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Record W2081563932 · doi:10.1037/a0029707

Changing perspective: Zooming in and out during visual search.

2012· article· en· W2081563932 on OpenAlexafffund
Grayden J. F. Solman, J. Allan Cheyne, Daniel Smilek

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2012
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVisual searchZoomScope (computer science)Computer sciencePerspective (graphical)Fixation (population genetics)Eye movementObserver (physics)Incremental heuristic searchArtificial intelligenceCognitive psychologyPsychologyComputer visionSearch algorithmBeam searchAlgorithmEngineering

Abstract

fetched live from OpenAlex

Laboratory studies of visual search are generally conducted in contexts with a static observer vantage point, constrained by a fixation cross or a headrest. In contrast, in many naturalistic search settings, observers freely adjust their vantage point by physically moving through space. In two experiments, we evaluate behavior during free vantage point (FVP) search, using observer-controlled zooming to simulate movement toward or away from search objects. We focus on scope fluctuations--repeated reversals in the direction of zooming during search. We found increased fluctuation when search items were sparse (Experiment 1) or of mixed size (Experiment 2). We propose that during FVP search, observers attempt to maximize the number of simultaneously discriminable items. Scope fluctuations emerge when maximizing does not enable simultaneous access to all search items, or when observers become disoriented in the search environment, necessitating repeated switches to a broad scope to reorient.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.085
GPT teacher head0.438
Teacher spread0.353 · 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

Citations7
Published2012
Admission routes2
Has abstractyes

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