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Record W2050087421 · doi:10.1177/0013916512466094

Seeing Beyond Your Visual Field

2012· article· en· W2050087421 on OpenAlexaff
Kevin R. Barton, Deltcho Valtchanov, Colin G. Ellard

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0540.017

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2012
Admission routes1
Has abstractyes

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