Human-computer interactions predict mental health
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909017898483712 |
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| author | Weilnhammer, Veith Ortega, Jefferson Whitney, David |
| author_facet | Weilnhammer, Veith Ortega, Jefferson Whitney, David |
| contents | Scalable assessments of mental illness remain a critical roadblock toward accessible and equitable care. Here, we show that everyday human-computer interactions encode mental health with biomarker accuracy. 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 tracks dynamic mental states along 13 clinically relevant dimensions, resolves circadian fluctuations and experimental manipulations of arousal and valence, achieves near-ceiling accuracy at the group level, captures information that is only partially reflected in verbal self-report, and improves the ability of large language models to infer user mental health. By extracting signatures of psychological function that have so far remained untapped, MAILA establishes human-computer interactions as a new modality for scalable digital phenotyping and a foundation for context-aware artificial intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_20179 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Human-computer interactions predict mental health Weilnhammer, Veith Ortega, Jefferson Whitney, David Neurons and Cognition Artificial Intelligence Human-Computer Interaction Scalable assessments of mental illness remain a critical roadblock toward accessible and equitable care. Here, we show that everyday human-computer interactions encode mental health with biomarker accuracy. 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 tracks dynamic mental states along 13 clinically relevant dimensions, resolves circadian fluctuations and experimental manipulations of arousal and valence, achieves near-ceiling accuracy at the group level, captures information that is only partially reflected in verbal self-report, and improves the ability of large language models to infer user mental health. By extracting signatures of psychological function that have so far remained untapped, MAILA establishes human-computer interactions as a new modality for scalable digital phenotyping and a foundation for context-aware artificial intelligence. |
| title | Human-computer interactions predict mental health |
| topic | Neurons and Cognition Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.20179 |