{"id":"W2028903335","doi":"10.1038/nn1538","title":"fMRI evidence for the neural representation of faces","year":2005,"lang":"en","type":"article","venue":"Nature Neuroscience","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":354,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"National Eye Institute; Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Fusiform face area; Functional magnetic resonance imaging; Optimal distinctiveness theory; Psychology; Face (sociological concept); Neuroscience; Population; Representation (politics); Face perception; Brain mapping; Communication; Computer vision; Pattern recognition (psychology); Artificial intelligence; Cognitive psychology; Computer science; Perception; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006339339,0.0003290614,0.0002392713,0.0006845082,0.0004070096,0.0008460794,0.0006804759,0.0009922151,0.009204034],"category_scores_gemma":[0.004048205,0.0003683218,0.0003257552,0.0003302629,0.0007956154,0.000920818,0.0005275742,0.001076905,0.001144596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003193625,"about_ca_system_score_gemma":0.0002988475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001349387,"about_ca_topic_score_gemma":0.001953451,"domain_scores_codex":[0.9998204,0.0000292997,0.000009062092,0.00006189138,0.00005291851,0.00002646119],"domain_scores_gemma":[0.9983753,0.0009835789,0.0001832612,0.0001826113,0.0001816576,0.00009354052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001272573,0.0001797906,0.005409861,0.0004835179,0.0001781006,0.00116387,0.0002990776,0.0002891161,0.9053064,0.007391093,0.003095536,0.07493106],"study_design_scores_gemma":[0.0005502085,0.001269506,0.611775,0.0003808937,0.0006371497,0.02430389,0.0008194104,0.00692865,0.2636837,0.06213935,0.0273453,0.000166935],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8780112,0.01033957,0.03713866,0.006410765,0.000793224,0.00006926113,0.001085974,0.0002721419,0.06587929],"genre_scores_gemma":[0.9691454,0.003825621,0.01792987,0.001360332,0.0005600873,0.0000652975,0.000729077,0.0001054468,0.006278777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009204034,"threshold_uncertainty_score":0.03079057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1728116073505453,"score_gpt":0.4209654453082297,"score_spread":0.2481538379576844,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}