Domain generality versus modality specificity: the paradox of statistical learning

• Published in: Trends in cognitive sciences Vol. 19; no. 3; pp. 117 - 125
• Main Authors: Frost, Ram, Armstrong, Blair C., Siegelman, Noam, Christiansen, Morten H.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.03.2015
• Subjects: Brain - physiology; domain-general mechanisms; Humans; Individuality; modality specificity; Models, Psychological; neurobiologically plausible models; Neurology; Probability Learning; Psychiatry; statistical learning; stimulus specificity; stimulus specificity; statistical learning; modality specificity; neurobiologically plausible models; domain-general mechanisms
• DOI: 10.1016/j.tics.2014.12.010
• ISSN: 1364-6613; 1879-307X

•Statistical learning (SL) theory is challenged by modality/stimulus-specific effects.•We argue that SL is shaped by both modality-specific constraints and domain-general principles.•SL relies on modality-specific neural networks and partially shared neural networks.•Studies of individual differences provide targeted insights into mechanisms of SL. 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.