{"id":"W2972038530","doi":"10.1177/0301006619867862","title":"Two Sides of Face Learning: Improving Between-Identity Discrimination While Tolerating More Within-Person Variability in Appearance","year":2019,"lang":"en","type":"article","venue":"Perception","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psychology; Identity (music); Face (sociological concept); Similarity (geometry); Context (archaeology); Task (project management); Cognitive psychology; Facial recognition system; Discrimination learning; Social psychology; Pattern recognition (psychology); Artificial intelligence; Computer science; Image (mathematics); Aesthetics; Biology; Art; Linguistics","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":[],"consensus_categories":[],"category_scores_codex":[0.00102035,0.0001480693,0.0002206766,0.0001415718,0.0001183071,0.00007082979,0.0001283625,0.00009336765,0.0004410924],"category_scores_gemma":[0.0006848767,0.0001535267,0.00006875445,0.0003250955,0.00007802521,0.001144996,0.00003756738,0.0003491367,0.0002276798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001559843,"about_ca_system_score_gemma":0.00002722421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000641935,"about_ca_topic_score_gemma":0.0001635168,"domain_scores_codex":[0.9980906,0.0004872869,0.0003560686,0.0004839312,0.0003694095,0.0002126909],"domain_scores_gemma":[0.9993547,0.0001294604,0.0002205252,0.0001819453,0.00006646072,0.00004692195],"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":[0.00001932514,0.0000513924,0.1138171,0.0001510112,7.991374e-7,2.5923e-7,0.007495278,0.002381671,0.8578687,0.000100583,0.000001079991,0.01811282],"study_design_scores_gemma":[0.0008963695,0.0001274547,0.8062211,0.0002552746,0.00001740033,0.00000476649,0.01177509,0.1159723,0.06414386,0.0002468722,0.000008583192,0.0003310044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899651,0.000003172136,0.007795654,0.0001383541,0.0001957781,0.0004254119,0.000007897373,0.00008180737,0.001386805],"genre_scores_gemma":[0.999088,0.000009340708,0.00051741,0.00005601721,0.00006607901,0.00001597548,0.0000261668,0.00001633255,0.0002046924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7937248,"threshold_uncertainty_score":0.6260637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05393040671792502,"score_gpt":0.3124219752001924,"score_spread":0.2584915684822674,"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."}}