Time for Consensus: Open Challenges and Recommendations for Research on Individual Differences in Statistical Learning

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Abstract
Statistical learning (SL), the ability to extract regularities from sensory input, is hypothesized to play a major role in many aspects of higher-level cognition. While earlier investigations centered on group-level effects, a growing body of research now focuses on individual differences in this ability. This consensus paper presents the collective insights of 27 researchers worldwide who are active in the field of individual differences in SL, with the goal of reflecting on the field’s progress so far while outlining key challenges going forward. Our discussion covers possible theoretical and operational definitions of SL, the trade-offs between controlled laboratory tasks and ecologically valid paradigms, considerations in interpreting correlations between SL performance and other cognitive abilities, issues of measurement reliability and statistical power, and rationales for adopting individual-differences versus group-level approaches. Based on these discussions, we outline nine actionable recommendations for the field to move forward, which are meant to lead to a new body of evidence that is more theoretically informed and more methodologically robust.