What exactly is learned in visual statistical learning? Insights from Bayesian modeling

Noam Siegelman*, Louisa Bogaerts, Blair C. Armstrong, Ram Frost

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


It is well documented that humans can extract patterns from continuous input through Statistical Learning (SL) mechanisms. The exact computations underlying this ability, however, remain unclear. One outstanding controversy is whether learners extract global clusters from the continuous input, or whether they are tuned to local co-occurrences of pairs of elements. Here we adopt a novel framework to address this issue, applying a generative latent-mixture Bayesian model to data tracking SL as it unfolds online using a self-paced learning paradigm. This framework not only speaks to whether SL proceeds through computations of global patterns versus local co-occurrences, but also reveals the extent to which specific individuals employ these computations. Our results provide evidence for inter-individual mixture, with different reliance on the two types of computations across individuals. We discuss the implications of these findings for understanding the nature of SL and individual-differences in this ability.

Original languageEnglish
Article number104002
Publication statusPublished - 1 Nov 2019
Externally publishedYes


  • Bayesian modeling
  • Individual differences
  • Online measures
  • Statistical learning


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