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Record W2998190657 · doi:10.1177/2633105519896803

Older Adults Show Less Flexible Spatial Cue Use When Navigating in a Virtual Reality Environment Compared With Younger Adults

2019· article· en· W2998190657 on OpenAlex
Kazushige Kimura, James F. Reichert, Debbie M. Kelly, Zahra Moussavi

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

VenueNeuroscience Insights · 2019
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLandmarkVirtual realityPsychologySituatedYoung adultComputer scienceHuman–computer interactionDevelopmental psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Daily life requires accurate navigation, and thus better understanding of aging on navigational abilities is critical. Importantly, the use of spatial properties by older and younger adults remains unclear. During this study, younger and older human adults were presented with a virtual environment in which they had to navigate a series of hallways. The hallways provided 2 general types of spatial information: geometric, which included distance and directional turns along a learned route, and featural, which included landmarks situated along the route. To investigate how participants used these different cue types, geometric and/or landmark information was manipulated during testing trials. Data from 40 younger (20 women) and 40 older (20 women) adults were analyzed. Our findings suggest that (1) both younger and older adults relied mostly on landmarks to find their way, and (2) younger adults were better able to adapt to spatial changes to the environment compared with older adults.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.802

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 communication0.0000.001
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 head0.022
GPT teacher head0.225
Teacher spread0.202 · 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