Towards a theory of individual differences in statistical learning

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

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

In recent years, statistical learning (SL) research has seen a growing interest in tracking individual performance in SL tasks, mainly as a predictor of linguistic abilities. We review studies from this line of research and outline three presuppositions underlying the experimental approach they employ: (i) that SL is a unified theoretical construct; (ii) that current SL tasks are interchangeable, and equally valid for assessing SL ability; and (iii) that performance in the standard forced-choice test in the task is a good proxy of SL ability. We argue that these three critical presuppositions are subject to a number of theoretical and empirical issues. First, SL shows patterns of modality- and informational specificity, suggesting that SL cannot be treated as a unified construct. Second, different SL tasks may tap into separate sub-components of SL that are not necessarily interchangeable. Third, the commonly used forced-choice tests in most SL tasks are subject to inherent limitations and confounds. As a first step, we offer a methodological approach that explicitly spells out a potential set of different SL dimensions, allowing for better transparency in choosing a specific SL task as a predictor of a given linguistic outcome. We then offer possible methodological solutions for better tracking and measuring SL ability. Taken together, these discussions provide a novel theoretical and methodological approach for assessing individual differences in SL, with clear testable predictions.

Original languageEnglish
Article number20160059
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Volume372
Issue number1711
DOIs
Publication statusPublished - 5 Jan 2017
Externally publishedYes

Keywords

  • Individual differences
  • Online measures
  • Predicting linguistic abilities
  • Statistical learning

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