Time for Consensus: Open Challenges and Recommendations for Research on Individual Differences in Statistical Learning
Year: 2025
Type: preprint
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, and issues of measurement reliability and statistical power. Based on these discussions, we outline eight 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.
Authors: Louisa Bogaerts, Davide Crepaldi, Haoyu Zhou, Ana Bautista, Kevin Brown, Li-Yun Wendy Chang, Ruth de Diego-Balaguer, Julia Hofweber, Erin Isbilen, Marc F Joanisse, Ferenc Kemeny, Panagiotis Kenanidis, Krisztina Sára Lukics, Mireia Marimon, Alice Milne, Dezso Nemeth, Yngwie A. Nielsen, Catia M Oliveira, Zhenghan Qi, jinglei ren, Jay Rueckl, Claudia Ruzza, Christophe Vanhouwe, Arpitha Vasudevamurthy, Denise H. Wu, Puyuan Zhang, Noam Siegelman
Institutions: Ghent University, University of Maryland, College Park
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4414380753
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