Beta-band activity is a signature of statistical learning

Louisa Bogaerts*, Craig G. Richter, Ayelet N. Landau, Ram Frost

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Through statistical learning (SL), cognitive systems may discover the underlying regularities in the environment. Testing human adults (n = 35, 21 females), we document, in the context of a classical visual SL task, divergent rhythmic EEG activity in the interstimulus delay periods within patterns versus between patterns (i.e., pattern transitions). Our findings reveal increased oscillatory activity in the beta band (∼20 Hz) at triplet transitions that indexes learning: It emerges with increased pattern repetitions; and importantly, it is highly correlated with behavioral learning outcomes. These findings hold the promise of converging on an online measure of learning regularities and provide important theoretical insights regarding the mechanisms of SL and prediction.

Original languageAmerican English
Pages (from-to)7523-7530
Number of pages8
JournalJournal of Neuroscience
Volume40
Issue number39
DOIs
StatePublished - 23 Sep 2020

Bibliographical note

Publisher Copyright:
© 2020 the authors.

Keywords

  • Electroencephalography
  • Neurobiological signature
  • Prediction
  • Statistical learning

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