MétaCan
Menu
Back to cohort

Progress in perceptual research: the case of prosopagnosia

2019· preprint· en· W2946932390 on OpenAlexafffund
Andrea Albonico, Jason J.S. Barton

Bibliographic record

VenueF1000Research · 2019
Typepreprint
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPsychologyPerceptionNeuroimagingCognitive psychologyFusiform face areaFace perceptionFace (sociological concept)Cognitive neuroscience of visual object recognitionVisual perceptionNeuroscienceObject (grammar)Developmental psychologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Prosopagnosia is an impairment in the ability to recognize faces and can be acquired after a brain lesion or occur as a developmental variant. Studies of prosopagnosia make important contributions to our understanding of face processing and object recognition in the human visual system. We review four areas of advances in the study of this condition in recent years. First are issues surrounding the diagnosis of prosopagnosia, including the development and evaluation of newer tests and proposals for diagnostic criteria, especially for the developmental variant. Second are studies of the structural basis of prosopagnosia, including the application of more advanced neuroimaging techniques in studies of the developmental variant. Third are issues concerning the face specificity of the defect in prosopagnosia, namely whether other object processing is affected to some degree and in particular the status of visual word processing in light of recent predictions from the "many-to-many hypothesis". Finally, there have been recent rehabilitative trials of perceptual learning applied to larger groups of prosopagnosic subjects that show that face impairments are not immutable in this condition.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.427
GPT teacher head0.496
Teacher spread0.070 · 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; both teacher heads agree on what is shown here.

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

Citations29
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueF1000ResearchSame topicFace Recognition and PerceptionFrench-language works237,207