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Record W2547462571 · doi:10.1109/gem.2014.7047969

Physiological acrophobia evaluation through in vivo exposure in a VR CAVE

2014· article· en· W2547462571 on OpenAlexafffund
João P. Costa, James Robb, Lennart E. Nacke

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVirtual realityImmersion (mathematics)AnxietyExposure therapyVirtual Reality Exposure TherapyPsychologyApplied psychologyComputer scienceSimulationHuman–computer interaction

Abstract

fetched live from OpenAlex

Acrophobia (i.e., the fear of heights) is commonly treated using Virtual Reality (VR) applications. Patients that suffer from this clinical condition can experience extreme levels of anxiety, stress, and discomfort, even at relatively low heights. VR computer-assisted virtual environments (CAVEs) have been found to be highly immersive and successful in the treatment of acrophobia. The general method of evaluating therapy progress is through self-reported questionnaire measures. However, these are subject to participant bias. Physiological measures, on the other hand, could provide a more objective way of assessing acrophobia. To our knowledge, psychophysiological measures are not commonly used in the evaluation of acrophobes and their therapy progress within CAVEs. Thus, we present a CAVE application for acrophobia treatment, which includes a physiological feedback mechanism to assess patient progress. It also permits patient movement to facilitate increased presence and immersion. In this application, players sequentially gain access to increasing heights as they successfully progress through lesser heights, as assessed through the evaluation of their physiological responses to VR stimuli.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.324
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2014
Admission routes2
Has abstractyes

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