Moving from Virtual Reality Exposure-Based Therapy to Augmented Reality Exposure-Based Therapy: A Review
Bibliographic record
Abstract
This paper reviews the move from virtual reality exposure-based therapy to augmented reality exposure-based therapy (ARET). Unlike virtual reality (VR), which entails a complete virtual environment (VE), augmented reality (AR) limits itself to producing certain virtual elements to then merge them into the view of the physical world. Although, the general public may only have become aware of AR in the last few years, AR type applications have been around since beginning of the twentieth century. Since, then, technological developments have enabled an ever increasing level of seamless integration of virtual and physical elements into one view. Like VR, AR allows the exposure to stimuli which, due to various reasons, may not be suitable for real-life scenarios. As such, AR has proven itself to be a medium through which individuals suffering from specific phobia can be exposed "safely" to the object(s) of their fear, without the costs associated with programing complete VEs. Thus, ARET can offer an efficacious alternative to some less advantageous exposure-based therapies. Above and beyond presenting what has been accomplished in ARET, this paper covers some less well-known aspects of the history of AR, raises some ARET related issues, and proposes potential avenues to be followed. These include the type of measures to be used to qualify the user's experience in an augmented reality environment, the exclusion of certain AR-type functionalities from the definition of AR, as well as the potential use of ARET to treat non-small animal phobias, such as social phobia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".