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Record W2160269677 · doi:10.1111/cdev.12039

The Moving Window Technique: A Window Into Developmental Changes in Attention During Facial Emotion Recognition

2012· article· en· W2160269677 on OpenAlexafffund
Elina Birmingham, T. Meixner, Grace Iarocci, Christopher Kanan, Daniel Smilek, James W. Tanaka

Bibliographic record

VenueChild Development · 2012
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of WaterlooUniversity of VictoriaSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of CanadaMichael Smith Health Research BC
KeywordsPsychologyFace perceptionFacial expressionPerceptionWindow (computing)Cognitive psychologyDevelopmental psychologyAttentional biasEye trackingTask (project management)Eye movementFace (sociological concept)CognitionCommunicationNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

The strategies children employ to selectively attend to different parts of the face may reflect important developmental changes in facial emotion recognition. Using the Moving Window Technique (MWT), children aged 5-12 years and adults (N = 129) explored faces with a mouse-controlled window in an emotion recognition task. An age-related increase in attention to the left eye emerged at age 11-12 years and reached significance in adulthood. This left-eye bias is consistent with previous eye tracking research and findings of a perceptual bias for the left side of faces. These results suggest that a strategic attentional bias to the left eye begins to emerge at age 11-12 years and is likely established sometime in adolescence.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.035
GPT teacher head0.257
Teacher spread0.222 · 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 teacher head, 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

Citations20
Published2012
Admission routes2
Has abstractyes

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