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Record W1988517662 · doi:10.1089/109493103322278736

A Platform for Combining Virtual Reality Experiments with Functional Magnetic Resonance Imaging

2003· article· en· W1988517662 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCyberPsychology & Behavior · 2003
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsYork UniversityUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
FundersHeart and Stroke Foundation of Canada
KeywordsFunctional magnetic resonance imagingVirtual realityHuman–computer interactionComputer scienceWired gloveMagnetic resonance imagingFunctional Brain ImagingNeuroimagingNeurosciencePsychologyMedicine

Abstract

fetched live from OpenAlex

How the brain functions during behavioural tasks conducted in virtual reality (VR) remains largely unresolved. This issue is extremely important both in terms of establishing the benefits of VR through basic science, as well as for future optimization of tasks conducted in VR environments. Here, the authors describe their current work to develop a testing platform for conducting VR experiments that can be probed by functional magnetic resonance imaging (fMRI) to measure brain activity. Examples involving human spatial navigation and data glove operation illustrate the technical feasibility of the approach and introduce thought-provoking observations of brain activation patterns. Future research directions for combined use of VR and fMRI are also discussed.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.077
GPT teacher head0.329
Teacher spread0.252 · 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