Impaired adaptation of learning to contingency volatility in internalizing psychopathology

eLife

Ondrej Zika¹  Christopher Gagne²  Sonia J Bishop³  Peter Dayan⁴ 

[1] Max Planck Institute for Biological Cybernetics, Tübingen, Germany;Department of Psychology, UC Berkeley, Berkeley, United States;Max Planck Institute for Biological Cybernetics, Tübingen, Germany;Max Planck Institute for Human Development, Berlin, Germany;

关键词: computational psychiatry;    anxiety;    depression;    decision making;    reinforcement learning;    uncertainty;   

DOI  :  10.7554/eLife.61387

来源: DOAJ

【 摘 要 】

Using a contingency volatility manipulation, we tested the hypothesis that difficulty adapting probabilistic decision-making to second-order uncertainty might reflect a core deficit that cuts across anxiety and depression and holds regardless of whether outcomes are aversive or involve reward gain or loss. We used bifactor modeling of internalizing symptoms to separate symptom variance common to both anxiety and depression from that unique to each. Across two experiments, we modeled performance on a probabilistic decision-making under volatility task using a hierarchical Bayesian framework. Elevated scores on the common internalizing factor, with high loadings across anxiety and depression items, were linked to impoverished adjustment of learning to volatility regardless of whether outcomes involved reward gain, electrical stimulation, or reward loss. In particular, high common factor scores were linked to dampened learning following better-than-expected outcomes in volatile environments. No such relationships were observed for anxiety- or depression-specific symptom factors.

【 授权许可】

Unknown