title: Domain generality versus modality specificity: the paradox of statistical learning.
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.
authors: The Hebrew University of Jerusalem, Jerusalem, Israel; Haskins Laboratories, New Haven, CT, USA; Basque Center for Cognition, Brain, and Language, San Sebastian, Spain. Electronic address: ram.frost@mail.huji.ac.il.
authors: Ram
authors: R
authors: Frost
authors: Basque Center for Cognition, Brain, and Language, San Sebastian, Spain.
authors: Blair C
authors: BC
authors: Armstrong
authors: The Hebrew University of Jerusalem, Jerusalem, Israel.
authors: Noam
authors: N
authors: Siegelman
authors: Haskins Laboratories, New Haven, CT, USA; Cornell University, Ithaca, NY, USA; University of Southern Denmark, Odense, Denmark.
authors: Morten H
authors: MH
authors: Christiansen
mesh_terms: D001921:Brain / Q000502:physiology*; D006801:Humans; D007206:Individuality; D008960:Models, Psychological; D011337:Probability Learning
keywords: domain-general mechanisms; modality specificity; neurobiologically plausible models; statistical learning; stimulus specificity
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