Dynamic Interactive Social Cognition Training in Virtual Reality (DiSCoVR) for People With a Psychotic Disorder: Single-Group Feasibility and Acceptability Study
Bibliographic record
Abstract
Background People with a psychotic disorder commonly experience problems in social cognition and functioning. Social cognition training (SCT) improves social cognition, but may inadequately simulate real-life social interactions. Virtual reality (VR) provides a realistic, interactive, customizable, and controllable training environment, which could facilitate the application of skills in daily life. Objective We developed a 16-session immersive VR SCT (Dynamic Interactive Social Cognition Training in Virtual Reality [DiSCoVR]) and conducted a single-group feasibility pilot study. Methods A total of 22 people with a psychotic disorder and reported problems in social cognition participated. Feasibility and acceptability were assessed using a survey for participants and therapists, and by examining relevant parameters (eg, dropouts). We analyzed preliminary treatment effects on social cognition, neurocognition, and psychiatric symptoms. Results A total of 17 participants completed the study. Participants enjoyed DiSCoVR (mean 7.25, SD 2.05; range 3-10), thought it was useful for daily social activities (mean 7.00, SD 2.05; range 3-10), and enjoyed the combination of VR and a therapist (mean 7.85, SD 2.11; range 3-10). The most frequently mentioned strength of DiSCoVR was the opportunity to practice with personalized social situations (14/20, 70%). A significant improvement of emotion perception was observed (Ekman 60 Faces; t16=–4.79, P<.001, d=–0.67), but no significant change was found in other measures of social cognition, neurocognition, psychiatric symptoms, or self-esteem. Conclusions DiSCoVR was feasible and acceptable to participants and therapists, and may improve emotion perception.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".