arXiv 2511.201792026-08-20q-bio.NCcs.AIcs.HC

人机交互预测心理健康

Human-computer interactions predict mental health

Helen Wills Neuroscience Institute, University of California Berkeley, USA（加州大学伯克利分校海伦·威尔斯神经科学研究所）；Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK（马克斯·普朗克伦敦大学学院计算精神病学与衰老研究中心）；Department of Psychology, University of California Berkeley, USA（加州大学伯克利分校心理学系）；Vision Science Group, University of California Berkeley, USA（加州大学伯克利分校视觉科学组）

Veith Weilnhammer, Jefferson Ortega, David Whitney

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AI总结：

研究通过 MAILA 框架利用日常人机交互数据预测心理健康状态，实现高精度的数字表型分析。

摘要：

可扩展的抑郁症评估仍是实现可及且公平的医疗护理的关键障碍。本文显示，日常人机交互编码心理健康具有生物标志物的准确性。我们介绍了 MAILA，一种用于从数字活动推断潜在心理状态的机器学习框架。我们训练 MAILA 在 18,200 个光标和触屏记录上，这些记录标记有 130 万份心理健康自我报告，来自 9,500 名参与者。MAILA 沿 13 个临床相关维度追踪动态心理状态，解决昼夜波动和实验操纵的唤醒和效价，实现群体层面的接近天花板精度，并捕捉仅部分反映在口头自我报告中的心理健康信息。通过提取迄今为止未被利用的心理功能签名，MAILA 建立了人机交互作为可扩展数字心理健康表型分析的新模态。

英文摘要：

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.

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