Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities

Louisa Bogaerts, Noam Siegelman, Ram Frost

פרסום מחקרי: פרסום בכתב עתמאמרביקורת עמיתים

תקציר

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.

שפה מקוריתאנגלית אמריקאית
עמודים (מ-עד)1250-1256
מספר עמודים7
כתב עתPsychonomic Bulletin and Review
כרך23
מספר גיליון4
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 1 אוג׳ 2016

ASJC Scopus subject areas

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