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

Louisa Bogaerts*, Noam Siegelman, Ram Frost

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)1250-1256
Number of pages7
JournalPsychonomic Bulletin and Review
Volume23
Issue number4
DOIs
Publication statusPublished - 1 Aug 2016
Externally publishedYes

Keywords

  • Individual differences
  • Sequence learning
  • Visual statistical learning

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