通过用户操作行为预测心理状态，实现无感式心理健康评估。
Human-computer interactions predict mental health
- 用机器学习分析鼠标和触摸屏操作数据，推断13维心理状态。
- 在9500人、130万份自评数据上验证，能捕捉动态变化与实验刺激反应。
- 可补充传统问卷盲区，提升大模型对心理状态的判断能力。
大规模、可扩展的心理健康评估仍是实现可及性与公平医疗的关键障碍。本文表明，日常人机交互行为蕴含关于自我报告的心理困扰与幸福感的高维信息。我们提出MAILA——一种从数字活动推断潜在心理状态的机器学习框架。该模型基于9,500名参与者提供的18,200段鼠标与触屏记录，以及130万份心理健康的自评数据进行训练。MAILA能够预测13个维度的心理困扰与幸福感动态变化，检测个体内部随时间的变化，并识别出实验诱发的情绪唤醒与效价波动。在群体层面，MAILA以高保真度还原了人口统计学特征与时段差异对心理健康的反映。此外，其捕捉到的信息部分未被言语自评覆盖；在合成概念验证中，显著提升了前沿大语言模型对用户心理状态的推断能力。MAILA首次实现了人机交互行为编码多维、可迁移心理健康信息的大规模系统性实证证明。
原文摘要 · Abstract (English)
Scalable assessments of mental illness remain a critical roadblock toward accessible and equitable care. Here, we show that everyday human-computer interactions encode high-dimensional information about self-reported psychological distress and wellbeing. We introduce MAILA, a MAchine-learning framework for Inferring Latent mental states from digital Activity. We trained MAILA on 18,200 cursor and touchscreen recordings labeled with 1.3 million mental-health self-reports collected from 9,500 participants. MAILA predicts dynamic mental states along 13 dimensions of distress and wellbeing, detects within-person changes over time, and resolves experimentally induced fluctuations in experienced arousal and valence. At the group level, MAILA recovers demographic and time-of-day patterns in self-reported mental health with high fidelity. MAILA also captures information only partially reflected in verbal self-report and, in a synthetic proof of concept, improves the ability of a frontier large language model to infer user mental health. By extracting signatures of psychological function that have so far remained untapped, MAILA provides the first large-scale, systematic proof of principle that human-computer interactions encode multidimensional and transferable information about mental health.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。