The Efficacy of Conversational Artificial Intelligence in Rectifying the Theory of Mind and Autonomy Biases. Data & Protocol

The study evaluates the efficacy of Conversational Artificial Intelligence (CAI) in rectifying cognitive biases and recognizing affect in human-AI interactions, which is crucial for digital mental health interventions. Cognitive biases—systematic deviations from normative thinking—affect mental health, intensifying conditions like depression and anxiety. The research employs a structured methodology with clinical-based virtual case scenarios simulating typical user-bot interactions. Performance and affect recognition were assessed across two categories of cognitive biases: theory of mind biases (anthropomorphization of AI, overtrust in AI, attribution to AI) and autonomy biases (illusion of control, fundamental attribution error, just-world hypothesis). A qualitative feedback mechanism was used with an ordinal scale to quantify responses based on accuracy, therapeutic quality, and adherence to CBT principles.

本研究评估了会话式人工智能（Conversational Artificial Intelligence，CAI）在人机交互中矫正认知偏差与识别情感状态的效能，该效能对于数字化心理健康干预而言至关重要。
认知偏差——即偏离规范思维的系统性偏差——会损害心理健康，加重抑郁、焦虑等精神疾患的症状。
本研究采用结构化研究方法，搭建基于临床场景的虚拟案例情境，以模拟典型的用户-聊天机器人交互过程。
研究针对两类认知偏差展开性能表现与情感识别能力评估：其一为心理理论偏差，涵盖人工智能拟人化、对AI过度信任、行为归因于AI三类子偏差；其二为自主性偏差，涵盖控制错觉、基本归因错误（fundamental attribution error）、公平世界谬误（just-world hypothesis）三类子偏差。
本研究采用质性反馈机制，结合序数量表，以交互准确率、治疗质量及对认知行为疗法（Cognitive Behavioral Therapy，CBT）原则的依从性为量化维度，对反馈结果进行量化分析。