{"id":"W1977688791","doi":"10.3758/bf03193597","title":"Recognition memory for realistic synthetic faces","year":2007,"lang":"en","type":"article","venue":"Memory & Cognition","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"National Eye Institute; National Institute of Mental Health","keywords":"Psychology; Recognition memory; Similarity (geometry); Perception; Pattern recognition (psychology); Facial recognition system; Face (sociological concept); Face perception; Memoria; Cognitive psychology; Multidimensional scaling; Artificial intelligence; Communication; Cognition; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00112316,0.0002423941,0.0002151802,0.0002952784,0.0003934283,0.00008837223,0.0001367081,0.0001747114,0.001450722],"category_scores_gemma":[0.001891539,0.0002532291,0.0001547829,0.0003084855,0.0001613863,0.0003779788,0.00002359474,0.0001919135,0.001560244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000108833,"about_ca_system_score_gemma":0.00004950554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002435434,"about_ca_topic_score_gemma":0.00008796778,"domain_scores_codex":[0.9978518,0.0001613284,0.0004372566,0.0006059267,0.000453921,0.0004897217],"domain_scores_gemma":[0.9983353,0.0008145497,0.0001794212,0.0002089145,0.0002948546,0.0001669629],"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":[0.0003050804,0.0001625873,0.000005204417,0.0001065388,0.000005477871,0.000009910313,0.0003323585,0.000008117902,0.6671135,0.00005251979,0.0008444155,0.3310542],"study_design_scores_gemma":[0.00181272,0.0003124833,0.001507643,0.0002037161,0.0001466981,0.0001516552,0.001193488,0.0006865321,0.9802688,0.01032106,0.002788999,0.0006062033],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8783003,0.00002228163,0.02615528,0.0004030051,0.001194717,0.001447863,0.0003865126,0.0005208065,0.09156921],"genre_scores_gemma":[0.9956531,0.00008329826,0.0006727056,0.001500147,0.0004008385,0.0001606968,0.0003075087,0.0000479559,0.001173746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3304481,"threshold_uncertainty_score":0.999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1021179107621981,"score_gpt":0.3227374055540312,"score_spread":0.2206194947918332,"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."}}