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Objects on a Collision Path With the Observer Demand Attention

2008· article· en· W2145164211 on OpenAlexaff
Jeffrey Y. Lin, Steven Franconeri, James T. Enns

Bibliographic record

VenuePsychological Science · 2008
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLoomingObserver (physics)ReflexivityPsychologyCollisionCognitive psychologyVisual searchArtificial intelligenceGazePath (computing)Computer visionCommunicationComputer scienceComputer security

Abstract

fetched live from OpenAlex

How observers distribute limited processing resources to regions of a scene is based on a dynamic balance between current goals and reflexive tendencies. Past research showed that these reflexive tendencies include orienting toward objects that expand as if they were looming toward the observer, presumably because this signal indicates an impending collision. Here we report that during visual search, items that loom abruptly capture attention more strongly when they approach from the periphery rather than from near the center of gaze (Experiment 1), and target objects are more likely to be attended when they are on a collision path with the observer rather than on a near-miss path (Experiment 2). Both effects are exaggerated when search is performed in a large projection dome (Experiment 3). These findings suggest that the human visual system prioritizes events that are likely to require a behaviorally urgent response.

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)0.007
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.139
GPT teacher head0.374
Teacher spread0.235 · 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

Citations71
Published2008
Admission routes1
Has abstractyes

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