Is there such a thing as a ‘good statistical learner’?

Louisa Bogaerts*, Noam Siegelman, Morten H. Christiansen, Ram Frost

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from ‘bad’ to ‘good’ statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research.

Original languageAmerican English
Pages (from-to)25-37
Number of pages13
JournalTrends in Cognitive Sciences
Volume26
Issue number1
DOIs
StatePublished - Jan 2022
Externally publishedYes

Bibliographical note

Funding Information:
This article was supported by the European Research Council (ERC) Advanced Grant Project 692502-L2STAT and the Israel Science Foundation (ISF) Grant Project 705/20 , awarded to R.F. L.B. received funding from the ERC Advanced Grant Project 833029-LEARNATTEND . N.S. received funding from the ISF , grant number 48/20 . We thank Merav Ahissar, Erin Isbilen, and Felicity Frinsel for their feedback on an earlier version of the paper and Blair Armstrong for the helpful discussions we had regarding computational mechanisms.

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • cognitive abilities
  • individual differences
  • statistical learning

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