Peng Zhou, Huimin Ma, Bochao Zou, Xiaowen Zhang, Shuyan Zhao, Yuxin Lin, Yidong Wang, Lei Feng, Gang Wang
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引用次数: 0
摘要
探索他人的思想,即传统上所说的心智理论(ToM),也许是人类作为社会人最基本的能力。心智图式(ToM)的缺陷可能会导致社会交往的困难甚至缺陷。本研究的重点是心智理论的两个核心组成部分,即推断他人信念的能力和推断他人情感的能力,我们分别称之为认知心智理论和情感心智理论。绘制认知-情感ToM的典型和非典型轨迹有望为精神障碍(如抑郁障碍(DD)和自闭症谱系障碍(ASD))的精确识别提供启示。然而,之前的大多数研究都未能以精细的方式捕捉到认知-情感ToM所涉及的潜在过程。为了解决这个问题,我们提出了一个创新的概念框架,即视觉心智理论(V-ToM),通过构建具有情感和认知意义的视觉场景,明确描述人类如何对他人的信念和情感做出推断的四个阶段过程。通过记录个体在观看视觉场景时的眼球运动,我们的模型使我们能够精确测量认知-情感 ToM 计算过程中的每个阶段,从而推断出每个阶段可能出现的困难。我们的模型基于一个大样本量(700 人)和一个新颖的视听范式,使用的是包含认知-情感意义的视觉场景。在此,我们报告了在健康对照组、DD 和 ASD 患者中获得的差异特征,这些特征克服了传统问卷评估的主观性,因此可作为基于人工智能辅助数字医学的心理健康应用的宝贵参考。
A conceptual framework of cognitive-affective theory of mind: towards a precision identification of mental disorders
To explore the minds of others, which is traditionally referred to as Theory of Mind (ToM), is perhaps the most fundamental ability of humans as social beings. Impairments in ToM could lead to difficulties or even deficits in social interaction. The present study focuses on two core components of ToM, the ability to infer others’ beliefs and the ability to infer others’ emotions, which we refer to as cognitive and affective ToM respectively. Charting both typical and atypical trajectories underlying the cognitive-affective ToM promises to shed light on the precision identification of mental disorders, such as depressive disorders (DD) and autism spectrum disorder (ASD). However, most prior studies failed to capture the underlying processes involved in the cognitive-affective ToM in a fine-grained manner. To address this problem, we propose an innovative conceptual framework, referred to as visual theory of mind (V-ToM), by constructing visual scenes with emotional and cognitive meanings and by depicting explicitly a four-stage process of how humans make inferences about the beliefs and emotions of others. Through recording individuals’ eye movements while looking at the visual scenes, our model enables us to accurately measure each stage involved in the computation of cognitive-affective ToM, thereby allowing us to infer about potential difficulties that might occur in each stage. Our model is based on a large sample size (n > 700) and a novel audio-visual paradigm using visual scenes containing cognitive-emotional meanings. Here we report the obtained differential features among healthy controls, DD and ASD individuals that overcome the subjectivity of conventional questionnaire-based assessment, and therefore could serve as valuable references for mental health applications based on AI-aided digital medicine.