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Record W2007391188 · doi:10.1167/12.9.740

On-Line Contributions of Peripheral Information to Visual Search in Scenes: Further Explorations of Object Content and Scene Context

2012· article· en· W2007391188 on OpenAlexaff
Eduardo Manoel Pereira, Monica S. Castelhano

Bibliographic record

VenueJournal of Vision · 2012
Typearticle
Languageen
FieldComputer Science
TopicVisual Attention and Saliency Detection
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer visionArtificial intelligenceScene statisticsObject (grammar)Computer scienceVisual searchContext (archaeology)GazeRepresentation (politics)PerceptionGeographyPsychology

Abstract

fetched live from OpenAlex

Past research has shown that when no immediate visual information is available in the periphery, scene context typically dominates search strategies (Castelhano & Henderson, 2007). In contrast, other studies have shown that when peripheral information is available, object content plays a significant role in the selection of potential target locations (corresponding to high spatial frequency information; Parkhurst, Law & Niebur, 2002; van Diepen & Wampers, 1998). The present study examined how search strategies are differentially affected by scene context and the placement of object content. Participants searched for a target using a gaze-contingent moving-window paradigm. The participants saw the search scene foveally, while extra-foveally, the scene was manipulated across five conditions: (1)Full Scene: search scene excluding the target; (2)Fractioned Scene: search scene with clusters of objects removed; (3)Object Array: an array of the scene objects on a grey background; (4)Scene Structure: a structural representation of the scene; and (5)No Scene: a black screen. Thus, the Object Array provided only object content, the Scene Structure provided structural information without gist information, and the Fractioned Scene provided scene context with a smaller number of objects. As expected, search performance was best in the Full Scene condition and worst in the No Scene condition across eye movement measures. Interestingly, there were no differences found in latency to the target between Object Array and Fractioned Scene conditions, but both were slower than Full Scene. Because the Fractioned Scene stimuli do not include clusters of objects around the target, the pattern of results suggests an interaction between scene context and object content. The information provided by objects surrounding the target plays an important role in the selection of potential target locations. Further experiments will report on the role of target information in the periphery and its influence on the selection prioritization of object content based on scene context. Meeting abstract presented at VSS 2012

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 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)0.001
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 score0.607
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.048
GPT teacher head0.358
Teacher spread0.310 · 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 teacher head, 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

Citations3
Published2012
Admission routes1
Has abstractyes

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