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Record W1939576353 · doi:10.1080/13506285.2015.1083067

Emotional modulation of attention orienting by gaze varies with dynamic cue sequence

2015· article· en· W1939576353 on OpenAlexafffund
Amandine Lassalle, Roxane J. Itier

Bibliographic record

VenueVisual Cognition · 2015
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health ResearchOntario Ministry of Research and InnovationCanada Research ChairsCanada Foundation for Innovation
KeywordsGazePsychologySurpriseFacial expressionCognitive psychologyEmotional expressionAngerExpression (computer science)Orienting responseCommunicationSocial psychologyNeuroscienceHabituationComputer science

Abstract

fetched live from OpenAlex

Recent gaze cueing studies using dynamic cue sequences have reported increased attention orienting by gaze with faces expressing fear, surprise or anger. Here, we investigated whether the type of dynamic cue sequence used impacted the magnitude of this effect. When the emotion was expressed before or concurrently with gaze shift, no modulation of gaze-oriented attention by emotion was seen. In contrast, when the face cue averted gaze before expressing an emotion (as if reacting to the object after first localizing it), the gaze orienting effect was clearly increased for fearful, surprised and angry faces compared to neutral faces. Thus, the type of dynamic sequence used, and in particular the order in which the gaze shift and the facial expression are presented, modulate gaze-oriented attention, with maximal modulation seen when the expression of emotion follows gaze shift.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.078
GPT teacher head0.337
Teacher spread0.259 · 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 designBench or experimental
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

Citations39
Published2015
Admission routes2
Has abstractyes

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