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Human-computer interactions predict mental health
arXiv:2511.20179v2 Announce Type: replace-cross
Abstract: Scalable assessments of mental illness, the leading driver of disability worldwide, remain a critical roadblock toward accessible and equitable care. Here, we show that human-computer interactions encode mental health with state-of-the-art biomarker precision. We introduce MAILA, a MAchine-learning framework for Inferring Latent mental states from digital Activity. We trained MAILA to predict 1.3 million mental-health self-reports from 20,000 cursor and touchscreen recordings recorded in 9,000 online participants. The dataset includes 2,000 individuals assessed longitudinally, 1,500 diagnosed with depression, and 500 with obsessive-compulsive disorder. MAILA tracks dynamic mental states along three orthogonal dimensions, identifies individuals living with mental illness, and achieves near-ceiling accuracy when predicting group-level mental health. By extracting non-verbal signatures of psychological function that have so far remained untapped, MAILA represents a key step toward foundation models for mental health. The ability to decode mental states at zero marginal cost creates new opportunities in neuroscience, medicine, and public health, while raising urgent questions about privacy, agency, and autonomy online.
Abstract: Scalable assessments of mental illness, the leading driver of disability worldwide, remain a critical roadblock toward accessible and equitable care. Here, we show that human-computer interactions encode mental health with state-of-the-art biomarker precision. We introduce MAILA, a MAchine-learning framework for Inferring Latent mental states from digital Activity. We trained MAILA to predict 1.3 million mental-health self-reports from 20,000 cursor and touchscreen recordings recorded in 9,000 online participants. The dataset includes 2,000 individuals assessed longitudinally, 1,500 diagnosed with depression, and 500 with obsessive-compulsive disorder. MAILA tracks dynamic mental states along three orthogonal dimensions, identifies individuals living with mental illness, and achieves near-ceiling accuracy when predicting group-level mental health. By extracting non-verbal signatures of psychological function that have so far remained untapped, MAILA represents a key step toward foundation models for mental health. The ability to decode mental states at zero marginal cost creates new opportunities in neuroscience, medicine, and public health, while raising urgent questions about privacy, agency, and autonomy online.
Anonymous