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dc.contributor.authorSiegelman, Noam
dc.contributor.authorBogaerts, Louisa
dc.contributor.authorArmstrong, Blair C.
dc.contributor.authorFrost, Ram
dc.date2020-06-19
dc.date.accessioned2020-02-14T07:45:52Z
dc.date.available2020-02-14T07:45:52Z
dc.date.issued2019
dc.identifier.citationNoam Siegelman, Louisa Bogaerts, Blair C. Armstrong, Ram Frost, What exactly is learned in visual statistical learning? Insights from Bayesian modeling, Cognition, Volume 192, 2019, 104002, ISSN 0010-0277, https://doi.org/10.1016/j.cognition.2019.06.014.es_ES
dc.identifier.issn0010-0277
dc.identifier.urihttp://hdl.handle.net/10810/41124
dc.descriptionAvailable online 19 June 2019.es_ES
dc.description.abstractIt 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.es_ES
dc.description.sponsorshipThis 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 Universityes_ES
dc.language.isoenges_ES
dc.publisherCognitiones_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/ERC-692502-L2STATes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/MC/743528es_ES
dc.rightsinfo:eu-repo/semantics/embargoedAccesses_ES
dc.subjectStatistical learninges_ES
dc.subjectBayesian modelinges_ES
dc.subjectOnline measureses_ES
dc.subjectIndividual differenceses_ES
dc.titleWhat exactly is learned in visual statistical learning? Insights from Bayesian modelinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2019 Elsevier B.V. All rights reserved.es_ES
dc.relation.publisherversionhttps://www.sciencedirect.com/journal/cognitiones_ES
dc.identifier.doi10.1016/j.cognition.2019.06.014


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