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Record W4386116292 · doi:10.1080/02643294.2023.2250510

“Looking at nothing”: An implicit ocular motor index of face recognition in developmental prosopagnosia

2023· article· en· W4386116292 on OpenAlexafffund
Aida Rahavi, Manuela Malaspina, Andrea Albonico, Jason J.S. Barton

Bibliographic record

VenueCognitive Neuropsychology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of British Columbia
FundersCanada Research ChairsDiscovery Eye Foundation
KeywordsPsychologyStimulus (psychology)BlankCognitive psychologyNothingFacial recognition systemDevelopmental psychologyAudiologyPattern recognition (psychology)

Abstract

fetched live from OpenAlex

Subjects often look towards to previous location of a stimulus related to a task even when that stimulus is no longer visible. In this study we asked whether this effect would be preserved or reduced in subjects with developmental prosopagnosia. Participants learned faces presented in video-clips and then saw a brief montage of four faces, which was replaced by a screen with empty boxes, at which time they indicated whether the learned face had been present in the montage. Control subjects were more likely to look at the blank location where the learned face had appeared, on both hit and miss trials, though the effect was larger on hit trials. Prosopagnosic subjects showed a reduced effect, though still better on hit than on miss trials. We conclude that explicit accuracy and our implicit looking at nothing effect are parallel effects reflecting the strength of the neural activity underlying face 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.

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)0.005
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.105
GPT teacher head0.342
Teacher spread0.236 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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