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. Therapeutic chatbots can make cognitive-behavioral therapy (CBT) more accessible and affordable, offering scalable and immediate support. 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. Therapeutic bots (Wysa, Youper) and general-use LLMs (GTP 3.5, GTP 4, Gemini Pro) were evaluated through scripted interactions, double-reviewed by cognitive scientists and a clinical psychologist. Statistical analysis showed therapeutic bots were consistently outperformed by non-therapeutic bots in bias rectification and in 4 out of 6 biases in affect recognition. The data suggests that non-therapeutic chatbots are more effective in addressing some cognitive biases.

翻译：本研究评估了对话式人工智能（CAI）在纠正认知偏见和识别人机交互中情感方面的效能，这对于数字心理健康干预至关重要。认知偏见（系统性偏离规范性思维）影响心理健康，会加剧抑郁和焦虑等状况。治疗性聊天机器人可以使认知行为疗法（CBT）更易获得且更经济，提供可扩展的即时支持。该研究采用结构化方法，基于临床虚拟案例场景模拟典型的用户-机器人交互。研究评估了性能和情感识别在两类认知偏见中的表现：心理理论偏见（对AI的拟人化、对AI的过度信任、对AI的归因）和自主性偏见（控制错觉、基本归因错误、公正世界假说）。研究采用定性反馈机制和序数量表，基于准确性、治疗质量和CBT原则依从性对回答进行量化。通过脚本化交互对治疗性机器人（Wysa, Youper）和通用大语言模型（GPT 3.5, GPT 4, Gemini Pro）进行评估，并由认知科学家和临床心理学家进行双重评审。统计分析显示，在偏见纠正方面，以及在情感识别的6种偏见中的4种上，治疗性机器人的表现持续逊色于非治疗性机器人。数据表明，非治疗性聊天机器人在应对某些认知偏见方面更为有效。