Computational Mechanisms of Addiction and Anxiety: A Developmental Perspective

Noam Goldway, Eran Eldar, Gal Shoval, Catherine A. Hartley*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

A central goal of computational psychiatry is to identify systematic relationships between transdiagnostic dimensions of psychiatric symptomatology and the latent learning and decision-making computations that inform individuals’ thoughts, feelings, and choices. Most psychiatric disorders emerge prior to adulthood, yet little work has extended these computational approaches to study the development of psychopathology. Here, we lay out a roadmap for future studies implementing this approach by developing empirically and theoretically informed hypotheses about how developmental changes in model-based control of action and Pavlovian learning processes may modulate vulnerability to anxiety and addiction. We highlight how insights from studies leveraging computational approaches to characterize the normative developmental trajectories of clinically relevant learning and decision-making processes may suggest promising avenues for future developmental computational psychiatry research.

Original languageEnglish
Pages (from-to)739-750
Number of pages12
JournalBiological Psychiatry
Volume93
Issue number8
DOIs
StatePublished - 15 Apr 2023

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