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Record W2135682029 · doi:10.1167/9.3.6

Viewing task influences eye movement control during active scene perception

2009· article· en· W2135682029 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Vision · 2009
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsQueen's University
FundersArmy Research OfficeEconomic and Social Research CouncilNatural Sciences and Engineering Research Council of CanadaQueen's UniversityNational Science Foundation
KeywordsEye movementFixation (population genetics)GazeSaccadeMemorizationPerceptionPsychologyTask (project management)Cognitive psychologyVisual searchComputer scienceArtificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

Expanding on the seminal work of G. Buswell (1935) and I. A. Yarbus (1967), we investigated how task instruction influences specific parameters of eye movement control. In the present study, 20 participants viewed color photographs of natural scenes under two instruction sets: visual search and memorization. Results showed that task influenced a number of eye movement measures including the number of fixations and gaze duration on specific objects. Additional analyses revealed that the areas fixated were qualitatively different between the two tasks. However, other measures such as average saccade amplitude and individual fixation durations remained constant across the viewing of the scene and across tasks. The present study demonstrates that viewing task biases the selection of scene regions and aggregate measures of fixation time on those regions but does not influence other measures, such as the duration of individual fixations.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.985
Threshold uncertainty score0.277

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 head0.009
GPT teacher head0.282
Teacher spread0.274 · 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