title: Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities.
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.
authors.affiliation: CNRS & Aix-Marseille University, Marseille, France. bog.louisa@gmail.com.
authors.forename: Louisa
authors.initials: L
authors.lastname: Bogaerts
authors.affiliation: Hebrew University of Jerusalem, Jerusalem, Israel.
authors.forename: Noam
authors.initials: N
authors.lastname: Siegelman
authors.affiliation: Hebrew University of Jerusalem, Jerusalem, Israel.|Haskins Laboratories, New Haven, CT, USA.|Basque Center of Cognition, Brain and Language, San Sebastian, Spain.
authors.forename: Ram
authors.initials: R
authors.lastname: Frost
mesh_terms: D000328:Adult; D001245:Association Learning; D005260:Female; D005556:Form Perception; D006801:Humans; D007206:Individuality; D008297:Male; D008570:Memory, Short-Term; D011337:Probability Learning; D011340:Problem Solving; D011930:Reaction Time; D012691:Serial Learning; D055815:Young Adult
keywords: Individual differences; Sequence learning; Visual statistical learning