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Record W2992942470

Virtual Reality Flight Environments May Tax Working Memory and Disrupt Prospective Memory

2019· article· en· W2992942470 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCORE Scholar (Wright State University) · 2019
Typearticle
Languageen
FieldPsychology
TopicCognitive Functions and Memory
Canadian institutionsnot available
FundersOntario Innovation Trust
KeywordsVirtual realityProspective memoryComputer scienceCognitive psychologyHuman–computer interactionBusinessPsychologyNeuroscienceCognition
DOInot available

Abstract

fetched live from OpenAlex

While vivid virtual reality (VR) environments may afford better performance for some flight tasks, it is possible that enhanced stimuli could overload some cognitive resources. Prospective memory (PM) is a cognitive factor sensitive to working memory and visual processing demands, and it may be a performance factor either adversely affected or enhanced by VR factors. Forty-seven pilots flew a VR flight simulation scenario, which included an auditory cue-based PM task. Self-ratings of psychological experiences in VR revealed three factors with relationships to PM: fluency, presence, and interactivity. Path analyses examined the relation of each of these factors with PM, and with two types of working memory, based on Level 1 SA. Higher fluency ratings were associated with lower PM, whereas greater presence and interactivity were correlated with better PM. Working memory also significantly mediated the effects of fluency on PM.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.021
GPT teacher head0.244
Teacher spread0.223 · 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