MétaCan
Menu
Back to cohort
Record W2604432278 · doi:10.1037/emo0000283

Eye fixation patterns for categorizing static and dynamic facial expressions.

2017· article· en· W2604432278 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEmotion · 2017
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversité de MontréalUniversité du Québec en Outaouais
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEye trackingFixation (population genetics)Eye movementFacial expressionPsychologyCognitive psychologyFace perceptionFacial recognition systemArtificial intelligenceComputer visionComputer scienceCommunicationPerceptionPattern recognition (psychology)Neuroscience

Abstract

fetched live from OpenAlex

Facial expressions of emotion are dynamic in nature, but most studies on the visual strategies underlying the recognition of facial emotions have used static stimuli. The present study directly compared the visual strategies underlying the recognition of static and dynamic facial expressions using eye tracking and the Bubbles technique. The results revealed different eye fixation patterns with the 2 kinds of stimuli, with fewer fixations on the eye and mouth area during the recognition of dynamic than static expressions. However, these differences in eye fixations were not accompanied by any systematic differences in the facial information that was actually processed to recognize the expressions. (PsycINFO Database Record

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.

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)0.000
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 score0.794
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.067
GPT teacher head0.351
Teacher spread0.284 · 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