How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning

Research output: Contribution to journalReview article

3 Scopus citations

Abstract

Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a “suite” of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors.

Original languageEnglish (US)
Pages (from-to)279-299
Number of pages21
JournalNeuroscience and Biobehavioral Reviews
Volume112
DOIs
StatePublished - May 2020

Keywords

  • Artificial grammar learning
  • Implicit learning
  • Sequential learning
  • Statistical learning

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Cognitive Neuroscience
  • Behavioral Neuroscience

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