Disrupted state transition learning as a computational marker of compulsivity

Paul B. Sharp*, Raymond J. Dolan, Eran Eldar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Background Disorders involving compulsivity, fear, and anxiety are linked to beliefs that the world is less predictable. We lack a mechanistic explanation for how such beliefs arise. Here, we test a hypothesis that in people with compulsivity, fear, and anxiety, learning a probabilistic mapping between actions and environmental states is compromised. Methods In Study 1 (n = 174), we designed a novel online task that isolated state transition learning from other facets of learning and planning. To determine whether this impairment is due to learning that is too fast or too slow, we estimated state transition learning rates by fitting computational models to two independent datasets, which tested learning in environments in which state transitions were either stable (Study 2: n = 1413) or changing (Study 3: n = 192). Results Study 1 established that individuals with higher levels of compulsivity are more likely to demonstrate an impairment in state transition learning. Preliminary evidence here linked this impairment to a common factor comprising compulsivity and fear. Studies 2 and 3 showed that compulsivity is associated with learning that is too fast when it should be slow (i.e. when state transition are stable) and too slow when it should be fast (i.e. when state transitions change). Conclusions Together, these findings indicate that compulsivity is associated with a dysregulation of state transition learning, wherein the rate of learning is not well adapted to the task environment. Thus, dysregulated state transition learning might provide a key target for therapeutic intervention in compulsivity.

Original languageAmerican English
Pages (from-to)2095-2105
Number of pages11
JournalPsychological Medicine
Volume53
Issue number5
DOIs
StatePublished - 24 Apr 2023

Bibliographical note

Publisher Copyright:
Copyright © The Author(s), 2021. Published by Cambridge University Press.

Keywords

  • Anxiety
  • compulsivity
  • computational modelling
  • model-based learning
  • transdiagnostic

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