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Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities
dc.contributor.author | Bogaerts, Louisa | |
dc.contributor.author | Siegelman, Noam | |
dc.contributor.author | Frost, Ram | |
dc.date.accessioned | 2017-09-26T11:42:40Z | |
dc.date.available | 2017-09-26T11:42:40Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Bogaerts, L., Siegelman, N. & Frost, R. Psychon Bull Rev (2016) 23: 1250. https://doi.org/10.3758/s13423-015-0996-z | es_ES |
dc.identifier.issn | 1069-9384 | |
dc.identifier.uri | http://hdl.handle.net/10810/22692 | |
dc.description | Published online: 7 January 2016 | es_ES |
dc.description.abstract | 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. | es_ES |
dc.description.sponsorship | This paper was supported by the Israel Science Foundation (Grant 217/14 awarded to Ram Frost), and by the National Institute of Child Health and Human Development (RO1 HD 067364 awarded to Ken Pugh and Ram Frost, and PO1-HD 01994 awarded to Haskins Laboratories). Louisa Bogaerts is a research fellow of the Fyssen Foundation. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Psychonomic Bulletin & Review | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Visual statistical learning | es_ES |
dc.subject | Sequence learning | es_ES |
dc.subject | Individual differences | es_ES |
dc.title | Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © Psychonomic Society, Inc. 2016 | es_ES |
dc.relation.publisherversion | https://link.springer.com/journal/13423 | es_ES |
dc.identifier.doi | 10.3758/s13423-015-0996-z |