Neurocognitive mechanisms of statistical-sequential learning: What do event-related potentials tell us?

Jerome Daltrozzo, Christopher M. Conway

Research output: Contribution to journalReview articlepeer-review

64 Scopus citations


Statistical-sequential learning (SL) is the ability to process patterns of environmental stimuli, such as spoken language, music, or one's motor actions, that unfold in time. The underlying neurocognitive mechanisms of SL and the associated cognitive representations are still not well understood as reflected by the heterogeneity of the reviewed cognitive models. The purpose of this review is: (1) to provide a general overview of the primary models and theories of SL, (2) to describe the empirical research - with a focus on the event-related potential (ERP) literature - in support of these models while also highlighting the current limitations of this research, and (3) to present a set of new lines of ERP research to overcome these limitations. The review is articulated around three descriptive dimensions in relation to SL: The level of abstractness of the representations learned through SL, the effect of the level of attention and consciousness on SL, and the developmental trajectory of SL across the life-span. We conclude with a new tentative model that takes into account these three dimensions and also point to several promising new lines of SL research.

Original languageEnglish (US)
Article number437
JournalFrontiers in Human Neuroscience
Issue numberJUNE
StatePublished - Jun 18 2014
Externally publishedYes


  • Artificial grammar
  • ERP
  • Implicit learning
  • P300
  • P600
  • Procedural learning
  • Sequential learning
  • Statistical learning

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Neurology
  • Psychiatry and Mental health
  • Biological Psychiatry
  • Behavioral Neuroscience


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