Domain generality versus modality specificity: The paradox of statistical learning

Ram Frost*, Blair C. Armstrong, Noam Siegelman, Morten H. Christiansen

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

332 Scopus citations

Abstract

Statistical learning (SL) is typically considered to be a domain-general mechanism by which cognitive systems discover the underlying distributional properties of the input. However, recent studies examining whether there are commonalities in the learning of distributional information across different domains or modalities consistently reveal modality and stimulus specificity. Therefore, important questions are how and why a hypothesized domain-general learning mechanism systematically produces such effects. Here, we offer a theoretical framework according to which SL is not a unitary mechanism, but a set of domain-general computational principles that operate in different modalities and, therefore, are subject to the specific constraints characteristic of their respective brain regions. This framework offers testable predictions and we discuss its computational and neurobiological plausibility.

Original languageAmerican English
Pages (from-to)117-125
Number of pages9
JournalTrends in Cognitive Sciences
Volume19
Issue number3
DOIs
StatePublished - 2015

Bibliographical note

Publisher Copyright:
© 2014 Elsevier Ltd.

Keywords

  • Domain-general mechanisms
  • Modality specificity
  • Neurobiologically plausible models
  • Statistical learning
  • Stimulus specificity

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