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Cognitive restructuring is a well-established mental health technique for amending automatic thoughts, which are distorted and biased beliefs about a situation, into objective and balanced thoughts. Since virtual agents can be used anytime and anywhere, they are expected to perform cognitive restructuring without being influenced by medical infrastructure or patients' stigma toward mental illness. Unfortunately, since the quantitative analysis of human-agent interaction is still insufficient, the effect on the user's cognitive state remains unclear. We collected interaction data between virtual agents and users to observe the mood improvements associated with changes in automatic thoughts that occur in user cognition and addressed the following two points: (1) implementation of a virtual agent that helps a user identify and evaluate automatic thoughts; (2) identification of the relationship between a user's facial expressions and the extent of the mood improvement subjectively felt by users during the human-agent interaction. We focus on these points because cognitive restructuring by a human therapist starts by identifying automatic thoughts and seeking sufficient evidence to find balanced thoughts (evaluation of automatic thoughts). Therapists also use such non-verbal behaviors as facial expressions to detect changes in a user's mood, which is an important indicator for guidance. Based on the results of this analysis, we provide a technical guidance framework that fully automates the identification and evaluation of automatic thoughts to achieve a virtual agent that can interact with users by taking into account their verbal and non-verbal behaviors in face-to-face situations. This research supports the possibility of improving the effectiveness of mental health care in cognitive restructuring using virtual agents.
Shidara et al. (Wed,) studied this question.