Understanding the Neural Bases of Implicit and Statistical Learning

Laura J. Batterink*, Ken Paller, Paul J Reber

*Corresponding author for this work

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Both implicit learning and statistical learning focus on the ability of learners to pick up on patterns in the environment. It has been suggested that these two lines of research may be combined into a single construct of “implicit statistical learning.” However, by comparing the neural processes that give rise to implicit versus statistical learning, we may determine the extent to which these two learning paradigms do indeed describe the same core mechanisms. In this review, we describe current knowledge about neural mechanisms underlying both implicit learning and statistical learning, highlighting converging findings between these two literatures. A common thread across all paradigms is that learning is supported by interactions between the declarative and nondeclarative memory systems of the brain. We conclude by discussing several outstanding research questions and future directions for each of these two research fields. Moving forward, we suggest that the two literatures may interface by defining learning according to experimental paradigm, with “implicit learning” reserved as a specific term to denote learning without awareness, which may potentially occur across all paradigms. By continuing to align these two strands of research, we will be in a better position to characterize the neural bases of both implicit and statistical learning, ultimately improving our understanding of core mechanisms that underlie a wide variety of human cognitive abilities.

Original languageEnglish (US)
JournalTopics in Cognitive Science
DOIs
StatePublished - Jan 1 2019

Keywords

  • EEG
  • fMRI
  • Implicit learning
  • Neural basis
  • Neuroimaging
  • Neuropsychology
  • Neuroscience
  • Statistical learning

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Linguistics and Language
  • Human-Computer Interaction
  • Cognitive Neuroscience
  • Artificial Intelligence

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