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Record W2893989510 · doi:10.1167/18.10.651

Don't Look Now: The influence of distractor features vs. spatial relevance on attentional deployment

2018· article· en· W2893989510 on OpenAlexaff
Ellen M O'Donoghue, Monica S. Castelhano

Bibliographic record

VenueJournal of Vision · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsRelevance (law)Visual searchSimilarity (geometry)Cognitive psychologyPsychologyVisual attentionContext (archaeology)PerceptionComputer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Visual search performance is aided by knowledge of scene context and likely target positioning (Castelhano & Henderson, 2007; Neider & Zelinsky, 2006). In a recent study, Pereira and Castelhano (2017) demonstrated that attention is differentially deployed depending on the target-relevance of each scene region: in an abrupt-onset paradigm, target-relevant distractors were fixated upon and saccaded towards significantly more often than target-irrelevant distractors. In the present study, we examined whether the visual features of distractors influence attentional deployment over and above the spatial relevance of their positions. Distractors were placed in scene regions that were operationalized as either target-relevant or target-irrelevant, and were either visually similar or dissimilar to the target object. Participants saccaded towards and fixated upon target-relevant distractors significantly more often than target-irrelevant distractors. Interestingly, visual target-distractor similarity did not have an effect: only distractors appearing within target-relevant regions reliably attracted attention, regardless of their visual similarity to the target. These findings suggest that attention during search is distributed based on likely target positioning and, surprisingly, that attentional capture within scenes may be better predicted by spatial relevance than by visual feature similarity. However, previous research has also demonstrated that distractors are more likely to capture attention when they share categorical features with the target (Wyble, Folk, & Potter, 2013). We will further examine the potential interaction between the spatial relevance of distractor positioning and categorical target-distractor similarity, in order to assess the extent to which spatial relevance and distractor features differentially predict attentional deployment in search through real-world scenes. Meeting abstract presented at VSS 2018

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)0.001
metaresearch head score (Gemma)0.019
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.374
Teacher spread0.326 · 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
Published2018
Admission routes1
Has abstractyes

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