Oversampled and undersolved: Depressive rumination from an active inference perspective

沉思 心理学 认知心理学 认知 心情 推论 习得的无助感 透视图(图形) 因果推理 发展心理学 社会心理学 临床心理学 计算机科学 人工智能 精神科 经济 计量经济学
作者
Max Berg,Matthias Feldmann,Lukas Kirchner,Tobias Kube
出处
期刊:Neuroscience & Biobehavioral Reviews [Elsevier BV]
卷期号:142: 104873-104873 被引量:16
标识
DOI:10.1016/j.neubiorev.2022.104873
摘要

Rumination is a widely recognized cognitive deviation in depression. Despite the recognition, researchers have struggled to explain why patients cannot disengage from the process, although it depresses their mood and fails to lead to effective problem-solving. We rethink rumination as repetitive but unsuccessful problem-solving attempts. Appealing to an active inference account, we suggest that adaptive problem-solving is based on the generation, evaluation, and performance of candidate policies that increase an organism's knowledge of its environment. We argue that the problem-solving process is distorted during rumination. Specifically, rumination is understood as engaging in excessive yet unsuccessful oversampling of policy candidates that do not resolve uncertainty. Because candidates are sampled from policies that were selected in states resembling one's current state, "bad" starting points (e.g., depressed mood, physical inactivity) make the problem-solving process vulnerable for generating a ruminative "halting problem". This problem leads to high opportunity costs, learned helplessness and diminished overt behavior. Besides reviewing evidence for the conceptual paths of this model, we discuss its neurophysiological correlates and point towards clinical implications.

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