{"id":"W4236574109","doi":"10.1167/7.9.24","title":"More efficient scanning for familiar faces","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Chin; Recall; Forehead; Psychology; Task (project management); Eye movement; Face (sociological concept); Cognitive psychology; Facial recognition system; Audiology; Communication; Artificial intelligence; Computer science; Pattern recognition (psychology); Medicine; Anatomy; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000180588,0.0001771462,0.0002803,0.0002890914,0.0001317159,0.0004011925,0.0002308905,0.0003185831,0.00447124],"category_scores_gemma":[0.001008118,0.0001521283,0.0002098493,0.00008424431,0.0002382951,0.0005600528,0.000411359,0.0006141575,0.0004982851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001670778,"about_ca_system_score_gemma":0.0001185285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007364223,"about_ca_topic_score_gemma":0.001398533,"domain_scores_codex":[0.9997919,0.0000147955,0.00001042932,0.0000967863,0.00005295764,0.00003312292],"domain_scores_gemma":[0.9994003,0.0001574185,0.0001294813,0.0001489414,0.00009374609,0.00007009081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001119495,0.00004638617,0.003475321,0.00003114235,0.00001452594,0.00009230334,0.0001215563,0.00003653215,0.987044,0.00004189834,0.0001232572,0.008861265],"study_design_scores_gemma":[0.0000330648,0.000829577,0.7703385,0.00002096471,0.00004145744,0.001580292,0.0003166072,0.001031225,0.2229259,0.0002719529,0.002581184,0.00002924706],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967408,0.0001712973,0.001879591,0.00003370886,0.00001359703,0.000008041842,0.00005203355,0.00004376644,0.00105726],"genre_scores_gemma":[0.9937921,0.000170515,0.002530283,0.0001023256,0.00001698158,0.00002087157,0.0002553726,0.00004102023,0.003070622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00447124,"threshold_uncertainty_score":0.01495779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03710190104446764,"score_gpt":0.3530756698762482,"score_spread":0.3159737688317805,"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."}}