RICHIR Simon, SHIRAI Akihiko Editors. International conference organized by Laval Virtual. Design and Evaluation of a Virtual Environment for the Treatment of
Bibliographic record
Abstract
Abstract—Cognitive-behavioural therapy is often used for anger treatment. An important element of this therapy is exposure to anger evoking stimuli. In this paper virtual reality is put forward as a technology that can effectively create these stimuli by exposing patients to social scenes that include anger stressors such as aggressive dialogues with virtual characters or arousing surrounding with loud music or flashing light. Applying a situated cognitive engineering approach a prototype system was developed which allowed a therapist to control these stressors. To evaluate the prototype an experiment was conducted in which participants, 18 non-patients and 2 patients, were exposed in a virtual environment to three types of social scenes: (1) a passive dialogue, (2) an aggressive dialogue, and (3) an aggressive dialogue with arousing surrounding. Results showed that these conditions had a significant effect on participants ’ galvanic skin response and the type of verbal reply towards the avatar. This effect was significant larger for the two patients than the non-patient group. In addition, evaluation of the therapist user interface suggested that most interaction components were relatively easy to use.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.023 |
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".