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Record W4417524923 · doi:10.1080/00140139.2025.2592985

Evaluating virtual reality work environments: cognitive and physiological impacts on office workers

2025· article· en· W4417524923 on OpenAlexafffund
I Miller Michael, Aditya Subramani Murugan, Eun‐Sik Kim

Bibliographic record

VenueErgonomics · 2025
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Windsor
KeywordsVirtual realityCognitionWork (physics)ProductivityHuman factors and ergonomicsOffice workersElementary cognitive taskCognitive ergonomics

Abstract

fetched live from OpenAlex

As travel becomes integral to modern life, many workers complete office tasks in non-traditional settings using small screens, which may hinder productivity and well-being. Virtual reality (VR) offers a potential solution. This study (n = 20) examined the effects of VR work environments on cognitive performance, physiological responses, and subjective ratings. Participants completed cognitive tasks in both VR and physical environments. While most performance measures showed no significant differences, reaction tasks were slightly better in the physical setup. However, VR yielded better electroencephalogram and subjective outcomes. To enhance ecological validity, a subset of participants repeated the VR condition in a campus cafeteria. Their performance, physiological, and subjective responses remained consistent with lab results. These findings suggest VR can effectively support office tasks in non-traditional environments, maintaining cognitive and physiological performance while improving user experience.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.071
GPT teacher head0.345
Teacher spread0.274 · 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 designObservational
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

Citations0
Published2025
Admission routes2
Has abstractyes

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