Baxter VR: A Scale-Based Prototype for Simulating Human-Dog Interactions in Virtual Reality Exposure Therapy
Bibliographic record
Abstract
Cynophobia, the fear of dogs, is among the most prevalent and debilitating animal phobias: 36% of animal phobia patients report fearing either dogs or cats, and dogs are ubiquitous in many cultures. Exposure therapy is considered first-line treatment for cynophobia, but the involvement of real dogs presents ethical and feasibility concerns. Virtual reality exposure therapy (VRET) has demonstrated potential as an alternative to in-person exposure therapy in the treatment of animal phobias, but literature evaluating its use for cynophobia is scarce, and a recent scoping review suggests a need for further exploration in this area. We thus present Baxter VR , a VRET-inspired prototype that enables the user to change their size relative to a virtual dog and surrounding environment, which then affects both the degree of interactivity in the environment and the dog’s behaviour itself. For example, the dog becomes fearful and acts aggressively if the user’s size increases to ”giant” scale; which is intended to dispel a common cynophobic belief that dog aggression occurs spontaneously. We argue that such an approach could guide further exploration of VRET interventions for cynophobia, culminating in a safer and more affordable treatment option for individuals suffering from this condition.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".