What exactly is learned in visual statistical learning? Insights from Bayesian modeling

Available online 19 June 2019.

Detalles Bibliográficos
Autores: Siegelman, Noam, Bogaerts, Louisa, Armstrong, Blair C., Frost, Ram
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/41124
Acceso en línea:http://hdl.handle.net/10810/41124
Access Level:acceso abierto
Palabra clave:Statistical learning
Bayesian modeling
Online measures
Individual differences
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spelling What exactly is learned in visual statistical learning? Insights from Bayesian modelingSiegelman, NoamBogaerts, LouisaArmstrong, Blair C.Frost, RamStatistical learningBayesian modelingOnline measuresIndividual differencesAvailable online 19 June 2019.It is well documented that humans can extract patterns from continuous input through Statistical Learning (SL) mechanisms. The exact computations underlying this ability, however, remain unclear. One outstanding controversy is whether learners extract global clusters from the continuous input, or whether they are tuned to local co-occurrences of pairs of elements. Here we adopt a novel framework to address this issue, applying a generative latent-mixture Bayesian model to data tracking SL as it unfolds online using a self-paced learning paradigm. This framework not only speaks to whether SL proceeds through computations of global patterns versus local co-occurrences, but also reveals the extent to which specific individuals employ these computations. Our results provide evidence for inter-individual mixture, with different reliance on the two types of computations across individuals. We discuss the implications of these findings for understanding the nature of SL and individual-differences in this ability.This paper was supported by the ERC Advanced grant awarded to Ram Frost (project 692502-L2STAT), and the Israel Science Foundation (Grant 217/14 awarded to Ram Frost), and NSERC grant DG-502584 to Blair Armstrong. Noam Siegelman is a Rothschild Yad-Hanadiv postdoctoral fellow. Louisa Bogaerts received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No. 743528 (IF-EF), at the Hebrew UniversityCognition202020202019info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/41124reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/EC/H2020/ERC-692502-L2STATinfo:eu-repo/grantAgreement/EC/H2020/MChttps://www.sciencedirect.com/journal/cognitioninfo:eu-repo/semantics/openAccess© 2019 Elsevier B.V. All rights reserved.oai:addi.ehu.eus:10810/411242026-06-18T09:23:17Z
dc.title.none.fl_str_mv What exactly is learned in visual statistical learning? Insights from Bayesian modeling
title What exactly is learned in visual statistical learning? Insights from Bayesian modeling
spellingShingle What exactly is learned in visual statistical learning? Insights from Bayesian modeling
Siegelman, Noam
Statistical learning
Bayesian modeling
Online measures
Individual differences
title_short What exactly is learned in visual statistical learning? Insights from Bayesian modeling
title_full What exactly is learned in visual statistical learning? Insights from Bayesian modeling
title_fullStr What exactly is learned in visual statistical learning? Insights from Bayesian modeling
title_full_unstemmed What exactly is learned in visual statistical learning? Insights from Bayesian modeling
title_sort What exactly is learned in visual statistical learning? Insights from Bayesian modeling
dc.creator.none.fl_str_mv Siegelman, Noam
Bogaerts, Louisa
Armstrong, Blair C.
Frost, Ram
author Siegelman, Noam
author_facet Siegelman, Noam
Bogaerts, Louisa
Armstrong, Blair C.
Frost, Ram
author_role author
author2 Bogaerts, Louisa
Armstrong, Blair C.
Frost, Ram
author2_role author
author
author
dc.subject.none.fl_str_mv Statistical learning
Bayesian modeling
Online measures
Individual differences
topic Statistical learning
Bayesian modeling
Online measures
Individual differences
description Available online 19 June 2019.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/41124
url http://hdl.handle.net/10810/41124
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/EC/H2020/ERC-692502-L2STAT
info:eu-repo/grantAgreement/EC/H2020/MC
https://www.sciencedirect.com/journal/cognition
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
© 2019 Elsevier B.V. All rights reserved.
eu_rights_str_mv openAccess
rights_invalid_str_mv © 2019 Elsevier B.V. All rights reserved.
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Cognition
publisher.none.fl_str_mv Cognition
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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