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Record W2050087421 · doi:10.1177/0013916512466094

Seeing Beyond Your Visual Field

2012· article· en· W2050087421 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 · 2012
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsField (mathematics)PerceptionVisual fieldSpace (punctuation)LimitingHuman–computer interactionVisual spaceComputer scienceVisual perceptionVirtual realityVisualizationPsychologyArtificial intelligenceEngineeringMathematicsPure mathematics

Abstract

fetched live from OpenAlex

Previous research has suggested that the layout of urban spaces can have a substantial influence on how people navigate through those spaces. However, to date, few studies have directly investigated how changes in layout interact with changes in visual field to shape a person’s route choice. Across two experiments, the influence of visual field and spatial layout was manipulated using virtual reality. It was found that route choice was significantly influenced by the configuration of a space—The more consistently organized environment led to more systematic route choices. However, limiting the perception of distant visual information was found to influence route choice in a similar but completely independent way. These findings suggest that navigation in urban spaces is dependent on the interaction between topology and the visual features of the space, where greater visual field and a consistently organized spatial layout lead to maximally efficient route choices.

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 score0.391
Threshold uncertainty score0.672

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)0.0010.000

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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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