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Record W2067147689 · doi:10.1167/8.1.9

More efficient scanning for familiar faces

2008· article· en· W2067147689 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.

Bibliographic record

VenueJournal of Vision · 2008
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsMcMaster University
Fundersnot available
KeywordsChinRecallForeheadPsychologyTask (project management)Eye movementCognitive psychologyFace (sociological concept)Facial recognition systemAudiologyCommunicationArtificial intelligenceComputer sciencePattern recognition (psychology)MedicineNeuroscienceAnatomy

Abstract

fetched live from OpenAlex

The present study reveals changes in eye movement patterns as newly learned faces become more familiar. Observers received multiple exposures to newly learned faces over four consecutive days. Recall tasks were performed on all 4 days, and a recognition task was performed on the fourth day. Eye movement behavior was compared across facial exposure and task type. Overall, the eyes were viewed for longer and more often than any other facial region, regardless of face familiarity. As a face became more familiar, observers made fewer fixations during recall and recognition. With increased exposure, observers sampled more from the eyes and sampled less from the nose, mouth, forehead, chin, and cheek regions. Interestingly, this change in scanning behavior was only observed for recall tasks, but not for recognition.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.168

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 studies0.0000.000
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 head0.070
GPT teacher head0.350
Teacher spread0.280 · 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