Using XR (Extended Reality) for Behavioral, Clinical, and Learning Sciences Requires Updates in Infrastructure and Funding
Bibliographic record
Abstract
Extended reality (XR, including augmented and virtual reality) creates a powerful intersection between information technology and cognitive, clinical, and education sciences. XR technology has long captured the public imagination, and its development is the focus of major technology companies. This article demonstrates the potential of XR to (1) deliver behavioral insights, (2) transform clinical treatments, and (3) improve learning and education. However, without appropriate policy, funding, and infrastructural investment, many research institutions will struggle to keep pace with the advances and opportunities of XR. To realize the full potential of XR for basic and translational research, funding should incentivize (1) appropriate training, (2) open software solutions, and (3) collaborations between complementary academic and industry partners. Bolstering the XR research infrastructure with the right investments and incentives is vital for delivering on the potential for transformative discoveries, innovations, and applications.
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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 | 0.051 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.086 | 0.027 |
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".