Validation of an Upgraded Virtual Reality Platform Designed for Real-Time Dialogical Psychotherapies
Bibliographic record
Abstract
Background: The advent of virtual reality in psychiatry presents a wealth of opportunities for a variety of psychopathologies. Avatar Interventions are dialogic and experiential treatments integrating personalized medicine with virtual reality (VR), which have shown promising results by enhancing the emotional regulation of their participants. Notably, Avatar Therapy for the treatment of auditory hallucinations (i.e., voices) allows patients to engage in dialogue with an avatar representing their most persecutory voice. In addition, Avatar Intervention for cannabis use disorder involves an avatar representing a significant person in the patient’s consumption. In both cases, the main goal is to modify the problematic relationship and allow patients to regain control over their symptoms. While results are promising, its potential to be applied to other psychopathologies, such as major depression, is an exciting area for further exploration. In an era where VR interventions are gaining popularity, the present study aims to investigate whether technological advancements could overcome current limitations, such as avatar realism, and foster a deeper immersion into virtual environments, thereby enhancing participants’ sense of presence within the virtual world. A newly developed virtual reality platform was compared to the current platform used by our research team in past and ongoing studies. Methods: This study involved 43 subjects: 20 healthy subjects and 23 subjects diagnosed with severe mental disorders. Each participant interacted with an avatar using both platforms. After each immersive session, questionnaires were administered by a graduate student in a double-blind manner to evaluate technological advancements and user experiences. Results: The findings indicate that the new technological improvements allow the new platform to significantly surpass the current platform as per multiple subjective parameters. Notably, the new platform was associated with superior realism of the avatar (d = 0.574; p < 0.001) and the voice (d = 1.035; p < 0.001), as well as enhanced lip synchronization (d = 0.693; p < 0.001). Participants reported a significantly heightened sense of presence (d = 0.520; p = 0.002) and an overall better immersive experience (d = 0.756; p < 0.001) with the new VR platform. These observations were true in both healthy subjects and participants with severe mental disorders. Conclusions: The technological improvements generated a heightened sense of presence among participants, thus improving their immersive experience. These two parameters could be associated with the effectiveness of VR interventions and future studies should be undertaken to evaluate their impact on outcomes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".