A Conceptual Framework Of Cognitiveaffective Theory Of Mind Towards A Precision Identification Of Mental Disorders Peng Zhou Huimin Ma Bochao Zou Xiaowen Zhang Shuyan Zhao Yuxin Lin Yidong Wang Lei Feng Gang Wang by Peng Zhou & Huimin Ma & Bochao Zou & Xiaowen Zhang & Shuyan Zhao & Yuxin Lin & Yidong Wang & Lei Feng & Gang Wang instant download after payment.

To explore the minds of others, which is traditionally referred to as Theory of Mind (ToM), is perhaps the most fundamental ability ofhumans as social beings. Impairments in ToM could lead to difficulties or even deficits in social interaction. The present studyfocuses on two core components of ToM, the ability to infer others’ beliefs and the ability to infer others’ emotions, which we referto as cognitive and affective ToM respectively. Charting both typical and atypical trajectories underlying the cognitive-affective ToMpromises to shed light on the precision identification of mental disorders, such as depressive disorders (DD) and autism spectrumdisorder (ASD). However, most prior studies failed to capture the underlying processes involved in the cognitive-affective ToM in afine-grained manner. To address this problem, we propose an innovative conceptual framework, referred to as visual theory ofmind (V-ToM), by constructing visual scenes with emotional and cognitive meanings and by depicting explicitly a four-stageprocess of how humans make inferences about the beliefs and emotions of others. Through recording individuals’ eye movementswhile looking at the visual scenes, our model enables us to accurately measure each stage involved in the computation of1234567890():,;cognitive-affective ToM, thereby allowing us to infer about potential difficulties that might occur in each stage. Our model is basedon 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 ofconventional questionnaire-based assessment, and therefore could serve as valuable references for mental health applicationsbased on AI-aided digital medicine.npj Mental Health Research (2023) 2:12 ;