Allies, Rivals, or Strangers? Exploring the Relationship Between Statistical Learning and Executive Functions

Poster Session F - Tuesday, March 10, 2026, 8:00 – 10:00 am PDT, Fairview/Kitsilano Ballrooms

Eszter Tóth-Fáber^1,2 (toth-faber.eszter@ttk.hu), Bence C. Farkas^3,4, Anna Boglárka Kocsis⁵, Orsolya Pesthy^6,1, Bianka Brezóczki^7,1,2, Andrea Kóbor¹, Karolina Janacsek^8,2, Dezső Németh^6,9,10; ¹Institute of Cognitive Neuroscience and Psychology, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary, ²Institute of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary, ³UVSQ, Inserm, Centre de Recherche en Epidémiologie et Santé des Populations, Université Paris-Saclay, ⁴LNC2, Département d’études Cognitives, École Normale Supérieure, INSERM, PSL Research University, ⁵Brain Imaging Centre, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary, ⁶Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, INSERM, CNRS, Université Claude Bernard Lyon 1, Bron, France, ⁷Doctoral School of Psychology, ELTE Eötvös Loránd University, Budapest, Hungary, ⁸Centre for Thinking and Learning, Institute for Lifecourse Development, School of Human Sciences, Faculty of Education, Health and Human Sciences, University of Greenwich, London, UK, ⁹BML-NAP Research Group, ELTE Eötvös Loránd University & HUN-REN Research Centre for Natural Sciences, Budapest, Hungary, ¹⁰Department of Education and Psychology, Faculty of Social Sciences, University of Atlántico Medio, Las Palmas de Gran Canaria, Spain

Human behavior relies on the interaction between automatic and controlled processes. Statistical learning (SL) is often considered an automatic mechanism supporting the extraction of environmental regularities, whereas executive functions (EFs) are associated with controlled, goal-directed processing. However, their functional relationship remains debated: SL and EFs may operate independently, cooperate, or compete during learning. In the present study, we investigated the interplay between SL and EFs in a large young adult sample (N = 192) using a multi-task, data-driven approach. Participants completed seven SL tasks, including measures of learning during ongoing performance (online reaction time tasks) and after learning (offline two-alternative forced-choice paradigms), alongside a battery of EF tasks. Factor analysis of EF measures revealed two latent factors: a working-memory–dominant EF factor and a verbal fluency factor. These factors were entered into a structural equation model (SEM) to examine their associations with SL performance. Results showed that the working-memory–dominant EF factor was positively associated with performance on two-alternative forced-choice SL tasks and the Weather Prediction task. In contrast, the same EF factor showed a trend-level negative association with the Alternating Serial Reaction time task which captures learning online during continuous performance. The verbal fluency factor was not related to SL performance in any task. These findings suggest that the relationship between executive functions and statistical learning depends on how learning is expressed and measured. Executive control may support performance when learning is assessed offline, while potentially interfering with online learning processes.

Topic Area: LONG-TERM MEMORY: Skill Learning