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Record W2000911941 · doi:10.1177/0013916514533189

Floor Plan Connectivity Influences Wayfinding Performance in Virtual Environments

2014· article· en· W2000911941 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.

Bibliographic record

VenueEnvironment and Behavior · 2014
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCued speechVirtual machinePlan (archaeology)Task (project management)Floor planPsychologyOrientation (vector space)Computer scienceTest (biology)Point (geometry)DestinationsCognitive psychologyHuman–computer interactionApplied psychologyEngineeringGeographyEcology

Abstract

fetched live from OpenAlex

The structure of the physical environment can have a significant influence on individuals’ ability to orient within it. We asked participants to perform a cued wayfinding task in two virtual environments to test the hypothesis that spatial orientation skills are indeed affected by the physical complexity of the environment. The two virtual environments used for testing differed solely in one objective measure of plan complexity, that is, the average number of connections at each decision point or terminal corridor. Our results showed that participants committed more errors and took longer to reach their destinations in the more interconnected environment. Performance was more efficient on trials in which participants were able to use previously learned routes relative to trials in which participants were forced to plan novel routes. These findings provide strong evidence that people’s ability to navigate in unfamiliar surroundings is affected by the layout complexity of the environment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.404

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 head0.011
GPT teacher head0.192
Teacher spread0.181 · 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