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 journalArticlepeer-review

20 Scopus citations

Abstract

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 languageAmerican English
Article number104002
JournalCognition
Volume192
DOIs
StatePublished - Nov 2019

Bibliographical note

Funding Information:
This paper was supported by the ERC Advanced grant awarded to Ram Frost (project 692502-L2STAT), and the Israel Science Foundation (Grant 217/14 awarded to Ram Frost), and NSERC grant DG-502584 to Blair Armstrong. Noam Siegelman is a Rothschild Yad-Hanadiv post-doctoral fellow. Louisa Bogaerts received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No. 743528 (IF-EF), at the Hebrew University. We wish to thank Per-li Piro for her help in data collection. We also thank Amy Perfors and an anonymous reviewer for their helpful comments.

Funding Information:
This paper was supported by the ERC Advanced grant awarded to Ram Frost (project 692502-L2STAT), and the Israel Science Foundation (Grant 217/14 awarded to Ram Frost), and NSERC grant DG-502584 to Blair Armstrong. Noam Siegelman is a Rothschild Yad-Hanadiv post-doctoral fellow. Louisa Bogaerts received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant Agreement No. 743528 (IF-EF), at the Hebrew University. We wish to thank Per-li Piro for her help in data collection. We also thank Amy Perfors and an anonymous reviewer for their helpful comments.

Publisher Copyright:
© 2019 Elsevier B.V.

Keywords

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

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