scholarly journals Order in the Statistical Learning of Phonotactics

2020 ◽  
Author(s):  
Peter Richtsmeier
Keyword(s):  
Statistical Learning ◽  
Temporal Structure ◽  
Order Effects ◽  
Learning Paradigm ◽  
Adult Participants ◽  
Learning Research ◽  
Strong Order ◽  
With Memory

A premise of statistical learning research is that learners attend to and learn the frequencies of repeating or co-occurring elements in the input. When the input is a series of words, participants readily learn the frequencies of phoneme sequences, that is, to learn phonotactic frequencies. Inherent to the concepts of both frequency and phonotactics is order, or the temporal structure of the input. Order is similarly inherent to statistical learning, yet the effect of order on statistical learning is not well understood. In the present study, adult participants learned the relative frequencies of eight item-medial consonant sequences, for example, the /mk/ in /nʌmkət/. Across five ordering conditions, both familiarization and test stimuli were independently ordered and randomized, thus allowing for a relatively broad search for order effects in an established statistical learning paradigm. Participants learned the target frequencies equivalently across the five ordering conditions, indicating no modulating effect of order. Nevertheless, participants also approached the task by applying idiosyncratic, structured orders to their responses. The result is an unexpected but robust effect of order. Both the results and the design of the study also allow for increased integration of statistical learning with memory and other aspects of cognition.

Rapid Serial Auditory Presentation

2015 ◽  
Vol 62 (5) ◽  
pp. 346-351 ◽  
Author(s):  
Ana Franco ◽  
Julia Eberlen ◽  
Arnaud Destrebecqz ◽  
Axel Cleeremans ◽  
Julie Bertels
Keyword(s):  
Statistical Learning ◽  
Reaction Times ◽  
Visual Presentation ◽  
Detection Task ◽  
Speech Segmentation ◽  
Auditory Presentation ◽  
Indirect Measure ◽  
Speech Stream ◽  
Adult Participants ◽  
Presentation Procedure

Abstract. 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.

Combining statistical learning and geostatistical approaches in a spatiotemporal framework for low flow estimation

2021 ◽  
Author(s):  
Johannes Laimighofer ◽  
Michael Melcher ◽  
Juraj Parajka ◽  
Gregor Laaha
Keyword(s):  
Statistical Learning ◽  
Mean Squared Error ◽  
Temporal Structure ◽  
Empirical Orthogonal Functions ◽  
Regression Coefficients ◽  
Low Flow ◽  
Support Vector ◽  
Spatiotemporal Model ◽  
Residual Field ◽  
The Mean

<p><span>This paper aims to develop a spatiotemporal model to estimate monthly low flow quantiles Q95 [P(Q<Q95=0.05)] standardized by catchment area in Austria. Our dataset consists of 325 gauging stations that where consistently monitored between 1976 to 2015, and it covers about 60% of the national territory of Austria. </span></p><p><span>In a first step we are adapting a spatiotemporal model initially designed for modeling air pollution data. This approach is based on empirical orthogonal functions (EOF), that should capture the temporal structure of the spatiotemporal model. The EOFs are weighted by regression coefficients estimated by universal kriging. We extend the model by using GLM-boosting, LASSO, Principal Component Regression (PCR) and Random Forest (RF) for selecting the regression coefficients of the EOFs. Furthermore, we do not limit the kriging structure of the residual field to geographical coordinates but use a broader approach of physiographic kriging. In a second step we implement separate models for the mean parts of the model and the residual parts of the model. The mean field is defined by statistical learning methods as RF, GAM-boosting, LASSO and Support Vector Machines (SVM). For the residual field we define two different approaches, either the </span><span>method developed in the first step</span><span> or spatiotemporal kriging.</span></p><p><span>Model performance is evaluated by cross validation and the best model is selected by the mean squared error (MSE). </span></p><p> </p>

Relational problems are not fully solved by a temporal sequence of statistical learning episodes

1997 ◽  
Vol 20 (1) ◽  
pp. 82-82 ◽  
Author(s):  
A. Vinter ◽  
P. Perruchet
Keyword(s):  
Implicit Learning ◽  
Statistical Learning ◽  
Temporal Sequence ◽  
The Other ◽  
Learning Mechanisms ◽  
Learning Research ◽  
The One

Clark & Thornton's conception finds an echo in implicit learning research, which shows that subjects may perform adaptively in complex structured situations through the use of simple statistical learning mechanisms. However, the authors fail to draw a distinction between, on the one hand, subjects' representations which emerge from type-1 learning mechanisms, and, on the other, their knowledge of the genuine abstract “recoding function” which defines a type-2 problem.

scholarly journals Non-native phonemes in adult word learning: evidence from the N400m

2009 ◽  
Vol 364 (1536) ◽  
pp. 3697-3709 ◽  
Author(s):  
Christian Dobel ◽  
Lothar Lagemann ◽  
Pienie Zwitserlood
Keyword(s):  
Statistical Learning ◽  
Word Learning ◽  
Native Language ◽  
The Novel ◽  
Learning Paradigm ◽  
Minimal Pairs ◽  
Lexical Activation ◽  
Semantic Access ◽  
Novel Words

Newborns are equipped with a large phonemic inventory that becomes tuned to one's native language early in life. We review and add new data about how learning of a non-native phoneme can be accomplished in adults and how the efficiency of word learning can be assessed by neurophysiological measures. For this purpose, we studied the acquisition of the voiceless, bilabial fricative /Φ/ via a statistical-learning paradigm. Phonemes were embedded in minimal pairs of pseudowords, differing only with respect to the fricative (/aΦo/ versus /afo/). During learning, pseudowords were combined with pictures of objects with some combinations of pseudowords and pictures occurring more frequently than others. Behavioural data and the N400m component, as an index of lexical activation/semantic access, showed that participants had learned to associate the pseudowords with the pictures. However, they could not discriminate within the minimal pairs. Importantly, before learning, the novel words with the sound /Φ/ showed smaller N400 amplitudes than those with native phonemes, evidencing their non-word status. Learning abolished this difference indicating that /Φ/ had become integrated into the native category /f/, instead of establishing a novel category. Our data and review demonstrate that native phonemic categories are powerful attractors hampering the mastery of non-native contrasts.

scholarly journals Examining Three-way Binding as a Constraint on Statistical Learning

2018 ◽  
Author(s):  
Hyungwook Yim ◽  
Simon Dennis ◽  
Vladimir Sloutsky
Keyword(s):  
Statistical Learning ◽  
Explicit Memory ◽  
Paired Associate ◽  
Empirical Studies ◽  
Learning Task ◽  
Paired Associate Learning ◽  
The Other ◽  
Learning Paradigm ◽  
Associate Learning ◽  
Memory Structures

Models of statistical learning do not place constraints on the complexity of the memory structure that is formed during statistical learning, while empirical studies using the statistical learning task have only examined the formation of simple memory structures (e.g., two-way binding). On the other hand, the memory literature, using explicit memory tasks, has shown that people are able to form memory structures of different complexities and that more complex memory structures (e.g., three-way binding) are usually more difficult to form. We examined whether complex memory structures such as three-way bindings can be implicitly formed through statistical learning by utilizing manipulations that have been used in the paired-associate learning paradigm (e.g., AB/ABr condition). Through three experiments, we show that while simple two-way binding structures can be formed implicitly, three-way bindings can only be formed with explicit instructions. The results indicate that explicit attention may be a necessary component in forming three-way memory structures and suggest that existing models should place constraints on the representational structures that can be formed.

scholarly journals Reinforced statistical learning of auditory categories

2020 ◽  
Author(s):  
Bob McMurray ◽  
Samantha Chiu
Keyword(s):  
Language Acquisition ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Statistical Learning ◽  
Relevant Dimension ◽  
Learning Paradigm ◽  
Statistical Structure ◽  
Critical Step ◽  
Specific Category ◽  
L2 Learning

A critical step in language acquisition is learning phoneme categories. While L1 learning has been thought to use unsupervised learning (using the distributional statistics of cues), recent research raises the possibility of supervised learning (using teaching signals). Similarly, L2 learning is studied with supervised learning, but unsupervised may also contribute. We developed the reinforced statistical learning paradigm to examine their interaction. Participants first underwent unsupervised learning, hearing a series of non-linguistic sounds whose statistical structure reflected two categories. In subsequent supervised learning, categories either matched or mismatched. Supervised learning was faster when phases matched, though benefits were limited to specific category configurations. Unsupervised learning did not affect the steepness of categorization along the relevant dimension, but it helped subjects learn to ignore irrelevant dimensions. Unsupervised learning may set the stage for supervised learning, but its role may be to determine which dimensions are important, and not to directly acquire categories.

The Columbus-Copapayo Sister-City Collection: A Service Learning/Research Model

2017 ◽  
Vol 39 (2) ◽  
pp. 39-43
Author(s):  
Katherine Borland
Keyword(s):  
Service Learning ◽  
Academic Community ◽  
Ohio State University ◽  
State University ◽  
Research Model ◽  
Research Partnership ◽  
Learning Paradigm ◽  
Learning Research ◽  
The Ohio State University

A central difficulty of the service learning paradigm is how to connect with and sustain a community identified project within the structural limitations of an academic course. The Columbus-Copapayo Sister-City Collection at the Ohio State University Folklore Archives resulted from an attempt to construct a research partnership independently of coursework to which service learning coursework could be appended. The model provides a flexible, open-ended means for pursuing academic-community collaborations.

The paradigm of statistical inference and the paradigm of statistical learning

2021 ◽  
Vol 68 (1) ◽  
pp. 1-16
Author(s):  
Józef Pociecha
Keyword(s):  
Statistical Inference ◽  
Statistical Learning ◽  
Thomas Kuhn ◽  
Learning Paradigm ◽  
Statistical Research ◽  
Learning From Data ◽  
Starting Point ◽  
Effective Reduction ◽  
Similarities And Differences

The starting point for the presentation of the similarities and differences between the principles of conducting statistical research according to the rules of both statistical inference and statistical learning is the paradigm theory, formulated by Thomas Kuhn. In the first section of this paper, the essential features of the statistical inference paradigm are characterised, with particular attention devoted to its limitations in contemporary statistical research. Subsequently, the article presents the challenges faced by this research jointly with the expanding opportunities for their effective reduction. The essence of learning from data is discussed and the principles of statistical learning are defined. Moreover, significant features of the statistical learning paradigm are formulated in the context of the differences between the statistical inference paradigm and the statistical learning paradigm. It is emphasised that the statistical learning paradigm, as the more universal one of the two discussed, broadens the possibilities of conducting statistical research, especially in socio-economic sciences.

Sign in / Sign up

Export Citation Format

Share Document