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Record W4412569835 · doi:10.1007/s10055-025-01191-4

Exploring working memory across aging using virtual reality

2025· article· en· W4412569835 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

VenueVirtual Reality · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsCanadian University Music SocietyBaycrest HospitalUniversity of TorontoNational Research Council Canada
FundersNational Research Council Canada
KeywordsComputer scienceVirtual realityHuman–computer interactionWorking memoryComputer graphics (images)Immersion (mathematics)MultimediaCognitionPsychology

Abstract

fetched live from OpenAlex

Abstract This study aimed to validate a virtual reality (VR) version of a working memory (WM) task. Validating this VR version of the WM task required replicating findings from traditional WM tasks. Nineteen younger adults and twenty older adults completed a delayed match-to-sample WM task in a VR headset. Participants were told to select the shapes that matched the ones previously presented. These shapes varied in number (WM load), with half of them being easily nameable (e.g., heart, Easy condition) or being more abstract (Difficult condition). The accuracy of responding decreased with increasing WM load and was lower for Difficult than Easy conditions when the shape could not be easily named. Additionally, a greater WM load effect was observed in older than younger adults, aligning with findings from non-VR WM research. These results show that VR is a suitable tool for cognitive research, providing new opportunities for more immersive and interactive cognitive assessments.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.546
GPT teacher head0.462
Teacher spread0.084 · 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