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 | |