Here, we take a computational approach to understand the mechanisms underlying face perception biases in depression. Thirty participants diagnosed with major depressive disorder and 30 healthy control participants took part in three studies involving recognition of identity and emotion in faces. We used signal detection theory to determine whether any perceptual biases exist in depression aside from decisional biases. We found lower sensitivity to happiness in general, and lower sensitivity to both happiness and sadness with ambiguous stimuli. Our use of highly-controlled face stimuli ensures that such asymmetry is truly perceptual in nature, rather than the result of studying expressions with inherently different discriminability. We found no systematic effect of depression on the perceptual interactions between face expression and identity. We also found that decisional strategies used in our task were different for people with depression and controls, but in a way that was highly specific to the stimulus set presented. We show through simulation that the observed perceptual effects, as well as other biases found in the literature, can be explained by a computational model in which channels encoding positive expressions are selectively suppressed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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对抑郁症中面部感知偏差机制的计算解释。

在这里，我们采用计算方法来了解抑郁症中面部感知偏差的潜在机制。30 名被诊断患有重度抑郁症的参与者和 30 名健康对照组参与者参加了三项涉及识别面部身份和情绪的研究。我们使用信号检测理论来确定除了决策偏差之外，抑郁症中是否存在任何感知偏差。我们发现总体上对幸福的敏感性较低，对含糊刺激的幸福和悲伤的敏感性较低。我们对高度控制的面部刺激的使用确保了这种不对称性本质上是真正的感知性，而不是研究具有固有不同可辨别性的表情的结果。我们发现抑郁症对面部表情和身份之间的感知相互作用没有系统性影响。我们还发现，我们的任务中使用的决策策略对于患有抑郁症和控制的人来说是不同的，但其方式对所呈现的刺激集具有高度特异性。我们通过模拟表明，观察到的感知效应以及文献中发现的其他偏差可以通过一个计算模型来解释，其中编码正表达的通道被选择性地抑制。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。可以通过一个计算模型来解释，其中编码正表达的通道被选择性地抑制。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。可以通过一个计算模型来解释，其中编码正表达的通道被选择性地抑制。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。