Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

Noam Siegelman*, Louisa Bogaerts, Ofer Kronenfeld, Ram Frost

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

76 Scopus citations

Abstract

From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible “statistical” properties that are the object of learning. Much less attention has been given to defining what “learning” is in the context of “statistical learning.” One major difficulty is that SL research has been monitoring participants’ performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL.

Original languageAmerican English
Pages (from-to)692-727
Number of pages36
JournalCognitive Science
Volume42
DOIs
StatePublished - Jun 2018

Bibliographical note

Publisher Copyright:
Copyright © 2017 Cognitive Science Society, Inc.

Keywords

  • Individual differences
  • Learning dynamics
  • Online measures
  • Statistical learning

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