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）在人机交互场景中纠正认知偏差、识别情绪状态的效能，该研究方向对于数字化心理健康干预而言至关重要。认知偏差，即偏离规范思维模式的系统性偏差，会对心理健康造成负面影响，加剧抑郁、焦虑等心理病症。本研究采用基于临床场景的结构化研究方法，通过虚拟案例模拟典型的用户-智能体交互过程。研究从两类认知偏差维度，对模型的交互性能与情绪识别能力进行了评估：其一为心理理论偏差（theory of mind biases），涵盖AI拟人化、对AI过度信任、将行为归因于AI三个子类别；其二为自主性偏差（autonomy biases），涵盖控制错觉、基本归因错误、公平世界假说三个子类别。本研究采用序数量表构建质性反馈机制，基于准确率、治疗质量与认知行为疗法（Cognitive Behavioral Therapy，CBT）原则依从性对反馈结果进行量化分析。