MétaCan
Menu
Back to cohort
Record W2044906851 · doi:10.1002/acp.1570

Characterizing visual behaviour in a lineup task

2009· article· en· W2044906851 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

VenueApplied Cognitive Psychology · 2009
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologyGazeCognitive psychologyEye trackingTask (project management)Visual searchSocial psychologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Abstract Eye tracking was used to monitor participants' visual behaviour while viewing lineups in order to determine whether gaze behaviour predicted decision accuracy. Participants viewed taped crimes followed by simultaneous lineups. Participants ( N = 34) viewed 4 target‐present and 4 target‐absent lineups. Decision time, number of fixations and duration of fixations differed for selections vs. non‐selections. Correct and incorrect selections differed only in terms of comparison‐type behaviour involving the selected face. Correct and incorrect non‐selections could be distinguished by decision time, number of fixations and duration of fixations on the target or most‐attended face and comparisons. Implications of visual behaviour for judgment strategy (relative vs. absolute) are discussed. Copyright © 2009 John Wiley & Sons, Ltd.

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 categoriesInsufficient payload (model declined to judge)
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 score0.790
Threshold uncertainty score0.999

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)0.0010.002

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 head0.046
GPT teacher head0.371
Teacher spread0.326 · 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