The multi-faceted nature of visual statistical learning: Individual differences in learning conditional and distributional regularities across time and space

Bethany Growns*, Noam Siegelman, Kristy A. Martire

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Emerging research has demonstrated that statistical learning is a modality-specific ability governed by domain-general principles. Yet limited research has investigated different forms of statistical learning within modality. This paper explores whether there is one unified statistical learning mechanism within the visual modality, or separate task-specific abilities. To do so, we examined individual differences in spatial and nonspatial conditional and distributional statistical learning. Participants completed four visual statistical learning tasks: conditional spatial, conditional nonspatial, distributional spatial, and distributional nonspatial. Performance on all four tasks significantly correlated with each other, and performance on all tasks accounted for a large portion of the variance across tasks (57%). Interestingly, a portion of the variance of task performance (between 11% and 18%) was also accounted for by performance on each of the individual tasks. Our results suggest that visual statistical learning is the result of the interplay between a unified mechanism for extracting conditional and distributional statistical regularities across time and space, and an individual’s ability to extract specific types of regularities.

Original languageAmerican English
Pages (from-to)1291-1299
Number of pages9
JournalPsychonomic Bulletin and Review
Volume27
Issue number6
DOIs
StatePublished - Dec 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020, The Psychonomic Society, Inc.

Keywords

  • Domain-general mechanisms
  • Individual differences
  • Modality specificity
  • Psychometrics
  • Statistical learning

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