Individual differences in probabilistic learning and updating predictive representations in individuals with obsessive-compulsive tendencies.

BACKGROUND: Obsessive-compulsive (OC) tendencies involve intrusive thoughts and rigid, repetitive behaviours that also manifest at the subclinical level in the general population. The neurocognitive factors driving the development and persistence of the excessive presence of these tendencies remain highly elusive, though emerging theories emphasize the role of implicit information processing. Despite various empirical studies on distinct neurocognitive processes, the incidental retrieval of environmental structures in dynamic and noisy environments, such as probabilistic learning, has received relatively little attention.

METHODS: In this study, we aimed to unravel potential individual differences in implicit probabilistic learning and the updating of predictive representations related to OC tendencies in a non-clinical population. We conducted two independent online experiments (NStudy1 = 164, NStudy2 = 256) with university students. Probabilistic learning was assessed using an implicit visuomotor probabilistic learning task, involving sequences with second-order non-adjacent dependencies.

RESULTS: Our findings revealed that implicit probabilistic learning remained remarkably robust among OC tendencies within a non-clinical population. Furthermore, the results highlighted effective updating capabilities of predictive representations, which were not influenced by OC tendencies.

CONCLUSIONS: These results offer new insights into individual differences in probabilistic learning and updating in relation to OC tendencies, contributing to theoretical, methodological, and practical approaches for understanding the maladaptive behavioural manifestations of OC disorder and subclinical tendencies.