{"id":"W2067147689","doi":"10.1167/8.1.9","title":"More efficient scanning for familiar faces","year":2008,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":114,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Chin; Recall; Forehead; Psychology; Task (project management); Eye movement; Cognitive psychology; Face (sociological concept); Facial recognition system; Audiology; Communication; Artificial intelligence; Computer science; Pattern recognition (psychology); Medicine; Neuroscience; Anatomy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000176714,0.00005554662,0.0001005847,0.0001098826,0.0001637026,0.0000167619,0.00008363571,0.00003368654,0.00005248501],"category_scores_gemma":[0.0002624671,0.00004119023,0.00009834265,0.00009618063,0.0000446312,0.0001189083,0.00001115655,0.00009663642,0.00003085098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002711025,"about_ca_system_score_gemma":0.00002681127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.945886e-7,"about_ca_topic_score_gemma":1.518334e-7,"domain_scores_codex":[0.9993027,0.00003403258,0.0002127774,0.00008538592,0.0002630735,0.0001020614],"domain_scores_gemma":[0.9995396,0.00009942355,0.0001589101,0.00004746952,0.00009141036,0.00006319577],"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":[0.0001054343,0.0001017094,0.0001304636,0.0000116254,0.000001352398,0.00001745056,0.0009779035,0.002556469,0.9749316,0.00001344648,0.004000351,0.0171522],"study_design_scores_gemma":[0.005734247,0.0034544,0.1351724,0.0007886508,0.00005045858,0.002758326,0.002227841,0.09962863,0.6639533,0.0003820677,0.08523816,0.0006114781],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950265,0.00003251767,0.003513477,0.0005354705,0.0003839831,0.00008172524,0.000004521725,0.00001025407,0.0004115586],"genre_scores_gemma":[0.9981735,0.00023047,0.000917631,0.0003705248,0.0001081158,8.516254e-7,4.203631e-7,0.00000638725,0.0001921059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3109783,"threshold_uncertainty_score":0.1679689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06985580579372011,"score_gpt":0.3498888719859511,"score_spread":0.280033066192231,"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."}}