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Record W1997353489 · doi:10.1037/a0030463

Attention orienting by gaze and facial expressions across development.

2013· article· en· W1997353489 on OpenAlexafffund
Karly Neath, Elizabeth S. Nilsen, Katarzyna Gittsovich, Roxane J. Itier

Bibliographic record

VenueEmotion · 2013
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsPsychologyGazeFacial expressionCognitive psychologyCognitionEmotional expressionDevelopmental psychologyTraitCommunicationNeuroscience

Abstract

fetched live from OpenAlex

Processing of facial expressions has been shown to potentiate orienting of attention toward the direction signaled by gaze in adults, an important social-cognitive function. However, little is known about how this social attention skill develops. This study is the first to examine the developmental trajectory of the gaze orienting effect (GOE), its modulations by facial expressions, and its links with theory of mind (ToM) abilities. Dynamic emotional stimuli were presented to 222 participants (7-25 years old) with normal trait anxiety using a gaze-cuing paradigm. The GOE was found as early as 7 years of age and decreased linearly until 12-13 years, at which point adult levels were reached. Both fearful and surprised expressions enhanced the GOE compared with neutral expressions. The GOE for fearful faces was also larger than for joyful and angry expressions. These effects did not interact with age and were not driven by intertrial variance. Importantly, the GOE did not correlate with ToM abilities as assessed by the "Reading the Mind in the Eyes" test. The implication of these findings for clinical and typically developing populations is discussed.

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)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.287
Teacher spread0.250 · 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

Citations51
Published2013
Admission routes2
Has abstractyes

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