complex physics-based simulations to simplified engineering calculations, and more recently, highly flexible statistical learning methods. Several engineering and statistical learning models are evaluated by using a particular physics-based model, namely, a Crank-Nicholson parabolic equation (CNPE), as a benchmark. Narrowband transmission loss values predicted with the CNPE, based upon a simulated data set of meteorological, boundary, and source conditions, act as simulated observations. In the simulated data set sound propagation conditions span from downward refracting to upward refracting, for acoustically hard and soft boundaries, and low frequencies. Engineering models used in the comparisons include the ISO 9613-2 method, Harmonoise, and Nord2000 propagation models. Statistical learning methods used in the comparisons include bagged decision tree regression, random forest regression, boosting regression, and artificial neural network models. Computed skill scores are relative to sound propagation in a homogeneous atmosphere over a rigid ground. Overall skill scores for the engineering noise models are 0.6%, -7.1%, and 83.8% for the ISO 9613-2, Harmonoise, and Nord2000 models, respectively. Overall skill scores for the statistical learning models are 99.5%, 99.5%, 99.6%, and 99.6% for bagged decision tree, random forest, boosting, and artificial neural network regression models, respectively.
ERIC Educational Resources Information Center
Arciuli, Joanne; Simpson, Ian C.
2011-01-01
It is possible that statistical learning (SL) plays a role in almost every mental activity. Indeed, research on SL has grown rapidly over recent decades in an effort to better understand perception and cognition. Yet, there remain gaps in our understanding of how SL operates, in particular with regard to its (im)mutability. Here, we investigated…
Statistical Learning Is Not Affected by a Prior Bout of Physical Exercise
ERIC Educational Resources Information Center
Stevens, David J.; Arciuli, Joanne; Anderson, David I.
2016-01-01
This study examined the effect of a prior bout of exercise on implicit cognition. Specifically, we examined whether a prior bout of moderate intensity exercise affected performance on a statistical learning task in healthy adults. A total of 42 participants were allocated to one of three conditions--a control group, a group that exercised for…
Learning of spatial statistics in nonhuman primates: contextual cueing in baboons (Papio papio).
Goujon, Annabelle; Fagot, Joel
2013-06-15
A growing number of theories of cognition suggest that many of our behaviors result from the ability to implicitly extract and use statistical redundancies present in complex environments. In an attempt to develop an animal model of statistical learning mechanisms in humans, the current study investigated spatial contextual cueing (CC) in nonhuman primates. Twenty-five baboons (Papio papio) were trained to search for a target (T) embedded within configurations of distrators (L) that were either predictive or non-predictive of the target location. Baboons exhibited an early CC effect, which remained intact after a 6-week delay and stable across extensive training of 20,000 trials. These results demonstrate the baboons' ability to learn spatial contingencies, as well as the robustness of CC as a cognitive phenomenon across species. Nevertheless, in both the youngest and oldest baboons, CC required many more trials to emerge than in baboons of intermediate age. As a whole, these results reveal strong similarities between CC in humans and baboons, suggesting similar statistical learning mechanisms in these two species. Therefore, baboons provide a valid model to investigate how statistical learning mechanisms develop and/or age during the life span, as well as how these mechanisms are implemented in neural networks, and how they have evolved throughout the phylogeny.
Technology Transfer Automated Retrieval System (TEKTRAN)
Tillage management practices have direct impact on water holding capacity, evaporation, carbon sequestration, and water quality. This study examines the feasibility of two statistical learning algorithms, such as Least Square Support Vector Machine (LSSVM) and Relevance Vector Machine (RVM), for cla...
ERIC Educational Resources Information Center
Tillmann, Barbara; McAdams, Stephen
2004-01-01
The present study investigated the influence of acoustical characteristics on the implicit learning of statistical regularities (transition probabilities) in sequences of musical timbres. The sequences were constructed in such a way that the acoustical dissimilarities between timbres potentially created segmentations that either supported (S1) or…
Students' Learning Strategies: Statistical Types and Their Relationship with Computer Literacy
ERIC Educational Resources Information Center
Saparniene, Diana
2006-01-01
Purpose: The purpose of this article is to identify and describe existing students' statistical types by their learning strategies and to show the connection with factual computer literacy. Methodology: The empirical-experimental part of the present study is based on a series of diagnostic studies of 1004 surveyed Lithuanian students. The article…
Words in a Sea of Sounds: The Output of Infant Statistical Learning.
ERIC Educational Resources Information Center
Saffran, Jenny R.
2001-01-01
Three experiments assessed the extent to which statistical learning generates novel word-like units, rather than probabilistically-related strings of sounds. Found that 8-month-olds' listening preferences were affected by the context (English versus nonsense) in which items from the familiarization phase were embedded during testing. Confirmed…
Learning Axes and Bridging Tools in a Technology-Based Design for Statistics
ERIC Educational Resources Information Center
Abrahamson, Dor; Wilensky, Uri
2007-01-01
We introduce a design-based research framework, "learning axes and bridging tools," and demonstrate its application in the preparation and study of an implementation of a middle-school experimental computer-based unit on probability and statistics, "ProbLab" (Probability Laboratory, Abrahamson and Wilensky 2002 [Abrahamson, D., & Wilensky, U.…
Interacting Effects of Instructions and Presentation Rate on Visual Statistical Learning
Bertels, Julie; Destrebecqz, Arnaud; Franco, Ana
2015-01-01
The statistical regularities of a sequence of visual shapes can be learned incidentally. Arciuli et al. (2014) recently argued that intentional instructions only improve learning at slow presentation rates as they favor the use of explicit strategies. The aim of the present study was (1) to test this assumption directly by investigating how instructions (incidental vs. intentional) and presentation rate (fast vs. slow) affect the acquisition of knowledge and (2) to examine how these factors influence the conscious vs. unconscious nature of the knowledge acquired. To this aim, we exposed participants to four triplets of shapes, presented sequentially in a pseudo-random order, and assessed their degree of learning in a subsequent completion task that integrated confidence judgments. Supporting Arciuli et al.’s (2014) claim, participant performance only benefited from intentional instructions at slow presentation rates. Moreover, informing participants beforehand about the existence of statistical regularities increased their explicit knowledge of the sequences, an effect that was not modulated by presentation speed. These results support that, although visual statistical learning can take place incidentally and, to some extent, outside conscious awareness, factors such as presentation rate and prior knowledge can boost learning of these regularities, presumably by favoring the acquisition of explicit knowledge. PMID:26648884
Using an online web magazine to motivate the learning of statistics
NASA Astrophysics Data System (ADS)
Din, Nor Anis Hayati Mohd; Ismail, Zaleha; Sulaiman, Norhafizah
2015-02-01
Students have difficulties to appreciate the role of mathematics in their daily life. Failure to understand how mathematics is relevant in life might hinder learning. An online web magazine entitled Dunia Matematik has been published to share mathematics that beyond the classrooms. This paper discusses students' rating of the articles which portray statistics in real life that made available at one section of the web magazine. Students' motivate on were assesed based on four components of ARCS model: Attention, Relevance, Confidence and Satisfaction. The thirty-seven form four students who participated in the study were given one hour to review on some selected articles on statistics that have been published in Dunia Matematik. At the end of the one hour session the participants were required to respond to 20 survey items. Findings showed that the students rated highest for Confidence while Attention was rated lowest. Meanwhile, the overall rating for the articles on Statistics of the online web magazine Dunia Matematik was rated considerably high. The study shows that form four students viewed the articles on applications of statistics published in Dunia Matematik has the potential to motivate the learning of statistics. It is hoped that students take advantage to what is offered by this local web magazine to attain learning and experience beyond classrooms.
Spatio-temporal statistical models with applications to atmospheric processes
Wikle, C.K.
1996-12-31
This doctoral dissertation is presented as three self-contained papers. An introductory chapter considers traditional spatio-temporal statistical methods used in the atmospheric sciences from a statistical perspective. Although this section is primarily a review, many of the statistical issues considered have not been considered in the context of these methods and several open questions are posed. The first paper attempts to determine a means of characterizing the semiannual oscillation (SAO) spatial variation in the northern hemisphere extratropical height field. It was discovered that the midlatitude SAO in 500hPa geopotential height could be explained almost entirely as a result of spatial and temporal asymmetries in the annual variation of stationary eddies. It was concluded that the mechanism for the SAO in the northern hemisphere is a result of land-sea contrasts. The second paper examines the seasonal variability of mixed Rossby-gravity waves (MRGW) in lower stratospheric over the equatorial Pacific. Advanced cyclostationary time series techniques were used for analysis. It was found that there are significant twice-yearly peaks in MRGW activity. Analyses also suggested a convergence of horizontal momentum flux associated with these waves. In the third paper, a new spatio-temporal statistical model is proposed that attempts to consider the influence of both temporal and spatial variability. This method is mainly concerned with prediction in space and time, and provides a spatially descriptive and temporally dynamic model.
Experience and Sentence Processing: Statistical Learning and Relative Clause Comprehension
ERIC Educational Resources Information Center
Wells, Justine B.; Christiansen, Morten H.; Race, David S.; Acheson, Daniel J.; MacDonald, Maryellen C.
2009-01-01
Many explanations of the difficulties associated with interpreting object relative clauses appeal to the demands that object relatives make on working memory. MacDonald and Christiansen [MacDonald, M. C., & Christiansen, M. H. (2002). "Reassessing working memory: Comment on Just and Carpenter (1992) and Waters and Caplan (1996)." "Psychological…
Pitch-class distribution modulates the statistical learning of atonal chord sequences.
Daikoku, Tatsuya; Yatomi, Yutaka; Yumoto, Masato
2016-10-01
The present study investigated whether neural responses could demonstrate the statistical learning of chord sequences and how the perception underlying a pitch class can affect the statistical learning of chord sequences. Neuromagnetic responses to two chord sequences of augmented triads that were presented every 0.5s were recorded from fourteen right-handed participants. One sequence was a series of 360 chord triplets, each of which consisted of three chords in the same pitch class (clustered pitch-classes sequences). The other sequence was a series of 360 chord triplets, each of which consisted of three chords in different pitch classes (dispersed pitch-classes sequences). The order of the triplets was constrained by a first-order Markov stochastic model such that a forthcoming triplet was statistically defined by the most recent triplet (80% for one; 20% for the other two). We performed a repeated-measures ANOVA with the peak amplitude and latency of the P1m, N1m and P2m. In the clustered pitch-classes sequences, the P1m responses to the triplets that appeared with higher transitional probability were significantly reduced compared with those with lower transitional probability, whereas no significant result was detected in the dispersed pitch-classes sequences. Neuromagnetic significance was concordant with the results of familiarity interviews conducted after each learning session. The P1m response is a useful index for the statistical learning of chord sequences. Domain-specific perception based on the pitch class may facilitate the domain-general statistical learning of chord sequences. PMID:27429093
Modulation of spatial attention by goals, statistical learning, and monetary reward.
Jiang, Yuhong V; Sha, Li Z; Remington, Roger W
2015-10-01
This study documented the relative strength of task goals, visual statistical learning, and monetary reward in guiding spatial attention. Using a difficult T-among-L search task, we cued spatial attention to one visual quadrant by (i) instructing people to prioritize it (goal-driven attention), (ii) placing the target frequently there (location probability learning), or (iii) associating that quadrant with greater monetary gain (reward-based attention). Results showed that successful goal-driven attention exerted the strongest influence on search RT. Incidental location probability learning yielded a smaller though still robust effect. Incidental reward learning produced negligible guidance for spatial attention. The 95 % confidence intervals of the three effects were largely nonoverlapping. To understand these results, we simulated the role of location repetition priming in probability cuing and reward learning. Repetition priming underestimated the strength of location probability cuing, suggesting that probability cuing involved long-term statistical learning of how to shift attention. Repetition priming provided a reasonable account for the negligible effect of reward on spatial attention. We propose a multiple-systems view of spatial attention that includes task goals, search habit, and priming as primary drivers of top-down attention.
Modulation of spatial attention by goals, statistical learning, and monetary reward
Sha, Li Z.; Remington, Roger W.
2015-01-01
This study documented the relative strength of task goals, visual statistical learning, and monetary reward in guiding spatial attention. Using a difficult T-among-L search task, we cued spatial attention to one visual quadrant by (i) instructing people to prioritize it (goal-driven attention), (ii) placing the target frequently there (location probability learning), or (iii) associating that quadrant with greater monetary gain (reward-based attention). Results showed that successful goal-driven attention exerted the strongest influence on search RT. Incidental location probability learning yielded a smaller though still robust effect. Incidental reward learning produced negligible guidance for spatial attention. The 95 % confidence intervals of the three effects were largely nonoverlapping. To understand these results, we simulated the role of location repetition priming in probability cuing and reward learning. Repetition priming underestimated the strength of location probability cuing, suggesting that probability cuing involved long-term statistical learning of how to shift attention. Repetition priming provided a reasonable account for the negligible effect of reward on spatial attention. We propose a multiple-systems view of spatial attention that includes task goals, search habit, and priming as primary drivers of top-down attention. PMID:26105657
Multisensory perception as an associative learning process
Connolly, Kevin
2014-01-01
Suppose that you are at a live jazz show. The drummer begins a solo. You see the cymbal jolt and you hear the clang. But in addition seeing the cymbal jolt and hearing the clang, you are also aware that the jolt and the clang are part of the same event. Casey O’Callaghan (forthcoming) calls this awareness “intermodal feature binding awareness.” Psychologists have long assumed that multimodal perceptions such as this one are the result of a automatic feature binding mechanism (see Pourtois et al., 2000; Vatakis and Spence, 2007; Navarra et al., 2012). I present new evidence against this. I argue that there is no automatic feature binding mechanism that couples features like the jolt and the clang together. Instead, when you experience the jolt and the clang as part of the same event, this is the result of an associative learning process. The cymbal’s jolt and the clang are best understood as a single learned perceptual unit, rather than as automatically bound. I outline the specific learning process in perception called “unitization,” whereby we come to “chunk” the world into multimodal units. Unitization has never before been applied to multimodal cases. Yet I argue that this learning process can do the same work that intermodal binding would do, and that this issue has important philosophical implications. Specifically, whether we take multimodal cases to involve a binding mechanism or an associative process will have impact on philosophical issues from Molyneux’s question to the question of how active or passive we consider perception to be. PMID:25309498
Multisensory perception as an associative learning process.
Connolly, Kevin
2014-01-01
Suppose that you are at a live jazz show. The drummer begins a solo. You see the cymbal jolt and you hear the clang. But in addition seeing the cymbal jolt and hearing the clang, you are also aware that the jolt and the clang are part of the same event. Casey O'Callaghan (forthcoming) calls this awareness "intermodal feature binding awareness." Psychologists have long assumed that multimodal perceptions such as this one are the result of a automatic feature binding mechanism (see Pourtois et al., 2000; Vatakis and Spence, 2007; Navarra et al., 2012). I present new evidence against this. I argue that there is no automatic feature binding mechanism that couples features like the jolt and the clang together. Instead, when you experience the jolt and the clang as part of the same event, this is the result of an associative learning process. The cymbal's jolt and the clang are best understood as a single learned perceptual unit, rather than as automatically bound. I outline the specific learning process in perception called "unitization," whereby we come to "chunk" the world into multimodal units. Unitization has never before been applied to multimodal cases. Yet I argue that this learning process can do the same work that intermodal binding would do, and that this issue has important philosophical implications. Specifically, whether we take multimodal cases to involve a binding mechanism or an associative process will have impact on philosophical issues from Molyneux's question to the question of how active or passive we consider perception to be.
Dollars and Sense: Convincing Students that They Can Learn and Want to Learn Statistics
ERIC Educational Resources Information Center
Paxton, Pamela
2006-01-01
Quantitative methods are an integral part of much sociological research. For that reason, many sociology departments require a course in statistics as part of an undergraduate major in sociology, and most faculty view the undergraduate statistics course as a valuable part of an undergraduate student's training in sociology. Unfortunately, most…
Big data analysis using modern statistical and machine learning methods in medicine.
Yoo, Changwon; Ramirez, Luis; Liuzzi, Juan
2014-06-01
In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.
Statistical and signal-processing concepts in surface metrology
Church, E.L.; Takacs, P.Z.
1986-03-01
This paper proposes the use of a simple two-scale model of surface roughness for testing and specifying the topographic figure and finish of synchrotron-radiation mirrors. In this approach the effects of figure and finish are described in terms of their slope distribution and power spectrum, respectively, which are then combined with the system point spread function to produce a composite image. The result can be used to predict mirror performance or to translate design requirements into manufacturing specifications. Pacing problems in this approach are the development of a practical long-trace slope-profiling instrument and realistic statistical models for figure and finish errors.
Toward digital staining using stimulated Raman scattering and statistical machine learning
NASA Astrophysics Data System (ADS)
Tanji, K.; Otsuka, Y.; Satoh, S.; Hashimoto, H.; Ozeki, Y.; Itoh, Kazuyoshi
2014-03-01
Stimulated Raman scattering (SRS) spectral microscopy is a promising imaging method, based on vibrational spectroscopy, which can visualize biological tissues with chemical specificity. SRS spectral microscopy has been used to obtain two-dimensional spectral images of rat liver tissue, three-dimensional images of a vessel in rat liver, and in vivo spectral images of mouse ear skin. Various multivariate analysis techniques, such as principal component analysis and independent component analysis, have been used to obtain spectral images. In this study, we propose a digital staining method. This method uses SRS spectra and statistical machine learning that makes use of prior knowledge of spectral peaks and their two-dimensional distributional patterns corresponding to the composition of tissue samples. The method selects spectral peaks on the basis of Mahalanobis distance, which is defined as the ratio of inter-group variation to intragroup variation. We also make use of higher-order local autocorrelations as feature values for two-dimensional distributional patterns. This combination of techniques allows groups corresponding to different intracellular structures to be clearly discriminated in the multidimensional feature space. We investigate the performance of our method on mouse liver tissue samples and show that the proposed method can digitally stain each intracellular structure such as cell nuclei, cytoplasm, and erythrocytes separately and clearly without time-consuming chemical staining processes. We anticipate that our method could be applied to computer-aided pathological diagnosis.
Statistical-Mechanical Analysis of Semi-Supervised Learning and Its Optimal Scheduling
NASA Astrophysics Data System (ADS)
Fujii, Takashi; Ito, Hidetaka; Miyoshi, Seiji
2016-08-01
Semi-supervised learning is a paradigm that uses a large number of unlabeled data and a small number of labeled data. We analyze the dynamical behaviors of semi-supervised learning in the framework of on-line learning by the statistical-mechanical method. A student uses several correlated input vectors in each update. The student is given a desired output for only one input vector out of these correlated input vectors. In this model, we derive simultaneous differential equations with deterministic forms that describe the dynamical behaviors of order parameters using the self-averaging property in the thermodynamic limit. We treat three well-known learning rules, that is, the Hebbian, Perceptron, and AdaTron learning rules. As a result, it is shown that using unlabeled data is effective in the early stages for all three learning rules. In addition, we show that the three learning rules have qualitatively different dynamical behaviors. Furthermore, we propose a new algorithm that improves the generalization performance by switching the number of input vectors used in an update as the time step proceeds.
Statistical Process Control of a Kalman Filter Model
Gamse, Sonja; Nobakht-Ersi, Fereydoun; Sharifi, Mohammad A.
2014-01-01
For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF) algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations. PMID:25264959
ERIC Educational Resources Information Center
Dietze, Stefan; Gugliotta, Alessio; Domingue, John
2009-01-01
Current E-Learning technologies primarily follow a data and metadata-centric paradigm by providing the learner with composite content containing the learning resources and the learning process description, usually based on specific metadata standards such as ADL SCORM or IMS Learning Design. Due to the design-time binding of learning resources,…
Learning Package by Means of the Inductive Teaching with Group Process
ERIC Educational Resources Information Center
Sawangsri, Benchaporn
2016-01-01
This research focuses on the inductive teaching with group process and students' behavior as working in a group. There were four instruments under this study. The descriptive statistics were employed. The findings revealed that, firstly, the effectiveness of LPIDTGP [Learning Package by means of the Inductive Teaching with Group Process] is higher…
Statistical process control for AR(1) or non-Gaussian processes using wavelets coefficients
NASA Astrophysics Data System (ADS)
Cohen, A.; Tiplica, T.; Kobi, A.
2015-11-01
Autocorrelation and non-normality of process characteristic variables are two main difficulties that industrial engineers must face when they should implement control charting techniques. This paper presents new issues regarding the probability distribution of wavelets coefficients. Firstly, we highlight that wavelets coefficients have capacities to strongly decrease autocorrelation degree of original data and are normally-like distributed, especially in the case of Haar wavelet. We used AR(1) model with positive autoregressive parameters to simulate autocorrelated data. Illustrative examples are presented to show wavelets coefficients properties. Secondly, the distributional parameters of wavelets coefficients are derived, it shows that wavelets coefficients reflect an interesting statistical properties for SPC purposes.
ERIC Educational Resources Information Center
Kamaruddin, Nafisah Kamariah Md; Jaafar, Norzilaila bt; Amin, Zulkarnain Md
2012-01-01
Inaccurate concept in statistics contributes to the assumption by the students that statistics do not relate to the real world and are not relevant to the engineering field. There are universities which introduced learning statistics using statistics lab activities. However, the learning is more on the learning how to use software and not to…
A computational visual saliency model based on statistics and machine learning.
Lin, Ru-Je; Lin, Wei-Song
2014-01-01
Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases. PMID:25084782
Statistical Considerations of Data Processing in Giovanni Online Tool
NASA Technical Reports Server (NTRS)
Suhung, Shen; Leptoukh, G.; Acker, J.; Berrick, S.
2005-01-01
The GES DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni) is a web-based interface for the rapid visualization and analysis of gridded data from a number of remote sensing instruments. The GES DISC currently employs several Giovanni instances to analyze various products, such as Ocean-Giovanni for ocean products from SeaWiFS and MODIS-Aqua; TOMS & OM1 Giovanni for atmospheric chemical trace gases from TOMS and OMI, and MOVAS for aerosols from MODIS, etc. (http://giovanni.gsfc.nasa.gov) Foremost among the Giovanni statistical functions is data averaging. Two aspects of this function are addressed here. The first deals with the accuracy of averaging gridded mapped products vs. averaging from the ungridded Level 2 data. Some mapped products contain mean values only; others contain additional statistics, such as number of pixels (NP) for each grid, standard deviation, etc. Since NP varies spatially and temporally, averaging with or without weighting by NP will be different. In this paper, we address differences of various weighting algorithms for some datasets utilized in Giovanni. The second aspect is related to different averaging methods affecting data quality and interpretation for data with non-normal distribution. The present study demonstrates results of different spatial averaging methods using gridded SeaWiFS Level 3 mapped monthly chlorophyll a data. Spatial averages were calculated using three different methods: arithmetic mean (AVG), geometric mean (GEO), and maximum likelihood estimator (MLE). Biogeochemical data, such as chlorophyll a, are usually considered to have a log-normal distribution. The study determined that differences between methods tend to increase with increasing size of a selected coastal area, with no significant differences in most open oceans. The GEO method consistently produces values lower than AVG and MLE. The AVG method produces values larger than MLE in some cases, but smaller in other cases. Further
Statistical learning and the challenge of syntax: Beyond finite state automata
NASA Astrophysics Data System (ADS)
Elman, Jeff
2003-10-01
Over the past decade, it has been clear that even very young infants are sensitive to the statistical structure of language input presented to them, and use the distributional regularities to induce simple grammars. But can such statistically-driven learning also explain the acquisition of more complex grammar, particularly when the grammar includes recursion? Recent claims (e.g., Hauser, Chomsky, and Fitch, 2002) have suggested that the answer is no, and that at least recursion must be an innate capacity of the human language acquisition device. In this talk evidence will be presented that indicates that, in fact, statistically-driven learning (embodied in recurrent neural networks) can indeed enable the learning of complex grammatical patterns, including those that involve recursion. When the results are generalized to idealized machines, it is found that the networks are at least equivalent to Push Down Automata. Perhaps more interestingly, with limited and finite resources (such as are presumed to exist in the human brain) these systems demonstrate patterns of performance that resemble those in humans.
Exploring the Neurodevelopment of Visual Statistical Learning Using Event-Related Brain Potentials
Jost, Ethan; Conway, Christopher M.; Purdy, John D.; Walk, Anne M.; Hendricks, Michelle A.
2014-01-01
Implicit statistical learning (ISL) allows for the learning of environmental patterns and is thought to be important for many aspects of perception, cognition, and language development. However, very little is known about the development of the underlying neural mechanisms that support ISL. To explore the neurodevelopment of ISL, we investigated the event-related potential (ERP) correlates of learning in adults, older children (aged 9-12), and younger children (aged 6-9) using a novel predictor-target paradigm. In this task, which was a modification of the standard oddball paradigm, participants were instructed to view a serial input stream of visual stimuli and to respond with a button press when a particular target appeared. Unbeknownst to the participants, covert statistical probabilities were embedded in the task such that the target was predicted to varying degrees by different predictor stimuli. The results were similar across all three age groups: a P300 component that was elicited by the high predictor stimulus after sufficient exposure to the statistical probabilities. These neurophysiological findings provide evidence for developmental invariance in ISL, with adult-like competence reached by at least age 6. PMID:25475992
Preserved Statistical Learning of Tonal and Linguistic Material in Congenital Amusia
Omigie, Diana; Stewart, Lauren
2011-01-01
Congenital amusia is a lifelong disorder whereby individuals have pervasive difficulties in perceiving and producing music. In contrast, typical individuals display a sophisticated understanding of musical structure, even in the absence of musical training. Previous research has shown that they acquire this knowledge implicitly, through exposure to music's statistical regularities. The present study tested the hypothesis that congenital amusia may result from a failure to internalize statistical regularities – specifically, lower-order transitional probabilities. To explore the specificity of any potential deficits to the musical domain, learning was examined with both tonal and linguistic material. Participants were exposed to structured tonal and linguistic sequences and, in a subsequent test phase, were required to identify items which had been heard in the exposure phase, as distinct from foils comprising elements that had been present during exposure, but presented in a different temporal order. Amusic and control individuals showed comparable learning, for both tonal and linguistic material, even when the tonal stream included pitch intervals around one semitone. However analysis of binary confidence ratings revealed that amusic individuals have less confidence in their abilities and that their performance in learning tasks may not be contingent on explicit knowledge formation or level of awareness to the degree shown in typical individuals. The current findings suggest that the difficulties amusic individuals have with real-world music cannot be accounted for by an inability to internalize lower-order statistical regularities but may arise from other factors. PMID:21779263
Learning at a Distance: I. Statistical Learning of Non-Adjacent Dependencies
ERIC Educational Resources Information Center
Newport, Elissa L.; Aslin, Richard N.
2004-01-01
In earlier work we have shown that adults, young children, and infants are capable of computing transitional probabilities among adjacent syllables in rapidly presented streams of speech, and of using these statistics to group adjacent syllables into word-like units. In the present experiments we ask whether adult learners are also capable of such…
Xu Chengjian; Schaaf, Arjen van der; Schilstra, Cornelis; Langendijk, Johannes A.; Veld, Aart A. van't
2012-03-15
Purpose: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. Methods and Materials: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. Results: It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. Conclusions: The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended.
NASA Astrophysics Data System (ADS)
Platnick, Steven; Ackerman, Steven; King, Michael; Zhang, Zhibo; Wind, Galina
2013-04-01
Cloud detection algorithms search for measurement signatures that differentiate a cloud-contaminated or "not-clear" pixel from the clear-sky background. These signatures can be spectral, textural or temporal in nature. The magnitude of the difference between the cloud and the background must exceed a threshold value for the pixel to be classified having a not-clear FOV. All detection algorithms employ multiple tests ranging across some portion of the solar reflectance and/or infrared spectrum. However, a cloud is not a single, uniform object, but rather has a distribution of optical thickness and morphology. As a result, problems can arise when the distributions of cloud and clear-sky background characteristics overlap, making some test results indeterminate and/or leading to some amount of detection misclassification. Further, imager cloud retrieval statistics are highly sensitive to how a pixel identified as not-clear by a cloud mask is determined to be useful for cloud-top and optical retrievals based on 1-D radiative models. This presentation provides an overview of the different 'choices' algorithm developers make in cloud detection algorithms and the impact on regional and global cloud amounts and fractional coverage, cloud type and property distributions. Lessons learned over the course of the MODIS cloud product development history are discussed. As an example, we will focus on the 1km MODIS Collection 5 cloud optical retrieval algorithm (product MOD06/MYD06 for Terra and Aqua, respectively) which removed pixels associated with cloud edges as defined by immediate adjacency to clear FOV MODIS cloud mask (MOD35/MYD35) pixels as well as ocean pixels with partly cloudy elements in the 250m MODIS cloud mask - part of the so-called Clear Sky Restoral algorithm. The Collection 6 algorithm attempts retrievals for these two types of partly cloudy pixel populations, but allows a user to isolate or filter out the populations. Retrieval sensitivities for these
Dyscalculia, dyslexia, and medical students' needs for learning and using statistics.
MacDougall, Margaret
2009-01-01
Much has been written on the learning needs of dyslexic and dyscalculic students in primary and early secondary education. However, it is not clear that the necessary disability support staff and specialist literature are available to ensure that these needs are being adequately met within the context of learning statistics and general quantitative skills in the self-directed learning environments encountered in higher education. This commentary draws attention to dyslexia and dyscalculia as two potentially unrecognized conditions among undergraduate medical students and in turn, highlights key developments from recent literature in the diagnosis of these conditions. With a view to assisting medical educators meet the needs of dyscalculic learners and the more varied needs of dyslexic learners, a comprehensive list of suggestions is provided as to how learning resources can be designed from the outset to be more inclusive. A hitherto neglected area for future research is also identified through a call for a thorough investigation of the meaning of statistical literacy within the context of the undergraduate medical curriculum. PMID:20165516
Dyscalculia, Dyslexia, and Medical Students’ Needs for Learning and Using Statistics
MacDougall, Margaret
2009-01-01
Much has been written on the learning needs of dyslexic and dyscalculic students in primary and early secondary education. However, it is not clear that the necessary disability support staff and specialist literature are available to ensure that these needs are being adequately met within the context of learning statistics and general quantitative skills in the self-directed learning environments encountered in higher education. This commentary draws attention to dyslexia and dyscalculia as two potentially unrecognized conditions among undergraduate medical students and in turn, highlights key developments from recent literature in the diagnosis of these conditions. With a view to assisting medical educators meet the needs of dyscalculic learners and the more varied needs of dyslexic learners, a comprehensive list of suggestions is provided as to how learning resources can be designed from the outset to be more inclusive. A hitherto neglected area for future research is also identified through a call for a thorough investigation of the meaning of statistical literacy within the context of the undergraduate medical curriculum. PMID:20165516
Candini, Giancarlo
2004-12-01
In the fields of didactics and continuous professional development (CPD) plans, the increasing use of multiple answer tests for the evaluation of the level of knowledge in various kinds of subjects makes it increasingly important to have reliable and effective tools for data processing and for the evaluation of the results. The aim of the present work is to explore a new methodological approach based on a widely tested statistical analysis able to yield more information content when compared with the traditional methods. With this purpose we suggest a Graduated Response Test and the relative operating characteristic curve (ROC) for the evaluation of the results. A short description of a computerized procedure, written in Visual Basic Pro (v.6.0), which automatically performs the statistical analysis, the ROC curves plot and the calculation of a learning index is given as well. PMID:15518651
NASA Technical Reports Server (NTRS)
Safford, Robert R.; Jackson, Andrew E.; Swart, William W.; Barth, Timothy S.
1994-01-01
Successful ground processing at KSC requires that flight hardware and ground support equipment conform to specifications at tens of thousands of checkpoints. Knowledge of conformance is an essential requirement for launch. That knowledge of conformance at every requisite point does not, however, enable identification of past problems with equipment, or potential problem areas. This paper describes how the introduction of Statistical Process Control and Process Capability Analysis identification procedures into existing shuttle processing procedures can enable identification of potential problem areas and candidates for improvements to increase processing performance measures. Results of a case study describing application of the analysis procedures to Thermal Protection System processing are used to illustrate the benefits of the approaches described in the paper.
Wurtz, R.; Kaplan, A.
2015-10-28
Pulse shape discrimination (PSD) is a variety of statistical classifier. Fully-realized statistical classifiers rely on a comprehensive set of tools for designing, building, and implementing. PSD advances rely on improvements to the implemented algorithm. PSD advances can be improved by using conventional statistical classifier or machine learning methods. This paper provides the reader with a glossary of classifier-building elements and their functions in a fully-designed and operational classifier framework that can be used to discover opportunities for improving PSD classifier projects. This paper recommends reporting the PSD classifier’s receiver operating characteristic (ROC) curve and its behavior at a gamma rejection rate (GRR) relevant for realistic applications.
[The learning and motor development transfer process].
Cecchini Estrada, José Antonio; Fernández Losa, Jorge Luis; Pallasá Manteca, Miguel; Cecchini Applegatte, Christian
2012-05-01
The aim of this study is to analyze the transference process in motor skill learning. For this purpose, 320 boys and girls, with ages ranging from 3 to 12 years (M= 7.61; SD= 2.61), took part in nine object movement reception drills in which the following variables were cross-examined: the presence-absence of displacement (static or in motion), the corporal segments utilized (hands or arms), the movement direction (right or left), and the moving object (volleyball or tennis ball). The results indicate that what is being transferred is the common factor among them, the ocular-kinesthetic regulating system, which is constructed according to a generalized motor program and a predictive strategy of continuous control. The way that individuals group by levels of skill that represent the developmental levels of the aforementioned regulating system can also be observed. Lastly, the results are discussed, and strategies to improve the learning process in sports and physical education are provided.
Perspectives on Learning: Methodologies for Exploring Learning Processes and Outcomes
ERIC Educational Resources Information Center
Goldman, Susan R.
2014-01-01
The papers in this Special Issue were initially prepared for an EARLI 2013 Symposium that was designed to examine methodologies in use by researchers from two sister communities, Learning and Instruction and Learning Sciences. The four papers reflect a common ground in advances in conceptions of learning since the early days of the "cognitive…
The application of statistical process control to the development of CIS-based photovoltaics
Wieting, R.D.
1996-01-01
This paper reviews the application of Statistical Process Control (SPC) as well as other statistical methods to the development of thin film CuInSe{sub 2}-based module fabrication processes. These methods have rigorously demonstrated the reproducibility of a number of individual process steps in module fabrication and led to the identification of previously unrecognized sources of process variation. A process exhibiting good statistical control with 11.4{percent} mean module efficiency has been demonstrated. {copyright} {ital 1996 American Institute of Physics.}
Understanding the Learning Process in SMEs
ERIC Educational Resources Information Center
Carr, James; Gannon-Leary, Pat
2007-01-01
A major obstacle to the diffusion of management development learning technologies from Higher Education Institutions to Small and Medium-sized Enterprises (SMEs) is a lack of understanding about how SME learners learn. This article examines the nature of learning in SMEs and considers the incidence of informal support for informal learning.…
77 FR 46096 - Statistical Process Controls for Blood Establishments; Public Workshop
Federal Register 2010, 2011, 2012, 2013, 2014
2012-08-02
...: Statistical process control is the application of statistical methods to the monitoring, or quality control... monitors manufacturing procedures, validation summaries, and quality control data prior to licensure and... at implementation and then monitor these processes on a regular basis, using quality control...
Interactive and Historical Processes of Distributing Statistical Concepts through Work Organization
ERIC Educational Resources Information Center
Hall, Rogers; Wright, Ken; Wieckert, Karen
2007-01-01
In this article, we analyze interactive processes through which research groups and their statistical advisors insert new (for researchers) statistical concepts into existing research practice. Through processes of talk-in-interaction (speaking, gesture, and inscription), they assemble specimens, research workers, devices, algorithms, and texts,…
Tools for the Improvement of Organizational Learning Processes in Innovation.
ERIC Educational Resources Information Center
de Weerd-Nederhof, Petra C.; Pacitti, Bernice J.; da Silva Gomes, Jorge F.; Pearson, Alan W.
2002-01-01
From case studies of organizational learning in research and development companies, learning tools or mechanisms were identified: job rotation, innovation process planning, and product innovation project review. Organizational learning involved parallel rather than linear processes of information acquisition, distribution, and interpretation and…
Intertime jump statistics of state-dependent Poisson processes.
Daly, Edoardo; Porporato, Amilcare
2007-01-01
A method to obtain the probability distribution of the interarrival times of jump occurrences in systems driven by state-dependent Poisson noise is proposed. Such a method uses the survivor function obtained by a modified version of the master equation associated to the stochastic process under analysis. A model for the timing of human activities shows the capability of state-dependent Poisson noise to generate power-law distributions. The application of the method to a model for neuron dynamics and to a hydrological model accounting for land-atmosphere interaction elucidates the origin of characteristic recurrence intervals and possible persistence in state-dependent Poisson models.
Intertime jump statistics of state-dependent Poisson processes
NASA Astrophysics Data System (ADS)
Daly, Edoardo; Porporato, Amilcare
2007-01-01
A method to obtain the probability distribution of the interarrival times of jump occurrences in systems driven by state-dependent Poisson noise is proposed. Such a method uses the survivor function obtained by a modified version of the master equation associated to the stochastic process under analysis. A model for the timing of human activities shows the capability of state-dependent Poisson noise to generate power-law distributions. The application of the method to a model for neuron dynamics and to a hydrological model accounting for land-atmosphere interaction elucidates the origin of characteristic recurrence intervals and possible persistence in state-dependent Poisson models.
Statistical process control (SPC) for coordinate measurement machines
Escher, R.N.
2000-01-04
The application of process capability analysis, using designed experiments, and gage capability studies as they apply to coordinate measurement machine (CMM) uncertainty analysis and control will be demonstrated. The use of control standards in designed experiments, and the use of range charts and moving range charts to separate measurement error into it's discrete components will be discussed. The method used to monitor and analyze the components of repeatability and reproducibility will be presented with specific emphasis on how to use control charts to determine and monitor CMM performance and capability, and stay within your uncertainty assumptions.
Supramodal processing optimizes visual perceptual learning and plasticity.
Zilber, Nicolas; Ciuciu, Philippe; Gramfort, Alexandre; Azizi, Leila; van Wassenhove, Virginie
2014-06-01
Multisensory interactions are ubiquitous in cortex and it has been suggested that sensory cortices may be supramodal i.e. capable of functional selectivity irrespective of the sensory modality of inputs (Pascual-Leone and Hamilton, 2001; Renier et al., 2013; Ricciardi and Pietrini, 2011; Voss and Zatorre, 2012). Here, we asked whether learning to discriminate visual coherence could benefit from supramodal processing. To this end, three groups of participants were briefly trained to discriminate which of a red or green intermixed population of random-dot-kinematograms (RDKs) was most coherent in a visual display while being recorded with magnetoencephalography (MEG). During training, participants heard no sound (V), congruent acoustic textures (AV) or auditory noise (AVn); importantly, congruent acoustic textures shared the temporal statistics - i.e. coherence - of visual RDKs. After training, the AV group significantly outperformed participants trained in V and AVn although they were not aware of their progress. In pre- and post-training blocks, all participants were tested without sound and with the same set of RDKs. When contrasting MEG data collected in these experimental blocks, selective differences were observed in the dynamic pattern and the cortical loci responsive to visual RDKs. First and common to all three groups, vlPFC showed selectivity to the learned coherence levels whereas selectivity in visual motion area hMT+ was only seen for the AV group. Second and solely for the AV group, activity in multisensory cortices (mSTS, pSTS) correlated with post-training performances; additionally, the latencies of these effects suggested feedback from vlPFC to hMT+ possibly mediated by temporal cortices in AV and AVn groups. Altogether, we interpret our results in the context of the Reverse Hierarchy Theory of learning (Ahissar and Hochstein, 2004) in which supramodal processing optimizes visual perceptual learning by capitalizing on sensory
Nonlinear Statistical Signal Processing: A Particle Filtering Approach
Candy, J
2007-09-19
A introduction to particle filtering is discussed starting with an overview of Bayesian inference from batch to sequential processors. Once the evolving Bayesian paradigm is established, simulation-based methods using sampling theory and Monte Carlo realizations are discussed. Here the usual limitations of nonlinear approximations and non-gaussian processes prevalent in classical nonlinear processing algorithms (e.g. Kalman filters) are no longer a restriction to perform Bayesian inference. It is shown how the underlying hidden or state variables are easily assimilated into this Bayesian construct. Importance sampling methods are then discussed and shown how they can be extended to sequential solutions implemented using Markovian state-space models as a natural evolution. With this in mind, the idea of a particle filter, which is a discrete representation of a probability distribution, is developed and shown how it can be implemented using sequential importance sampling/resampling methods. Finally, an application is briefly discussed comparing the performance of the particle filter designs with classical nonlinear filter implementations.
Evaluation as Learning: Course Evaluation as Part of the Learning Process.
ERIC Educational Resources Information Center
Martensson, Paer
This paper describes an example of a course evaluation where the evaluation process becomes an important part of the learning process. The setting is an action-learning based course in an executive program. The participants apply a framework (the X-model) for perceiving processes to their own learning. The framework is presented, and experiences…
Karuza, Elisabeth A; Li, Ping; Weiss, Daniel J; Bulgarelli, Federica; Zinszer, Benjamin D; Aslin, Richard N
2016-10-01
Successful knowledge acquisition requires a cognitive system that is both sensitive to statistical information and able to distinguish among multiple structures (i.e., to detect pattern shifts and form distinct representations). Extensive behavioral evidence has highlighted the importance of cues to structural change, demonstrating how, without them, learners fail to detect pattern shifts and are biased in favor of early experience. Here, we seek a neural account of the mechanism underpinning this primacy effect in learning. During fMRI scanning, adult participants were presented with two artificial languages: a familiar language (L1) on which they had been pretrained followed by a novel language (L2). The languages were composed of the same syllable inventory organized according to unique statistical structures. In the absence of cues to the transition between languages, posttest familiarity judgments revealed that learners on average more accurately segmented words from the familiar language compared with the novel one. Univariate activation and functional connectivity analyses showed that participants with the strongest learning of L1 had decreased recruitment of fronto-subcortical and posterior parietal regions, in addition to a dissociation between downstream regions and early auditory cortex. Participants with a strong new language learning capacity (i.e., higher L2 scores) showed the opposite trend. Thus, we suggest that a bias toward neural efficiency, particularly as manifested by decreased sampling from the environment, accounts for the primacy effect in learning. Potential implications of this hypothesis are discussed, including the possibility that "inefficient" learning systems may be more sensitive to structural changes in a dynamic environment. PMID:27315265
Karuza, Elisabeth A; Li, Ping; Weiss, Daniel J; Bulgarelli, Federica; Zinszer, Benjamin D; Aslin, Richard N
2016-10-01
Successful knowledge acquisition requires a cognitive system that is both sensitive to statistical information and able to distinguish among multiple structures (i.e., to detect pattern shifts and form distinct representations). Extensive behavioral evidence has highlighted the importance of cues to structural change, demonstrating how, without them, learners fail to detect pattern shifts and are biased in favor of early experience. Here, we seek a neural account of the mechanism underpinning this primacy effect in learning. During fMRI scanning, adult participants were presented with two artificial languages: a familiar language (L1) on which they had been pretrained followed by a novel language (L2). The languages were composed of the same syllable inventory organized according to unique statistical structures. In the absence of cues to the transition between languages, posttest familiarity judgments revealed that learners on average more accurately segmented words from the familiar language compared with the novel one. Univariate activation and functional connectivity analyses showed that participants with the strongest learning of L1 had decreased recruitment of fronto-subcortical and posterior parietal regions, in addition to a dissociation between downstream regions and early auditory cortex. Participants with a strong new language learning capacity (i.e., higher L2 scores) showed the opposite trend. Thus, we suggest that a bias toward neural efficiency, particularly as manifested by decreased sampling from the environment, accounts for the primacy effect in learning. Potential implications of this hypothesis are discussed, including the possibility that "inefficient" learning systems may be more sensitive to structural changes in a dynamic environment.
Visual statistical learning is not reliably modulated by selective attention to isolated events
Musz, Elizabeth; Weber, Matthew J.; Thompson-Schill, Sharon L.
2014-01-01
Recent studies of visual statistical learning (VSL) indicate that the visual system can automatically extract temporal and spatial relationships between objects. We report several attempts to replicate and extend earlier work (Turk-Browne et al., 2005) in which observers performed a cover task on one of two interleaved stimulus sets, resulting in learning of temporal relationships that occur in the attended stream, but not those present in the unattended stream. Across four experiments, we exposed observers to a similar or identical familiarization protocol, directing attention to one of two interleaved stimulus sets; afterward, we assessed VSL efficacy for both sets using either implicit response-time measures or explicit familiarity judgments. In line with prior work, we observe learning for the attended stimulus set. However, unlike previous reports, we also observe learning for the unattended stimulus set. When instructed to selectively attend to only one of the stimulus sets and ignore the other set, observers could extract temporal regularities for both sets. Our efforts to experimentally decrease this effect by changing the cover task (Experiment 1) or the complexity of the statistical regularities (Experiment 3) were unsuccessful. A fourth experiment using a different assessment of learning likewise failed to show an attentional effect. Simulations drawing random samples our first three experiments (n=64) confirm that the distribution of attentional effects in our sample closely approximates the null. We offer several potential explanations for our failure to replicate earlier findings, and discuss how our results suggest limiting conditions on the relevance of attention to VSL. PMID:25172196
NASA Astrophysics Data System (ADS)
Joshi, Deepti; St-Hilaire, André; Daigle, Anik; Ouarda, Taha B. M. J.
2013-04-01
SummaryThis study attempts to compare the performance of two statistical downscaling frameworks in downscaling hydrological indices (descriptive statistics) characterizing the low flow regimes of three rivers in Eastern Canada - Moisie, Romaine and Ouelle. The statistical models selected are Relevance Vector Machine (RVM), an implementation of Sparse Bayesian Learning, and the Automated Statistical Downscaling tool (ASD), an implementation of Multiple Linear Regression. Inputs to both frameworks involve climate variables significantly (α = 0.05) correlated with the indices. These variables were processed using Canonical Correlation Analysis and the resulting canonical variates scores were used as input to RVM to estimate the selected low flow indices. In ASD, the significantly correlated climate variables were subjected to backward stepwise predictor selection and the selected predictors were subsequently used to estimate the selected low flow indices using Multiple Linear Regression. With respect to the correlation between climate variables and the selected low flow indices, it was observed that all indices are influenced, primarily, by wind components (Vertical, Zonal and Meridonal) and humidity variables (Specific and Relative Humidity). The downscaling performance of the framework involving RVM was found to be better than ASD in terms of Relative Root Mean Square Error, Relative Mean Absolute Bias and Coefficient of Determination. In all cases, the former resulted in less variability of the performance indices between calibration and validation sets, implying better generalization ability than for the latter.
NASA Astrophysics Data System (ADS)
Abdullah, Rusli; Samah, Bahaman Abu; Bolong, Jusang; D'Silva, Jeffrey Lawrence; Shaffril, Hayrol Azril Mohamed
2014-09-01
Today, teaching and learning (T&L) using technology as tool is becoming more important especially in the field of statistics as a part of the subject matter in higher education system environment. Eventhough, there are many types of technology of statistical learnig tool (SLT) which can be used to support and enhance T&L environment, however, there is lack of a common standard knowledge management as a knowledge portal for guidance especially in relation to infrastructure requirement of SLT in servicing the community of user (CoU) such as educators, students and other parties who are interested in performing this technology as a tool for their T&L. Therefore, there is a need of a common standard infrastructure requirement of knowledge portal in helping CoU for managing of statistical knowledge in acquiring, storing, desseminating and applying of the statistical knowedge for their specific purposes. Futhermore, by having this infrastructure requirement of knowledge portal model of SLT as a guidance in promoting knowledge of best practise among the CoU, it can also enhance the quality and productivity of their work towards excellence of statistical knowledge application in education system environment.
Learning at a distance II. Statistical learning of non-adjacent dependencies in a non-human primate.
Newport, Elissa L; Hauser, Marc D; Spaepen, Geertrui; Aslin, Richard N
2004-09-01
In earlier work we have shown that adults, infants, and cotton-top tamarin monkeys are capable of computing the probability with which syllables occur in particular orders in rapidly presented streams of human speech, and of using these probabilities to group adjacent syllables into word-like units. We have also investigated adults' learning of regularities among elements that are not adjacent, and have found strong selectivities in their ability to learn various kinds of non-adjacent regularities. In the present paper we investigate the learning of these same non-adjacent regularities in tamarin monkeys, using the same materials and familiarization methods. Three types of languages were constructed. In one, words were formed by statistical regularities between non-adjacent syllables. Words contained predictable relations between syllables 1 and 3; syllable 2 varied. In a second type of language, words were formed by statistical regularities between non-adjacent segments. Words contained predictable relations between consonants; the vowels varied. In a third type of language, also formed by regularities between non-adjacent segments, words contained predictable relations between vowels; the consonants varied. Tamarin monkeys were exposed to these languages in the same fashion as adults (21 min of exposure to a continuous speech stream) and were then tested in a playback paradigm measuring spontaneous looking (no reinforcement). Adult subjects learned the second and third types of language easily, but failed to learn the first. However, tamarin monkeys showed a different pattern, learning the first and third type of languages but not the second. These differences held up over multiple replications, using different sounds instantiating each of the patterns. These results suggest differences among learners in the elementary units perceived in speech (syllables, consonants, and vowels) and/or the distance over which such units can be related, and therefore differences
NASA Astrophysics Data System (ADS)
Koparan, Timur; Güven, Bülent
2015-07-01
The point of this study is to define the effect of project-based learning approach on 8th Grade secondary-school students' statistical literacy levels for data representation. To achieve this goal, a test which consists of 12 open-ended questions in accordance with the views of experts was developed. Seventy 8th grade secondary-school students, 35 in the experimental group and 35 in the control group, took this test twice, one before the application and one after the application. All the raw scores were turned into linear points by using the Winsteps 3.72 modelling program that makes the Rasch analysis and t-tests, and an ANCOVA analysis was carried out with the linear points. Depending on the findings, it was concluded that the project-based learning approach increases students' level of statistical literacy for data representation. Students' levels of statistical literacy before and after the application were shown through the obtained person-item maps.
Kim, Jihoon; Grillo, Janice M; Ohno-Machado, Lucila
2011-01-01
Objective To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs. Methods From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures. Results The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM. Limitations The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction. Conclusion The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs. PMID:21672912
Simulation-Based Learning: The Learning-Forgetting-Relearning Process and Impact of Learning History
ERIC Educational Resources Information Center
Davidovitch, Lior; Parush, Avi; Shtub, Avy
2008-01-01
The results of empirical experiments evaluating the effectiveness and efficiency of the learning-forgetting-relearning process in a dynamic project management simulation environment are reported. Sixty-six graduate engineering students performed repetitive simulation-runs with a break period of several weeks between the runs. The students used a…
NASA Astrophysics Data System (ADS)
Appelhans, Tim; Mwangomo, Ephraim; Otte, Insa; Detsch, Florian; Nauss, Thomas; Hemp, Andreas; Ndyamkama, Jimmy
2015-04-01
This study introduces the set-up and characteristics of a meteorological station network on the southern slopes of Mt. Kilimanjaro, Tanzania. The set-up follows a hierarchical approach covering an elevational as well as a land-use disturbance gradient. The network consists of 52 basic stations measuring ambient air temperature and above ground air humidity and 11 precipitation measurement sites. We provide in depth descriptions of various machine learning and classical geo-statistical methods used to fill observation gaps and extend the spatial coverage of the network to a total of 60 research sites. Performance statistics for these methods indicate that the presented data sets provide reliable measurements of the meteorological reality at Mt. Kilimanjaro. These data provide an excellent basis for ecological studies and are also of great value for regional atmospheric numerical modelling studies for which such comprehensive in-situ validation observations are rare, especially in tropical regions of complex terrain.
Mitra, Jhimli; Shen, Kai-kai; Ghose, Soumya; Bourgeat, Pierrick; Fripp, Jurgen; Salvado, Olivier; Pannek, Kerstin; Taylor, D Jamie; Mathias, Jane L; Rose, Stephen
2016-04-01
Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which probe the connectivity of neural networks, show significant promise. We present a machine learning approach to classify TBI participants primarily with mild traumatic brain injury (mTBI) based on altered structural connectivity patterns derived through the network based statistical analysis of structural connectomes generated from TBI and age-matched control groups. In this approach, higher order diffusion models were used to map white matter connections between 116 cortical and subcortical regions. Tracts between these regions were generated using probabilistic tracking and mean fractional anisotropy (FA) measures along these connections were encoded in the connectivity matrices. Network-based statistical analysis of the connectivity matrices was performed to identify the network differences between a representative subset of the two groups. The affected network connections provided the feature vectors for principal component analysis and subsequent classification by random forest. The validity of the approach was tested using data acquired from a total of 179 TBI patients and 146 controls participants. The analysis revealed altered connectivity within a number of intra- and inter-hemispheric white matter pathways associated with DAI, in consensus with existing literature. A mean classification accuracy of 68.16%±1.81% and mean sensitivity of 80.0%±2.36% were achieved in correctly classifying the TBI patients evaluated on the subset of the participants that was not used for the statistical analysis, in a 10-fold cross-validation framework. These results highlight the potential for statistical machine learning approaches applied to structural connectomes to identify patients with diffusive axonal injury. PMID
ERIC Educational Resources Information Center
Mittelholtz, David J.; And Others
Differences in learning processes were studied in more versus less intellectually able undergraduate students. Thirty subjects were selected to represent a wide range of general and mathematical reasoning abilities, based on the following test scores: Necessary Arithmetic Operations and Vocabulary Test V2 from the Educational Testing Service ETS…
NASA Astrophysics Data System (ADS)
Conde, Miguel Ángel; García-Peñalvo, Francisco José; Casany, Marià José; Alier Forment, Marc
Learning processes are changing related to technological and sociological evolution, taking this in to account, a new learning strategy must be considered. Specifically what is needed is to give an effective step towards the eLearning 2.0 environments consolidation. This must imply the fusion of the advantages of the traditional LMS (Learning Management System) - more formative program control and planning oriented - with the social learning and the flexibility of the web 2.0 educative applications.
Using Statistical Process Control to Make Data-Based Clinical Decisions.
ERIC Educational Resources Information Center
Pfadt, Al; Wheeler, Donald J.
1995-01-01
Statistical process control (SPC), which employs simple statistical tools and problem-solving techniques such as histograms, control charts, flow charts, and Pareto charts to implement continual product improvement procedures, can be incorporated into human service organizations. Examples illustrate use of SPC procedures to analyze behavioral data…
Statistical Process Control Charts for Measuring and Monitoring Temporal Consistency of Ratings
ERIC Educational Resources Information Center
Omar, M. Hafidz
2010-01-01
Methods of statistical process control were briefly investigated in the field of educational measurement as early as 1999. However, only the use of a cumulative sum chart was explored. In this article other methods of statistical quality control are introduced and explored. In particular, methods in the form of Shewhart mean and standard deviation…
Developing Students' Thought Processes for Choosing Appropriate Statistical Methods
ERIC Educational Resources Information Center
Murray, James; Knowles, Elizabeth
2014-01-01
Students often struggle to select appropriate statistical tests when investigating research questions. The authors present a lesson study designed to make students' thought processes visible while considering this choice. The authors taught their students a way to organize knowledge about statistical tests and observed its impact in the…
Game Inspired Tool Support for e-Learning Processes
ERIC Educational Resources Information Center
Charles, Marie-Therese; Bustard, David; Black, Michaela
2009-01-01
Student engagement is crucial to the success of e-learning but is often difficult to achieve in practice. One significant factor is the quality of the learning content; also important, however, is the suitability of the process through which that material is studied. In recent years much research has been devoted to improving e-learning content…
Implementation of Process Oriented Guided Inquiry Learning (POGIL) in Engineering
ERIC Educational Resources Information Center
Douglas, Elliot P.; Chiu, Chu-Chuan
2013-01-01
This paper describes implementation and testing of an active learning, team-based pedagogical approach to instruction in engineering. This pedagogy has been termed Process Oriented Guided Inquiry Learning (POGIL), and is based upon the learning cycle model. Rather than sitting in traditional lectures, students work in teams to complete worksheets…
ERIC Educational Resources Information Center
Liu, Tzu-Chien
2010-01-01
Understanding and applying statistical concepts is essential in modern life. However, common statistical misconceptions limit the ability of students to understand statistical concepts. Although simulation-based computer assisted learning (CAL) is promising for use in students learning statistics, substantial improvement is still needed. For…
Saadati, Farzaneh; Ahmad Tarmizi, Rohani; Mohd Ayub, Ahmad Fauzi; Abu Bakar, Kamariah
2015-01-01
Because students' ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is 'value added' because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students' problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students. PMID:26132553
Saadati, Farzaneh; Ahmad Tarmizi, Rohani
2015-01-01
Because students’ ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is ‘value added’ because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students’ problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students. PMID:26132553
Saadati, Farzaneh; Ahmad Tarmizi, Rohani; Mohd Ayub, Ahmad Fauzi; Abu Bakar, Kamariah
2015-01-01
Because students' ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is 'value added' because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students' problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students.
ERIC Educational Resources Information Center
Neumann, David L.; Hood, Michelle
2009-01-01
A wiki was used as part of a blended learning approach to promote collaborative learning among students in a first year university statistics class. One group of students analysed a data set and communicated the results by jointly writing a practice report using a wiki. A second group analysed the same data but communicated the results in a…
ERIC Educational Resources Information Center
Endress, Ansgar D.; Mehler, Jacques
2009-01-01
Word-segmentation, that is, the extraction of words from fluent speech, is one of the first problems language learners have to master. It is generally believed that statistical processes, in particular those tracking "transitional probabilities" (TPs), are important to word-segmentation. However, there is evidence that word forms are stored in…
Modality-Constrained Statistical Learning of Tactile, Visual, and Auditory Sequences
ERIC Educational Resources Information Center
Conway, Christopher M.; Christiansen, Morten H.
2005-01-01
The authors investigated the extent to which touch, vision, and audition mediate the processing of statistical regularities within sequential input. Few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored. The current data reveal not only commonalities but also…
A Goal-Based Approach for Learning in Business Processes
NASA Astrophysics Data System (ADS)
Soffer, Pnina; Ghattas, Johny; Peleg, Mor
Organizations constantly strive to improve their business performance; hence they make business process redesign efforts. So far, redesign has mainly been a human task, which relies on human reasoning and creativity, although various analysis tools can support it by identifying improvement opportunities. This chapter proposes an automated approach for learning from accumulated experience and improving business processes over time. The approach ties together three aspects of business processes: goals, context, and actual paths. It proposes a learning cycle, including a learning phase, where the relevant context is identified and used for making improvements in the process model, and a runtime application phase, where the improved process model is applied at runtime and actual results are stored for the next learning cycle. According to our approach, a goal-oriented process model is essential for learning to improve process outcomes.
Interrupted Time Series Versus Statistical Process Control in Quality Improvement Projects.
Andersson Hagiwara, Magnus; Andersson Gäre, Boel; Elg, Mattias
2016-01-01
To measure the effect of quality improvement interventions, it is appropriate to use analysis methods that measure data over time. Examples of such methods include statistical process control analysis and interrupted time series with segmented regression analysis. This article compares the use of statistical process control analysis and interrupted time series with segmented regression analysis for evaluating the longitudinal effects of quality improvement interventions, using an example study on an evaluation of a computerized decision support system.
Statistics to the Rescue!: Using Data to Evaluate a Manufacturing Process
ERIC Educational Resources Information Center
Keithley, Michael G.
2009-01-01
The use of statistics and process controls is too often overlooked in educating students. This article describes an activity appropriate for high school students who have a background in material processing. It gives them a chance to advance their knowledge by determining whether or not a manufacturing process works well. The activity follows a…
Pulsipher, B.A.; Kuhn, W.L.
1987-02-01
Current planning for liquid high-level nuclear wastes existing in the US includes processing in a liquid-fed ceramic melter to incorporate it into a high-quality glass, and placement in a deep geologic repository. The nuclear waste vitrification process requires assurance of a quality product with little or no final inspection. Statistical process control (SPC) is a quantitative approach to one quality assurance aspect of vitrified nuclear waste. This method for monitoring and controlling a process in the presence of uncertainties provides a statistical basis for decisions concerning product quality improvement. Statistical process control is shown to be a feasible and beneficial tool to help the waste glass producers demonstrate that the vitrification process can be controlled sufficiently to produce an acceptable product. This quantitative aspect of quality assurance could be an effective means of establishing confidence in the claims to a quality product. 2 refs., 4 figs.
Nolan, Bernard T.; Fienen, Michael N.; Lorenz, David L.
2015-01-01
We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R2 and a version with R2 within one standard error of the maximum (the 1SE model). The former yielded CV training R2 values of 0.94–1.0. Cross-validation testing R2 values indicate predictive performance, and these were 0.22–0.39 for the maximum R2 models and 0.19–0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.
NASA Astrophysics Data System (ADS)
Nolan, Bernard T.; Fienen, Michael N.; Lorenz, David L.
2015-12-01
We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R2 and a version with R2 within one standard error of the maximum (the 1SE model). The former yielded CV training R2 values of 0.94-1.0. Cross-validation testing R2 values indicate predictive performance, and these were 0.22-0.39 for the maximum R2 models and 0.19-0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.
Feedback Processes in Multimedia Language Learning Software
ERIC Educational Resources Information Center
Kartal, Erdogan
2010-01-01
Feedback has been one of the important elements of learning and teaching theories and still pervades the literature and instructional models, especially computer and web-based ones. However, the mechanisms about feedback dominating the fundamentals of all the instructional models designed for self-learning have changed considerably with the…
LANGUAGE LEARNING, THE INDIVIDUAL AND THE PROCESS.
ERIC Educational Resources Information Center
NAJAM, EDWARD W.
THE PROCEEDINGS OF THE INDIANA-PURDUE FOREIGN LANGUAGE CONFERENCE ON LANGUAGE LEARNING ARE DIVIDED INTO THREE GENERAL CATEGORIES AND INTRODUCED BY DIEKHOFF'S SPEECH ADVOCATING TEACHER PARTICIPATION IN THE REVISION OF PROGRAM POLICY TO MEET CONTINUOUS SOCIAL CHANGE. IN THE FIRST SECTION, THE INTERRELATION OF PSYCHOLOGY AND LANGUAGE LEARNING, ARE…
Modeling Learning Processes in Lexical CALL.
ERIC Educational Resources Information Center
Goodfellow, Robin; Laurillard, Diana
1994-01-01
Studies the performance of a novice Spanish student using a Computer-assisted language learning (CALL) system designed for vocabulary enlargement. Results indicate that introspective evidence may be used to validate performance data within a theoretical framework that characterizes the learning approach as "surface" or "deep." (25 references)…
Sleep, off-line processing, and vocal learning.
Margoliash, Daniel; Schmidt, Marc F
2010-10-01
The study of song learning and the neural song system has provided an important comparative model system for the study of speech and language acquisition. We describe some recent advances in the bird song system, focusing on the role of off-line processing including sleep in processing sensory information and in guiding developmental song learning. These observations motivate a new model of the organization and role of the sensory memories in vocal learning. PMID:19906416
Which processes are involved in cognitive procedural learning?
Beaunieux, Hélène; Hubert, Valérie; Witkowski, Thomas; Pitel, Anne-Lise; Rossi, Sandrine; Danion, Jean-Marie; Desgranges, Béatrice; Eustache, Francis
2006-07-01
Procedural memory is characterised by a relative resistance to pathology, making its assessment of the utmost importance. However, few studies have looked at the cognitive processes involved in cognitive procedural learning. In an initial experiment, we studied the role of different cognitive functions in massed cognitive procedural learning. Our results confirmed the existence of three separate learning phases and, for the first time, demonstrated the involvement of episodic memory and executive functions in the first learning phase. In a second experiment, we studied the effect of distributed learning conditions on the dynamics of procedural learning. This second study confirmed our results but showed that these conditions slow down the process of cognitive procedural learning. Our overall findings call into question the status of functionally autonomous memory system that is currently allotted to procedural memory, and suggest that the role of nonprocedural cognitive components should be taken into account in patient rehabilitation. PMID:16754239
Students' Learning Activities While Studying Biological Process Diagrams
NASA Astrophysics Data System (ADS)
Kragten, Marco; Admiraal, Wilfried; Rijlaarsdam, Gert
2015-08-01
Process diagrams describe how a system functions (e.g. photosynthesis) and are an important type of representation in Biology education. In the present study, we examined students' learning activities while studying process diagrams, related to their resulting comprehension of these diagrams. Each student completed three learning tasks. Verbal data and eye-tracking data were collected as indications of students' learning activities. For the verbal data, we applied a fine-grained coding scheme to optimally describe students' learning activities. For the eye-tracking data, we used fixation time and transitions between areas of interest in the process diagrams as indices of learning activities. Various learning activities while studying process diagrams were found that distinguished between more and less successful students. Results showed that between-student variance in comprehension score was highly predicted by meaning making of the process arrows (80%) and fixation time in the main area (65%). Students employed successful learning activities consistently across learning tasks. Furthermore, compared to unsuccessful students, successful students used a more coherent approach of interrelated learning activities for comprehending process diagrams.
Attentional Processes in Children's Learning. Appendix A: Project Papers.
ERIC Educational Resources Information Center
White, Sheldon H.
This appendix includes seven papers which focus on various aspects of the learning processes of children ages 5-7: (1) S. Thompson, "Transitions to concrete operations: A survey of Piaget's writings" (in outline form); (2) S. H. White, "Changes in learning processes in the late preschool years," an examination of cross-cultural evidence of…
Epistemological Beliefs of Community College Students and Their Learning Processes.
ERIC Educational Resources Information Center
Schreiber, James B.; Shinn, David
2003-01-01
Argues that epistemological beliefs of community college students can impact their learning processes. Explains that epistemological beliefs interact with other knowledge structures. Reports on a study that explores the association between students' epistemological beliefs and learning processes. Suggests there are relationships between Fixed…
Enhancing the Teaching-Learning Process: A Knowledge Management Approach
ERIC Educational Resources Information Center
Bhusry, Mamta; Ranjan, Jayanthi
2012-01-01
Purpose: The purpose of this paper is to emphasize the need for knowledge management (KM) in the teaching-learning process in technical educational institutions (TEIs) in India, and to assert the impact of information technology (IT) based KM intervention in the teaching-learning process. Design/methodology/approach: The approach of the paper is…
Electrical Storm Simulation to Improve the Learning Physics Process
ERIC Educational Resources Information Center
Martínez Muñoz, Miriam; Jiménez Rodríguez, María Lourdes; Gutiérrez de Mesa, José Antonio
2013-01-01
This work is part of a research project whose main objective is to understand the impact that the use of Information and Communication Technology (ICT) has on the teaching and learning process on the subject of Physics. We will show that, with the use of a storm simulator, physics students improve their learning process on one hand they understand…
The Adoption Process of Corporate E-Learning in Italy
ERIC Educational Resources Information Center
Comacchio, Anna; Scapolan, AnnaChiara
2004-01-01
The diffusion process of e-learning has been, in recent years, at the centre of several studies. These researches focused mainly on the USA case, where there has been an exponential adoption both in the public and private sectors. From this perspective the paper would give a contribution to understand the diffusion process of e-learning in a…
Students' Learning Activities While Studying Biological Process Diagrams
ERIC Educational Resources Information Center
Kragten, Marco; Admiraal, Wilfried; Rijlaarsdam, Gert
2015-01-01
Process diagrams describe how a system functions (e.g. photosynthesis) and are an important type of representation in Biology education. In the present study, we examined students' learning activities while studying process diagrams, related to their resulting comprehension of these diagrams. Each student completed three learning tasks. Verbal…
Rapid e-Learning Tools Selection Process for Cognitive and Psychomotor Learning Objectives
ERIC Educational Resources Information Center
Ku, David Tawei; Huang, Yung-Hsin
2012-01-01
This study developed a decision making process for the selection of rapid e-learning tools that could match different learning domains. With the development of the Internet, the speed of information updates has become faster than ever. E-learning has rapidly become the mainstream for corporate training and academic instruction. In order to reduce…
A Concept Transformation Learning Model for Architectural Design Learning Process
ERIC Educational Resources Information Center
Wu, Yun-Wu; Weng, Kuo-Hua; Young, Li-Ming
2016-01-01
Generally, in the foundation course of architectural design, much emphasis is placed on teaching of the basic design skills without focusing on teaching students to apply the basic design concepts in their architectural designs or promoting students' own creativity. Therefore, this study aims to propose a concept transformation learning model to…
Assessing a learning process with functional ANOVA estimators of EEG power spectral densities.
Gutiérrez, David; Ramírez-Moreno, Mauricio A
2016-04-01
We propose to assess the process of learning a task using electroencephalographic (EEG) measurements. In particular, we quantify changes in brain activity associated to the progression of the learning experience through the functional analysis-of-variances (FANOVA) estimators of the EEG power spectral density (PSD). Such functional estimators provide a sense of the effect of training in the EEG dynamics. For that purpose, we implemented an experiment to monitor the process of learning to type using the Colemak keyboard layout during a twelve-lessons training. Hence, our aim is to identify statistically significant changes in PSD of various EEG rhythms at different stages and difficulty levels of the learning process. Those changes are taken into account only when a probabilistic measure of the cognitive state ensures the high engagement of the volunteer to the training. Based on this, a series of statistical tests are performed in order to determine the personalized frequencies and sensors at which changes in PSD occur, then the FANOVA estimates are computed and analyzed. Our experimental results showed a significant decrease in the power of [Formula: see text] and [Formula: see text] rhythms for ten volunteers during the learning process, and such decrease happens regardless of the difficulty of the lesson. These results are in agreement with previous reports of changes in PSD being associated to feature binding and memory encoding.
Project T.E.A.M. (Technical Education Advancement Modules). Advanced Statistical Process Control.
ERIC Educational Resources Information Center
Dunlap, Dale
This instructional guide, one of a series developed by the Technical Education Advancement Modules (TEAM) project, is a 20-hour advanced statistical process control (SPC) and quality improvement course designed to develop the following competencies: (1) understanding quality systems; (2) knowing the process; (3) solving quality problems; and (4)…
ERIC Educational Resources Information Center
Billings, Paul H.
This instructional guide, one of a series developed by the Technical Education Advancement Modules (TEAM) project, is a 6-hour introductory module on statistical process control (SPC), designed to develop competencies in the following skill areas: (1) identification of the three classes of SPC use; (2) understanding a process and how it works; (3)…
The words children hear: Picture books and the statistics for language learning
Montag, Jessica L.; Jones, Michael N.; Smith, Linda B.
2015-01-01
Young children learn language from the speech they hear. Previous work suggests that the statistical diversity of words and of linguistic contexts is associated with better language outcomes. One potential source of lexical diversity is the text of picture books that caregivers read aloud to children. Many parents begin reading to their children shortly after birth, so this is potentially an important source of linguistic input for many children. We constructed a corpus of 100 children’s picture books and compared word type and token counts to a matched sample of child-directed speech. Overall, the picture books contained more unique word types than the child-directed speech. Further, individual picture books generally contained more unique word types than length-matched, child-directed conversations. The text of picture books may be an important source of vocabulary for young children, and these findings suggest a mechanism that underlies the language benefits associated with reading to children. PMID:26243292
The Words Children Hear: Picture Books and the Statistics for Language Learning.
Montag, Jessica L; Jones, Michael N; Smith, Linda B
2015-09-01
Young children learn language from the speech they hear. Previous work suggests that greater statistical diversity of words and of linguistic contexts is associated with better language outcomes. One potential source of lexical diversity is the text of picture books that caregivers read aloud to children. Many parents begin reading to their children shortly after birth, so this is potentially an important source of linguistic input for many children. We constructed a corpus of 100 children's picture books and compared word type and token counts in that sample and a matched sample of child-directed speech. Overall, the picture books contained more unique word types than the child-directed speech. Further, individual picture books generally contained more unique word types than length-matched, child-directed conversations. The text of picture books may be an important source of vocabulary for young children, and these findings suggest a mechanism that underlies the language benefits associated with reading to children.
Considerations for implementing an organizational lessons learned process.
Fosshage, Erik D
2013-05-01
This report examines the lessons learned process by a review of the literature in a variety of disciplines, and is intended as a guidepost for organizations that are considering the implementation of their own closed-loop learning process. Lessons learned definitions are provided within the broader context of knowledge management and the framework of a learning organization. Shortcomings of existing practices are summarized in an attempt to identify common pitfalls that can be avoided by organizations with fledgling experiences of their own. Lessons learned are then examined through a dual construct of both process and mechanism, with emphasis on integrating into organizational processes and promoting lesson reuse through data attributes that contribute toward changed behaviors. The report concludes with recommended steps for follow-on efforts.
Statistical error in simulations of Poisson processes: Example of diffusion in solids
NASA Astrophysics Data System (ADS)
Nilsson, Johan O.; Leetmaa, Mikael; Vekilova, Olga Yu.; Simak, Sergei I.; Skorodumova, Natalia V.
2016-08-01
Simulations of diffusion in solids often produce poor statistics of diffusion events. We present an analytical expression for the statistical error in ion conductivity obtained in such simulations. The error expression is not restricted to any computational method in particular, but valid in the context of simulation of Poisson processes in general. This analytical error expression is verified numerically for the case of Gd-doped ceria by running a large number of kinetic Monte Carlo calculations.
Empathy and feedback processing in active and observational learning.
Rak, Natalia; Bellebaum, Christian; Thoma, Patrizia
2013-12-01
The feedback-related negativity (FRN) and the P300 have been related to the processing of one's own and other individuals' feedback during both active and observational learning. The aim of the present study was to elucidate the role of trait-empathic responding with regard to the modulation of the neural correlates of observational learning in particular. Thirty-four healthy participants completed an active and an observational learning task. On both tasks, the participants' aim was to maximize their monetary gain by choosing from two stimuli the one that showed the higher probability of reward. Participants gained insight into the stimulus-reward contingencies according to monetary feedback presented after they had made an active choice or by observing the choices of a virtual partner. Participants showed a general improvement in learning performance on both learning tasks. P200, FRN, and P300 amplitudes were larger during active, as compared with observational, learning. Furthermore, nonreward elicited a significantly more negative FRN than did reward in the active learning task, while only a trend was observed for observational learning. Distinct subcomponents of trait cognitive empathy were related to poorer performance and smaller P300 amplitudes for observational learning only. Taken together, both the learning performance and event-related potentials during observational learning are affected by different aspects of trait cognitive empathy, and certain types of observational learning may actually be disrupted by a higher tendency to understand and adopt other people's perspectives.
Statistical signatures of structural organization: The case of long memory in renewal processes
NASA Astrophysics Data System (ADS)
Marzen, Sarah E.; Crutchfield, James P.
2016-04-01
Identifying and quantifying memory are often critical steps in developing a mechanistic understanding of stochastic processes. These are particularly challenging and necessary when exploring processes that exhibit long-range correlations. The most common signatures employed rely on second-order temporal statistics and lead, for example, to identifying long memory in processes with power-law autocorrelation function and Hurst exponent greater than 1/2. However, most stochastic processes hide their memory in higher-order temporal correlations. Information measures-specifically, divergences in the mutual information between a process' past and future (excess entropy) and minimal predictive memory stored in a process' causal states (statistical complexity)-provide a different way to identify long memory in processes with higher-order temporal correlations. However, there are no ergodic stationary processes with infinite excess entropy for which information measures have been compared to autocorrelation functions and Hurst exponents. Here, we show that fractal renewal processes-those with interevent distribution tails ∝t-α-exhibit long memory via a phase transition at α = 1. Excess entropy diverges only there and statistical complexity diverges there and for all α < 1. When these processes do have power-law autocorrelation function and Hurst exponent greater than 1/2, they do not have divergent excess entropy. This analysis breaks the intuitive association between these different quantifications of memory. We hope that the methods used here, based on causal states, provide some guide as to how to construct and analyze other long memory processes.
Pearce, Marcus T; Ruiz, María Herrojo; Kapasi, Selina; Wiggins, Geraint A; Bhattacharya, Joydeep
2010-03-01
The ability to anticipate forthcoming events has clear evolutionary advantages, and predictive successes or failures often entail significant psychological and physiological consequences. In music perception, the confirmation and violation of expectations are critical to the communication of emotion and aesthetic effects of a composition. Neuroscientific research on musical expectations has focused on harmony. Although harmony is important in Western tonal styles, other musical traditions, emphasizing pitch and melody, have been rather neglected. In this study, we investigated melodic pitch expectations elicited by ecologically valid musical stimuli by drawing together computational, behavioural, and electrophysiological evidence. Unlike rule-based models, our computational model acquires knowledge through unsupervised statistical learning of sequential structure in music and uses this knowledge to estimate the conditional probability (and information content) of musical notes. Unlike previous behavioural paradigms that interrupt a stimulus, we devised a new paradigm for studying auditory expectation without compromising ecological validity. A strong negative correlation was found between the probability of notes predicted by our model and the subjectively perceived degree of expectedness. Our electrophysiological results showed that low-probability notes, as compared to high-probability notes, elicited a larger (i) negative ERP component at a late time period (400-450 ms), (ii) beta band (14-30 Hz) oscillation over the parietal lobe, and (iii) long-range phase synchronization between multiple brain regions. Altogether, the study demonstrated that statistical learning produces information-theoretic descriptions of musical notes that are proportional to their perceived expectedness and are associated with characteristic patterns of neural activity.
Statistical learning as an individual ability: Theoretical perspectives and empirical evidence
Siegelman, Noam; Frost, Ram
2015-01-01
Although the power of statistical learning (SL) in explaining a wide range of linguistic functions is gaining increasing support, relatively little research has focused on this theoretical construct from the perspective of individual differences. However, to be able to reliably link individual differences in a given ability such as language learning to individual differences in SL, three critical theoretical questions should be posed: Is SL a componential or unified ability? Is it nested within other general cognitive abilities? Is it a stable capacity of an individual? Following an initial mapping sentence outlining the possible dimensions of SL, we employed a battery of SL tasks in the visual and auditory modalities, using verbal and non-verbal stimuli, with adjacent and non-adjacent contingencies. SL tasks were administered along with general cognitive tasks in a within-subject design at two time points to explore our theoretical questions. We found that SL, as measured by some tasks, is a stable and reliable capacity of an individual. Moreover, we found SL to be independent of general cognitive abilities such as intelligence or working memory. However, SL is not a unified capacity, so that individual sensitivity to conditional probabilities is not uniform across modalities and stimuli. PMID:25821343
NASA Astrophysics Data System (ADS)
Suhir, E.
2014-05-01
The well known and widely used experimental reliability "passport" of a mass manufactured electronic or a photonic product — the bathtub curve — reflects the combined contribution of the statistics-related and reliability-physics (physics-of-failure)-related processes. When time progresses, the first process results in a decreasing failure rate, while the second process associated with the material aging and degradation leads to an increased failure rate. An attempt has been made in this analysis to assess the level of the reliability physics-related aging process from the available bathtub curve (diagram). It is assumed that the products of interest underwent the burn-in testing and therefore the obtained bathtub curve does not contain the infant mortality portion. It has been also assumed that the two random processes in question are statistically independent, and that the failure rate of the physical process can be obtained by deducting the theoretically assessed statistical failure rate from the bathtub curve ordinates. In the carried out numerical example, the Raleigh distribution for the statistical failure rate was used, for the sake of a relatively simple illustration. The developed methodology can be used in reliability physics evaluations, when there is a need to better understand the roles of the statistics-related and reliability-physics-related irreversible random processes in reliability evaluations. The future work should include investigations on how powerful and flexible methods and approaches of the statistical mechanics can be effectively employed, in addition to reliability physics techniques, to model the operational reliability of electronic and photonic products.
Semi-supervised learning of statistical models for natural language understanding.
Zhou, Deyu; He, Yulan
2014-01-01
Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in F-measure.
Research on Student Learning of Upper-Level Thermal and Statistical Physics
NASA Astrophysics Data System (ADS)
Thompson, John
2011-03-01
Within the last decade, physics education researchers have begun to extend the tools and methods used at the introductory level to conduct systematic investigations of student learning of thermal and statistical physics in the upper division. Most research in thermodynamics has focused on student ideas about the first and second laws and the associated concepts (e.g., work, heat, entropy). Several studies yield insights about broader ideas, such as state functions. Research in statistical physics has focused on the concepts underlying multiplicity and related ideas in probability. Research has identified a number of conceptual difficulties with varied degrees of persistence, some of which are consistent with findings at the introductory level. Some investigations further probe connections between physics and relevant mathematics concepts in these areas, including student interpretation of canonical representations such as pressure-volume (P-V) diagrams. Results from research are guiding the development of curricular materials in order to address several known difficulties. Supported in part by NSF Grants DUE-0817282 and DUE-0837214.
A statistical learning strategy for closed-loop control of fluid flows
NASA Astrophysics Data System (ADS)
Guéniat, Florimond; Mathelin, Lionel; Hussaini, M. Yousuff
2016-04-01
This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz'63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.
NASA Astrophysics Data System (ADS)
Goetz, Jason; Brenning, Alexander; Petschko, Helene; Leopold, Philip
2015-04-01
With so many techniques now available for landslide susceptibility modelling, it can be challenging to decide on which technique to apply. Generally speaking, the criteria for model selection should be tied closely to end users' purpose, which could be spatial prediction, spatial analysis or both. In our research, we focus on comparing the spatial predictive abilities of landslide susceptibility models. We illustrate how spatial cross-validation, a statistical approach for assessing spatial prediction performance, can be applied with the area under the receiver operating characteristic curve (AUROC) as a prediction measure for model comparison. Several machine learning and statistical techniques are evaluated for prediction in Lower Austria: support vector machine, random forest, bundling with penalized linear discriminant analysis, logistic regression, weights of evidence, and the generalized additive model. In addition to predictive performance, the importance of predictor variables in each model was estimated using spatial cross-validation by calculating the change in AUROC performance when variables are randomly permuted. The susceptibility modelling techniques were tested in three areas of interest in Lower Austria, which have unique geologic conditions associated with landslide occurrence. Overall, we found for the majority of comparisons that there were little practical or even statistically significant differences in AUROCs. That is the models' prediction performances were very similar. Therefore, in addition to prediction, the ability to interpret models for spatial analysis and the qualitative qualities of the prediction surface (map) are considered and discussed. The measure of variable importance provided some insight into the model behaviour for prediction, in particular for "black-box" models. However, there were no clear patterns in all areas of interest to why certain variables were given more importance over others.
ERIC Educational Resources Information Center
Rodriguez, Clemente; Gutierrez-Perez, Jose; Pozo, Teresa
2010-01-01
Introduction: This research seeks to determine the influence exercised by a set of presage and process variables (students' pre-existing opinion towards statistics, their dedication to mastery of statistics content, assessment of the teaching materials, and the teacher's effort in the teaching of statistics) in students' resolution of activities…
NASA Technical Reports Server (NTRS)
Hutchens, Dale E.; Doan, Patrick A.; Boothe, Richard E.
1997-01-01
Bonding labs at both MSFC and the northern Utah production plant prepare bond test specimens which simulate or witness the production of NASA's Reusable Solid Rocket Motor (RSRM). The current process for preparing the bonding surfaces employs 1,1,1-trichloroethane vapor degreasing, which simulates the current RSRM process. Government regulations (e.g., the 1990 Amendments to the Clean Air Act) have mandated a production phase-out of a number of ozone depleting compounds (ODC) including 1,1,1-trichloroethane. In order to comply with these regulations, the RSRM Program is qualifying a spray-in-air (SIA) precision cleaning process using Brulin 1990, an aqueous blend of surfactants. Accordingly, surface preparation prior to bonding process simulation test specimens must reflect the new production cleaning process. The Bonding Lab Statistical Process Control (SPC) program monitors the progress of the lab and its capabilities, as well as certifies the bonding technicians, by periodically preparing D6AC steel tensile adhesion panels with EA-91 3NA epoxy adhesive using a standardized process. SPC methods are then used to ensure the process is statistically in control, thus producing reliable data for bonding studies, and identify any problems which might develop. Since the specimen cleaning process is being changed, new SPC limits must be established. This report summarizes side-by-side testing of D6AC steel tensile adhesion witness panels and tapered double cantilevered beams (TDCBs) using both the current baseline vapor degreasing process and a lab-scale spray-in-air process. A Proceco 26 inches Typhoon dishwasher cleaned both tensile adhesion witness panels and TDCBs in a process which simulates the new production process. The tests were performed six times during 1995, subsequent statistical analysis of the data established new upper control limits (UCL) and lower control limits (LCL). The data also demonstrated that the new process was equivalent to the vapor
ERIC Educational Resources Information Center
Guler, Cetin; Altun, Arif
2010-01-01
Learning objects (LOs) can be defined as resources that are reusable, digital with the aim of fulfilling learning objectives (or expectations). Educators, both at the individual and institutional levels, are cautioned about the fact that LOs are to be processed through a proper development process. Who should be involved in the LO development…
ERIC Educational Resources Information Center
Place, Nancy A.; Coskie, Tracy L.
2006-01-01
Using a communities-of-practice framework, this qualitative study investigated what eight teachers learned about literacy teaching and learning through participation in the National Board for Professional Teaching Standards certification process. This article presents two selected teacher cases which suggest that the National Board process created…
A Measurable Model of the Creative Process in the Context of a Learning Process
ERIC Educational Resources Information Center
Ma, Min; Van Oystaeyen, Fred
2016-01-01
The authors' aim was to arrive at a measurable model of the creative process by putting creativity in the context of a learning process. The authors aimed to provide a rather detailed description of how creative thinking fits in a general description of the learning process without trying to go into an analysis of a biological description of the…
Spatiotopic perceptual learning mediated by retinotopic processing and attentional remapping.
Zhang, En; Zhang, Gong-Liang; Li, Wu
2013-12-01
Visual processing takes place in both retinotopic and spatiotopic frames of reference. Whereas visual perceptual learning is usually specific to the trained retinotopic location, our recent study has shown spatiotopic specificity of learning in motion direction discrimination. To explore the mechanisms underlying spatiotopic processing and learning, and to examine whether similar mechanisms also exist in visual form processing, we trained human subjects to discriminate an orientation difference between two successively displayed stimuli, with a gaze shift in between to manipulate their positional relation in the spatiotopic frame of reference without changing their retinal locations. Training resulted in better orientation discriminability for the trained than for the untrained spatial relation of the two stimuli. This learning-induced spatiotopic preference was seen only at the trained retinal location and orientation, suggesting experience-dependent spatiotopic form processing directly based on a retinotopic map. Moreover, a similar but weaker learning-induced spatiotopic preference was still present even if the first stimulus was rendered irrelevant to the orientation discrimination task by having the subjects judge the orientation of the second stimulus relative to its mean orientation in a block of trials. However, if the first stimulus was absent, and thus no attention was captured before the gaze shift, the learning produced no significant spatiotopic preference, suggesting an important role of attentional remapping in spatiotopic processing and learning. Taken together, our results suggest that spatiotopic visual representation can be mediated by interactions between retinotopic processing and attentional remapping, and can be modified by perceptual training.
Powerful Practices in Digital Learning Processes
ERIC Educational Resources Information Center
Sørensen, Birgitte Holm; Levinsen, Karin Tweddell
2015-01-01
The present paper is based on two empirical research studies. The "Netbook 1:1" project (2009-2012), funded by the municipality of Gentofte and Microsoft Denmark, is complete, while "Students' digital production and students as learning designers" (2013-2015), funded by the Danish Ministry of Education, is ongoing. Both…
Lessons Learned from the Collaborative Writing Process
ERIC Educational Resources Information Center
Bhavsar, Victoria; Ahn, Ruth
2013-01-01
We reflect on how to implement the instrumental aspect of collaborative writing in such a way that the developmental aspect of collaborative writing is maximally fostered, based on conditions necessary for socially constructed learning. We discuss four instrumental strategies that bolster mutual ownership of the writing and protect the social…
Professional Learning Is a Lifelong Process
ERIC Educational Resources Information Center
Blanton, Patricia
2009-01-01
At a recent conference sponsored by the National Science Teachers Association on Professional Learning Communities in Science, I was reminded how crucial it is for teachers to continually examine their practice. As one new to teaching physics, you may be overwhelmed by the task of helping your students develop accurate understanding of the…
Expert Knowledge, Distinctiveness, and Levels of Processing in Language Learning
ERIC Educational Resources Information Center
Bird, Steve
2012-01-01
The foreign language vocabulary learning research literature often attributes strong mnemonic potency to the cognitive processing of meaning when learning words. Routinely cited as support for this idea are experiments by Craik and Tulving (C&T) demonstrating superior recognition and recall of studied words following semantic tasks ("deep"…
Beyond Chalk and Talk: Engaging Students in the Learning Process.
ERIC Educational Resources Information Center
Evans, Ruby
Teaching and learning in the traditional classroom continues to evolve in the presence of technological innovation. This paper highlights basic strategies in which the traditional classroom can be modified to involve students more actively in the teaching and learning process. One of the strategies outlined in this paper includes the incorporation…
The Circle of Learning: Individual and Group Processes.
ERIC Educational Resources Information Center
Chang, Ernest; Simpson, Don
1997-01-01
A paradigm is presented for modeling the processes found in individual and group learning. Using combinations of dimensions of learner activities (by oneself or with peers) and orientation (toward the person or toward the group as the focus), four quadrants of activity-orientation learning space are derived. (SLD)
Information Processing, Knowledge Acquisition and Learning: Developmental Perspectives.
ERIC Educational Resources Information Center
Hoyer, W. J.
1980-01-01
Several different conceptions of the relationship between learning and development are considered in this article. It is argued that dialectical and ecological developmental orientations might provide a useful basis for synthesizing the contrasting frameworks of the operant, information processing, learning theory, and knowledge acquisition…
The Self-Directed Learning Process of Older, Rural Adults
ERIC Educational Resources Information Center
Roberson, Donald N., Jr.; Merriam, Sharan B.
2005-01-01
Medical advances and lifestyle changes have resulted in older adults living longer and healthier lives. Nevertheless, older adulthood, as other life stages, requires change in work, family, and health. Self-directed learning (SDL) is one way of negotiating these transitions. The purpose of this study was to understand this process of learning.…
Reframing Teachers' Intercultural Learning as an Emotional Process
ERIC Educational Resources Information Center
Jokikokko, Katri
2016-01-01
The importance of emotions in the process of intercultural learning has been recognised, but the topic has not been extensively theorised. This theoretical review article synthesises the research literature on emotions in the context of teachers' intercultural learning. The article argues that emotions are a vital part of any change, and thus play…
NASA Astrophysics Data System (ADS)
Papageorge, Michael; Sutton, Jeffrey A.
2016-08-01
In this manuscript, we investigate the statistical convergence of turbulent flow statistics from finite-record-length time-series measurements. Analytical solutions of the convergence rate of the mean, variance, and autocorrelation function as a function of record length are presented based on using mean-squared error analysis and the consideration of turbulent flows as random processes. Experimental assessment of the statistical convergence theory is presented using 20-kHz laser Rayleigh scattering measurements of a conserved scalar (ξ) in a turbulent free jet. Excellent agreement between experiments and theory is noted, providing validation of the statistical convergence analysis. To the authors' knowledge, this is the first reported assessment and verification of statistical convergence theory as applied to turbulent flows. The verified theory provides a practitioner a method for a priori determining the necessary temporal record length for a desired statistical accuracy or conversely, accurately estimating the uncertainty of a measurement for a given temporal record length. Furthermore, we propose a new hybrid "multi-burst" data processing scheme based on combined independent ensemble and time-series statistics targeted for shorter-duration time-series measurements. The new methodology is based on taking the ensemble mean of derived statistical moments from many individual finite-duration time-series measurements. This approach is used to systematically converge toward the "expected" value of any statistical moment at a rate of √ M, where M is the number of individual time-series measurements. The proposed multi-burst methodology is assessed experimentally, and excellent agreement between measurements and theory is observed. A key outcome of the implementation of the multi-burst processing method is noted in the estimation of the autocorrelation function. Specifically, an unbiased estimator of the autocorrelation function can be used with much less
Dipnall, Joanna F.
2016-01-01
Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and
Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali
2012-01-01
Objective: Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. Methods: A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Results: Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. Conclusion: These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction. PMID:24644468
ERIC Educational Resources Information Center
Jarman, Jay
2011-01-01
This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms,…
Vicarious neural processing of outcomes during observational learning.
Monfardini, Elisabetta; Gazzola, Valeria; Boussaoud, Driss; Brovelli, Andrea; Keysers, Christian; Wicker, Bruno
2013-01-01
Learning what behaviour is appropriate in a specific context by observing the actions of others and their outcomes is a key constituent of human cognition, because it saves time and energy and reduces exposure to potentially dangerous situations. Observational learning of associative rules relies on the ability to map the actions of others onto our own, process outcomes, and combine these sources of information. Here, we combined newly developed experimental tasks and functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms that govern such observational learning. Results show that the neural systems involved in individual trial-and-error learning and in action observation and execution both participate in observational learning. In addition, we identified brain areas that specifically activate for others' incorrect outcomes during learning in the posterior medial frontal cortex (pMFC), the anterior insula and the posterior superior temporal sulcus (pSTS).
Learning Processes and Background Characteristics as Predictors of Tertiary Grades.
ERIC Educational Resources Information Center
Watkins, David
1986-01-01
Learning processes, as measured by the Approaches to Studying Inventory, contributed to the prediction of freshman grades of 181 Australians. Other measures, such as college entrance examinations, locus of control, and student background characteristics, had less predictive ability. (GDC)
ERIC Educational Resources Information Center
Karimi, Hamid; O'Brian, Sue; Onslow, Mark; Jones, Mark; Menzies, Ross; Packman, Ann
2013-01-01
Purpose: Stuttering varies between and within speaking situations. In this study, the authors used statistical process control charts with 10 case studies to investigate variability of stuttering frequency. Method: Participants were 10 adults who stutter. The authors counted the percentage of syllables stuttered (%SS) for segments of their speech…
ERIC Educational Resources Information Center
Matthews, Tansy E.
2009-01-01
This article describes the development of the Virtual Library of Virginia (VIVA). The VIVA statistics-processing system remains a work in progress. Member libraries will benefit from the ability to obtain the actual data from the VIVA site, rather than just the summaries, so a project to make these data available is currently being planned. The…
ERIC Educational Resources Information Center
Smith, Toni M.; Hjalmarson, Margret A.
2013-01-01
The purpose of this study is to examine prospective mathematics specialists' engagement in an instructional sequence designed to elicit and develop their understandings of random processes. The study was conducted with two different sections of a probability and statistics course for K-8 teachers. Thirty-two teachers participated. Video analyses…
ERIC Educational Resources Information Center
Hantula, Donald A.
1995-01-01
Clinical applications of statistical process control (SPC) in human service organizations are considered. SPC is seen as providing a standard set of criteria that serves as a common interface for data-based decision making, which may bring decision making under the control of established contingencies rather than the immediate contingencies of…
NASA Astrophysics Data System (ADS)
Goetz, J. N.; Brenning, A.; Petschko, H.; Leopold, P.
2015-08-01
Statistical and now machine learning prediction methods have been gaining popularity in the field of landslide susceptibility modeling. Particularly, these data driven approaches show promise when tackling the challenge of mapping landslide prone areas for large regions, which may not have sufficient geotechnical data to conduct physically-based methods. Currently, there is no best method for empirical susceptibility modeling. Therefore, this study presents a comparison of traditional statistical and novel machine learning models applied for regional scale landslide susceptibility modeling. These methods were evaluated by spatial k-fold cross-validation estimation of the predictive performance, assessment of variable importance for gaining insights into model behavior and by the appearance of the prediction (i.e. susceptibility) map. The modeling techniques applied were logistic regression (GLM), generalized additive models (GAM), weights of evidence (WOE), the support vector machine (SVM), random forest classification (RF), and bootstrap aggregated classification trees (bundling) with penalized discriminant analysis (BPLDA). These modeling methods were tested for three areas in the province of Lower Austria, Austria. The areas are characterized by different geological and morphological settings. Random forest and bundling classification techniques had the overall best predictive performances. However, the performances of all modeling techniques were for the majority not significantly different from each other; depending on the areas of interest, the overall median estimated area under the receiver operating characteristic curve (AUROC) differences ranged from 2.9 to 8.9 percentage points. The overall median estimated true positive rate (TPR) measured at a 10% false positive rate (FPR) differences ranged from 11 to 15pp. The relative importance of each predictor was generally different between the modeling methods. However, slope angle, surface roughness and plan
Motivation within the Information Processing Model of Foreign Language Learning
ERIC Educational Resources Information Center
Manolopoulou-Sergi, Eleni
2004-01-01
The present article highlights the importance of the motivational construct for the foreign language learning (FLL) process. More specifically, in the present article it is argued that motivation is likely to play a significant role at all three stages of the FLL process as they are discussed within the information processing model of FLL, namely,…
NASA Astrophysics Data System (ADS)
Graham, Daniel J.; Friedenberg, Jay D.; Rockmore, Daniel N.
2009-02-01
An emerging body of research suggests that artists consistently seek modes of representation that are efficiently processed by the human visual system, and that these shared properties could leave statistical signatures. In earlier work, we showed evidence that perceived similarity of representational art could be predicted using intensity statistics to which the early visual system is attuned, though semantic content was also found to be an important factor. Here we report two studies that examine the visual perception of similarity. We test a collection of non-representational art, which we argue possesses useful statistical and semantic properties, in terms of the relationship between image statistics and basic perceptual responses. We find two simple statistics-both expressed as single values-that predict nearly a third of the overall variance in similarity judgments of abstract art. An efficient visual system could make a quick and reasonable guess as to the relationship of a given image to others (i.e., its context) by extracting these basic statistics early in the visual stream, and this may hold for natural scenes as well as art. But a major component of many types of art is representational content. In a second study, we present findings related to efficient representation of natural scene luminances in landscapes by a well-known painter. We show empirically that elements of contemporary approaches to high-dynamic range tone-mapping-which are themselves deeply rooted in an understanding of early visual system coding-are present in the way Vincent Van Gogh transforms scene luminances into painting luminances. We argue that global tone mapping functions are a useful descriptor of an artist's perceptual goals with respect to global illumination and we present evidence that mapping the scene to a painting with different implied lighting properties produces a less efficient mapping. Together, these studies suggest that statistical regularities in art can shed
Electrical Breakdown In Nitrogen At Low Pressure - Physical Processes And Statistics
NASA Astrophysics Data System (ADS)
Gocic, S.
2010-07-01
The results of investigation of the electrical breakdown in nitrogen, obtained in combined approach based on measuring of the current-voltage characteristic, modeling of basic physical processes and statistical analysis of the breakdown time delay are presented in this report. Measurement of the current-voltage characteristics with additional monitoring of spatial and temporal distribution of the emission from discharge provides information concerned on development of different regime of low-pressure gas discharge and on processes of the electrical breakdown and discharge maintenance. The presented model of the gas discharge includes the kinetics of mains constituents of the nitrogen plasma, charged particles, vibrationally manifold of molecular ground state, molecular singlet and triplet states and nitrogen atoms. The model is applied in case of a homogenous electric field, at electric field to gas density ratio E/N of 1000 Td (1Td = 10^-17 Vcm^2). The obtained results show that the main mechanism of a nitrogen atoms production in this case is the molecular dissociation in a direct electron impact, while influence of highly excited vibrationall states can be neglected. Also, two new distributions of the statistical time delay of electrical breakdown in nitrogen, the Gaussian and Gauss-exponential ones, are presented. Distributions are theoretically founded on binomial distribution for the occurrence of initiating electrons and described by using analytical and numerical models. Moreover, the correlation coefficient between the statistical and formative time delay of electrical breakdown in nitrogen is de- termined. Starting from bivariate normal (Gaussian) distribution of two random variables, the analytical distribution of the electrical breakdown time delay is theoretically founded on correlation of the dependent statistical and formative time delay. Gaussian density dis- tribution of the electrical breakdown time delay goes to Gaussian of the formative time or
Doubly stochastic Poisson processes in artificial neural learning.
Card, H C
1998-01-01
This paper investigates neuron activation statistics in artificial neural networks employing stochastic arithmetic. It is shown that a doubly stochastic Poisson process is an appropriate model for the signals in these circuits.
ERIC Educational Resources Information Center
Novak, Elena; Johnson, Tristan E.; Tenenbaum, Gershon; Shute, Valerie J.
2016-01-01
The study explored instructional benefits of a storyline gaming characteristic (GC) on learning effectiveness, efficiency, and engagement with the use of an online instructional simulation for graduate students in an introductory statistics course. A storyline is a game-design element that connects scenes with the educational content. In order to…
ERIC Educational Resources Information Center
Kalaian, Sema A.; Kasim, Rafa M.
2014-01-01
This meta-analytic study focused on the quantitative integration and synthesis of the accumulated pedagogical research in undergraduate statistics education literature. These accumulated research studies compared the academic achievement of students who had been instructed using one of the various forms of small-group learning methods to those who…
ERIC Educational Resources Information Center
Valdes, Kathryn A.; And Others
This volume of the National Longitudinal Transition Study of Special Education Students (NLTS) offers statistical data relating to 1,191 students with learning disabilities (ages 13-21). The study design involved a survey of parents/guardians, examination of school records, and a survey of school programs. The 43 tables describe: youths'…
Measuring Learning Outcomes and Attitudes in a Flipped Introductory Statistics Course
ERIC Educational Resources Information Center
Cilli-Turner, Emily
2015-01-01
Recent studies have highlighted the positive effects on learning and retention rates that active learning brings to the classroom. A flipped classroom is a type of active learning where transmission of content occurs outside of the classroom environment and problem solving and learning activities become the focus of classroom time. This article…
NASA Astrophysics Data System (ADS)
Stefani, Jerry A.; Poarch, Scott; Saxena, Sharad; Mozumder, P. K.
1994-09-01
An advanced multivariable off-line process control system, which combines traditional Statistical Process Control (SPC) with feedback control, has been applied to the CVD tungsten process on an Applied Materials Centura reactor. The goal of the model-based controller is to compensate for shifts in the process and maintain the wafer state responses on target. In the present application the controller employs measurements made on test wafers by off-line metrology tools to track the process behavior. This is accomplished by using model- bases SPC, which compares the measurements with predictions obtained from empirically-derived process models. For CVD tungsten, a physically-based modeling approach was employed based on the kinetically-limited H2 reduction of WF6. On detecting a statistically significant shift in the process, the controller calculates adjustments to the settings to bring the process responses back on target. To achieve this a few additional test wafers are processed at slightly different settings than the nominal. This local experiment allows the models to be updated to reflect the current process performance. The model updates are expressed as multiplicative or additive changes in the process inputs and a change in the model constant. This approach for model updating not only tracks the present process/equipment state, but it also provides some diagnostic capability regarding the cause of the process shift. The updated models are used by an optimizer to compute new settings to bring the responses back to target. The optimizer is capable of incrementally entering controllables into the strategy, reflecting the degree to which the engineer desires to manipulates each setting. The capability of the controller to compensate for shifts in the CVD tungsten process has been demonstrated. Targets for film bulk resistivity and deposition rate were maintained while satisfying constraints on film stress and WF6 conversion efficiency.
New ordering principle for the classical statistical analysis of Poisson processes with background
NASA Astrophysics Data System (ADS)
Giunti, C.
1999-03-01
Inspired by the recent proposal by Feldman and Cousins of a ``unified approach to the classical statistical analysis of small signals'' based on a choice of ordering in Neyman's construction of classical confidence intervals, I propose a new ordering principle for the classical statistical analysis of Poisson processes with a background which minimizes the effect on the resulting confidence intervals of the observation of fewer background events than expected. The new ordering principle is applied to the calculation of the confidence region implied by the recent null result of the KARMEN neutrino oscillation experiment.
A survey of image processing techniques and statistics for ballistic specimens in forensic science.
Gerules, George; Bhatia, Sanjiv K; Jackson, Daniel E
2013-06-01
This paper provides a review of recent investigations on the image processing techniques used to match spent bullets and cartridge cases. It is also, to a lesser extent, a review of the statistical methods that are used to judge the uniqueness of fired bullets and spent cartridge cases. We review 2D and 3D imaging techniques as well as many of the algorithms used to match these images. We also provide a discussion of the strengths and weaknesses of these methods for both image matching and statistical uniqueness. The goal of this paper is to be a reference for investigators and scientists working in this field.
Crowder, S.V.; Eshleman, L.
1998-08-01
In many manufacturing environments such as the nuclear weapons complex, emphasis has shifted from the regular production and delivery of large orders to infrequent small orders. However, the challenge to maintain the same high quality and reliability standards white building much smaller lot sizes remains. To meet this challenge, specific areas need more attention, including fast and on-target process start-up, low volume statistical process control, process characterization with small experiments, and estimating reliability given few actual performance tests of the product. In this paper the authors address the issue of low volume statistical process control. They investigate an adaptive filtering approach to process monitoring with a relatively short time series of autocorrelated data. The emphasis is on estimation and minimization of mean squared error rather than the traditional hypothesis testing and run length analyses associated with process control charting. The authors develop an adaptive filtering technique that assumes initial process parameters are unknown, and updates the parameters as more data become available. Using simulation techniques, they study the data requirements (the length of a time series of autocorrelated data) necessary to adequately estimate process parameters. They show that far fewer data values are needed than is typically recommended for process control applications. And they demonstrate the techniques with a case study from the nuclear weapons manufacturing complex.
CROWDER, STEPHEN V.
1999-09-01
In many manufacturing environments such as the nuclear weapons complex, emphasis has shifted from the regular production and delivery of large orders to infrequent small orders. However, the challenge to maintain the same high quality and reliability standards while building much smaller lot sizes remains. To meet this challenge, specific areas need more attention, including fast and on-target process start-up, low volume statistical process control, process characterization with small experiments, and estimating reliability given few actual performance tests of the product. In this paper we address the issue of low volume statistical process control. We investigate an adaptive filtering approach to process monitoring with a relatively short time series of autocorrelated data. The emphasis is on estimation and minimization of mean squared error rather than the traditional hypothesis testing and run length analyses associated with process control charting. We develop an adaptive filtering technique that assumes initial process parameters are unknown, and updates the parameters as more data become available. Using simulation techniques, we study the data requirements (the length of a time series of autocorrelated data) necessary to adequately estimate process parameters. We show that far fewer data values are needed than is typically recommended for process control applications. We also demonstrate the techniques with a case study from the nuclear weapons manufacturing complex.
Application of statistical process control charts to monitor changes in animal production systems.
De Vries, A; Reneau, J K
2010-04-01
Statistical process control (SPC) is a method of monitoring, controlling, and improving a process through statistical analysis. An important SPC tool is the control chart, which can be used to detect changes in production processes, including animal production systems, with a statistical level of confidence. This paper introduces the philosophy and types of control charts, design and performance issues, and provides a review of control chart applications in animal production systems found in the literature from 1977 to 2009. Primarily Shewhart and cumulative sum control charts have been described in animal production systems, with examples found in poultry, swine, dairy, and beef production systems. Examples include monitoring of growth, disease incidence, water intake, milk production, and reproductive performance. Most applications describe charting outcome variables, but more examples of control charts applied to input variables are needed, such as compliance to protocols, feeding practice, diet composition, and environmental factors. Common challenges for applications in animal production systems are the identification of the best statistical model for the common cause variability, grouping of data, selection of type of control chart, the cost of false alarms and lack of signals, and difficulty identifying the special causes when a change is signaled. Nevertheless, carefully constructed control charts are powerful methods to monitor animal production systems. Control charts might also supplement randomized controlled trials. PMID:20081080
Statistically Qualified Neuro-Analytic system and Method for Process Monitoring
Vilim, Richard B.; Garcia, Humberto E.; Chen, Frederick W.
1998-11-04
An apparatus and method for monitoring a process involves development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two steps: deterministic model adaption and stochastic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics,augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation emor minimization technique. Stochastic model adaptation involves qualifying any remaining uncertainty in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output. Preferably, the developed SQNA model is validated using known sequential probability ratio tests and applied to the process as an on-line monitoring system.
Abstraction processes in artificial grammar learning.
Shanks, D R; Johnstone, T; Staggs, L
1997-02-01
Four experiments explored the extent the extent to which abstract knowledge may underlie subjects' performance when asked to judge the grammaticality of letter strings generated from an artificial grammar. In Experiment 1 and 2 subjects studied grammatical strings instantiated with one set of letters and were then tested on grammatical and ungrammatical strings formed either from the same or a changed letter-set. Even with a change of letter-set, subjects were found to be sensitive to a variety of violation of the grammar. In Experiments 3 and 4, the critical manipulation involved the way in which the training strings were studied: an incidental learning procedure was used for some subjects, and others engaged in an explicit code-breaking task to try to learn the rules of the grammar. When strings were generated from a biconditional (Experiment 4) but not from a standard finite-state grammar (Experiment 3), grammaticality judgements for test strings were independent of their surface similarity to specific studied strings. Overall, the results suggest that transfer in this simple memory task is mediated at least to some extent by abstract knowledge.