Measuring individual differences in statistical learning: Current pitfalls and possible solutions

Noam Siegelman*, Louisa Bogaerts, Ram Frost

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

141 Scopus citations

Abstract

Most research in statistical learning (SL) has focused on the mean success rates of participants in detecting statistical contingencies at a group level. In recent years, however, researchers have shown increased interest in individual abilities in SL, either to predict other cognitive capacities or as a tool for understanding the mechanism underlying SL. Most if not all of this research enterprise has employed SL tasks that were originally designed for group-level studies. We argue that from an individual difference perspective, such tasks are psychometrically weak, and sometimes even flawed. In particular, the existing SL tasks have three major shortcomings: (1) the number of trials in the test phase is often too small (or, there is extensive repetition of the same targets throughout the test); (2) a large proportion of the sample performs at chance level, so that most of the data points reflect noise; and (3) the test items following familiarization are all of the same type and an identical level of difficulty. These factors lead to high measurement error, inevitably resulting in low reliability, and thereby doubtful validity. Here we present a novel method specifically designed for the measurement of individual differences in visual SL. The novel task we offer displays substantially superior psychometric properties. We report data regarding the reliability of the task and discuss the importance of the implementation of such tasks in future research.

Original languageAmerican English
Pages (from-to)418-432
Number of pages15
JournalBehavior Research Methods
Volume49
Issue number2
DOIs
StatePublished - 1 Apr 2017

Bibliographical note

Publisher Copyright:
© 2016, Psychonomic Society, Inc.

Keywords

  • Individual differences
  • Psychometrics
  • Statistical learning

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