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Record W2098240276 · doi:10.1177/0956797613516147

Catching Eyes

2014· article· en· W2098240276 on OpenAlexafffund
Anne Böckler, Robrecht P. R. D. van der Wel, Timothy N. Welsh

Bibliographic record

VenuePsychological Science · 2014
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of Toronto
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaAssociation for Psychological Science
KeywordsGazePsychologyEye contactEye movementMotion (physics)Cognitive psychologyCommunicationSensory cueNeuroscienceComputer visionComputer science

Abstract

fetched live from OpenAlex

Direct eye contact and motion onset are two powerful cues that capture attention. In the present study, we combined direct gaze with the sudden onset of motion to determine whether these cues have independent or shared influences. Participants identified targets presented randomly on one of four faces. Initially, two faces depicted direct gaze, and two faces depicted averted gaze. Simultaneously with or 900 ms before target presentation, one face with averted gaze switched to direct gaze, and one face with direct gaze switched to averted gaze. When gaze transitions and target presentation were simultaneous, the greatest response-time facilitation occurred at the location of the sudden onset of direct gaze. When target presentation was delayed, direct-gaze cues maintained a facilitatory influence, whereas motion cues induced an inhibitory influence. These findings reveal that gaze cues and motion cues at the same location influence information processing via independent and concurrently acting social and nonsocial attention channels.

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)0.002
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 score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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 head0.100
GPT teacher head0.407
Teacher spread0.307 · 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

Citations90
Published2014
Admission routes2
Has abstractyes

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