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Searching for an anchor in an unpredictable world: A computational model of obsessive compulsive disorder.
Psychological Review ( IF 5.1 ) Pub Date : 2020-02-27 , DOI: 10.1037/rev0000188
Isaac Fradkin 1 , Rick A Adams 2 , Thomas Parr 3 , Jonathan P Roiser 4 , Jonathan D Huppert 1
Affiliation  

In this article, we develop a computational model of obsessive-compulsive disorder (OCD). We propose that OCD is characterized by a difficulty in relying on past events to predict the consequences of patients' own actions and the unfolding of possible events. Clinically, this corresponds both to patients' difficulty in trusting their own actions (and therefore repeating them), and to their common preoccupation with unlikely chains of events. Critically, we develop this idea on the basis of the well-developed framework of the Bayesian brain, where this impairment is formalized as excessive uncertainty regarding state transitions. We illustrate the validity of this idea using quantitative simulations and use these to form specific empirical predictions. These predictions are evaluated in relation to existing evidence, and are used to delineate directions for future research. We show how seemingly unrelated findings and phenomena in OCD can be explained by the model, including a persistent experience that actions were not adequately performed and a tendency to repeat actions; excessive information gathering (i.e., checking); indecisiveness and pathological doubt; overreliance on habits at the expense of goal-directed behavior; and overresponsiveness to sensory stimuli, thoughts, and feedback. We discuss the relationship and interaction between our model and other prominent models of OCD, including models focusing on harm-avoidance, not-just-right experiences, or impairments in goal-directed behavior. Finally, we outline potential clinical implications and suggest lines for future research. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

中文翻译:

在不可预测的世界中寻找锚:强迫症的计算模型。

在本文中,我们开发了一个强迫症 (OCD) 的计算模型。我们认为强迫症的特点是难以依靠过去的事件来预测患者自己行为的后果和可能发生的事件。在临床上,这既对应于患者难以相信自己的行为(并因此重复它们),也对应于他们对不太可能发生的事件链的普遍关注。至关重要的是,我们在贝叶斯大脑的完善框架的基础上发展了这个想法,其中这种损害被形式化为关于状态转换的过度不确定性。我们使用定量模拟来说明这个想法的有效性,并使用这些来形成特定的经验预测。这些预测是根据现有证据进行评估的,并用于描绘未来研究的方向。我们展示了模型如何解释强迫症中看似无关的发现和现象,包括行为没有得到充分执行的持续体验和重复行为的倾向;过度的信息收集(即检查);优柔寡断和病理性怀疑;以牺牲目标导向的行为为代价过度依赖习惯;以及对感官刺激、想法和反馈的过度反应。我们讨论了我们的模型与其他突出的强迫症模型之间的关系和相互作用,包括专注于避免伤害、不恰到好处的体验或目标导向行为障碍的模型。最后,我们概述了潜在的临床意义,并为未来的研究提出了建议。(PsycINFO 数据库记录 (c) 2020 APA,保留所有权利)。
更新日期:2020-02-27
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