{"id":"W2132934054","doi":"10.1068/p3339","title":"Configural Face Processing Develops more Slowly than Featural Face Processing","year":2002,"lang":"en","type":"article","venue":"Perception","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":660,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Face (sociological concept); Set (abstract data type); Psychology; Contrast (vision); Face perception; Artificial intelligence; Computer vision; Pattern recognition (psychology); Communication; Computer science; Cognitive psychology; Perception; Neuroscience","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001287455,0.0002930574,0.0002081708,0.0001437417,0.0006001474,0.0003316553,0.000213712,0.0001768003,0.002574733],"category_scores_gemma":[0.0001425487,0.000270566,0.00009259143,0.000437856,0.0001811704,0.001098909,0.00003711944,0.0003573167,0.002294144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001590674,"about_ca_system_score_gemma":0.00003388532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001658913,"about_ca_topic_score_gemma":0.00001173224,"domain_scores_codex":[0.9979693,0.0001224674,0.0002998562,0.0006328649,0.0004893377,0.000486144],"domain_scores_gemma":[0.9994043,0.00002937544,0.0001410243,0.0001667765,0.0001076787,0.0001508813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002277739,0.00007127661,0.0002748344,0.00008641005,0.000001165534,0.000004617377,0.01121888,0.00004191207,0.6225813,0.000006586118,0.0009631909,0.364727],"study_design_scores_gemma":[0.004696738,0.0004976133,0.3096454,0.001268787,0.0001438182,0.0009601275,0.03430158,0.4775956,0.1371584,0.0003721813,0.02920908,0.004150698],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823524,0.00008635089,0.004565474,0.002745836,0.0003032987,0.0004166792,0.00001964334,0.0005069692,0.009003334],"genre_scores_gemma":[0.9898092,0.0001277347,0.0008526887,0.001135838,0.0002089572,0.00004120435,0.00003761708,0.00004031197,0.007746422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.485423,"threshold_uncertainty_score":0.9999747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07887539313004559,"score_gpt":0.303584198123097,"score_spread":0.2247088049930514,"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."}}