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Record W2070062071 · doi:10.1167/12.9.739

Does Context act like a Spatial Attentional Set?: Exploring attentional control during visual search in scenes.

2012· article· en· W2070062071 on OpenAlexaff
Jordan Bawks, Monica S. Castelhano

Bibliographic record

VenueJournal of Vision · 2012
Typearticle
Languageen
FieldComputer Science
TopicVisual Attention and Saliency Detection
Canadian institutionsQueen's University
Fundersnot available
KeywordsVisual searchFixation (population genetics)Context (archaeology)Eye movementCognitive psychologyAttentional controlPsychologySet (abstract data type)Object (grammar)Visual attentionComputer scienceArtificial intelligenceCognitionNeuroscienceGeographyMedicine

Abstract

fetched live from OpenAlex

Attentional control and attentional sets (Folk & Remington, 1994) have been extensively studied in visual search arrays as a selectivity mechanism for filtering out irrelevant information. In the current study, we explore whether such a mechanism could also exist during visual search in scenes. Research has shown that when people search for an object in a scene, they use context to narrow their search to specific regions where the target is likely to be found. The current study investigates whether context can act as a spatial attentional set, where different areas of the scene become more or less relevant depending on the search target. Participants searched for a target object in 24 real world scenes while their eye movements were tracked. On 50% of the trials, an irrelevant distractor object would unexpectedly appear. The distractor would onset 50ms after the first fixation began and would appear either in the target’s context region (Within condition) half the time or in a different context region (Outside condition). The relevant scene context was based on the general placement of target object (lower, middle and upper regions). Fixation data was used to determine the proportion of trials in which a participant immediately fixated on the distractor (within 2 fixations). We compared the proportion of fixations in the Within and Outside conditions and found they were significantly more likely to immediately fixate on the distractor when it was presented Within context (57%) than Outside (33%, p<.001). These results are in line with previous work showing that sudden onsetting objects do capture attention in scenes (Brockmole & Henderson, JEP:HPP, 2005). However, the current study shows that this ability to capture attention is dependent on attentional settings and suggests scene context is used to focus attention on target object regions and suppress information from other regions. 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 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)0.004
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.039
GPT teacher head0.326
Teacher spread0.287 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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