Assessing Social Anxiety Through Digital Biomarkers Embedded in a Gaming Task
Bibliographic record
Abstract
Digital biomarkers of mental health issues offer many advantages, including timely identification for early intervention, ongoing assessment during treatment, and reducing barriers to assessment stemming from geography, age, fear, or disparities in access to systems of care. Embedding digital biomarkers into games may further increase the reach of digital assessment. In this study, we explore game-based digital biomarkers for social anxiety, based on interaction with a non-player character (NPC). We show that social anxiety affects a player's accuracy and their movement path in a gaming task involving an NPC. Further, we compared first versus third-person camera perspectives and the use of customized versus predefined avatars to explore the influence of common game interface factors on the expression of social anxiety through in-game movements. Our findings provide new insights about how game-based digital biomarkers can be effectively used for social anxiety, affording the benefits of early and ongoing digital assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".