{"id":"W2568239085","doi":"10.1167/16.12.917","title":"Rapid category learning in high-level vision: From face instances to person categories","year":2016,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Categorization; Presentation (obstetrics); Psychology; Orientation (vector space); Perception; Face (sociological concept); Test (biology); Scale (ratio); Cognitive psychology; Artificial intelligence; Computer science; Mathematics; Cartography; Medicine; Geography","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":[0.001396966,0.0003497162,0.0006053913,0.0004810858,0.0002054082,0.001430392,0.000633419,0.0006753847,0.001909801],"category_scores_gemma":[0.01597821,0.000491881,0.0003873245,0.0003107314,0.0007331937,0.002672775,0.001334725,0.001223138,0.0004201889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004020925,"about_ca_system_score_gemma":0.0004028808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001389334,"about_ca_topic_score_gemma":0.0009223819,"domain_scores_codex":[0.9991462,0.0002140011,0.00003195991,0.000280051,0.0002316869,0.00009614083],"domain_scores_gemma":[0.9958671,0.00227327,0.0005725913,0.0006403301,0.000303472,0.000343242],"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":[0.00253277,0.0006753954,0.04812208,0.0004850797,0.00014247,0.0003622358,0.002811604,0.007696416,0.5382168,0.004328741,0.001558525,0.3930677],"study_design_scores_gemma":[0.0002905265,0.004017625,0.6216155,0.0001472414,0.0001986265,0.001568722,0.001854665,0.1562742,0.160803,0.04886431,0.004095262,0.0002703493],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.961726,0.0001769991,0.03537738,0.0001104438,0.00002348151,0.00009708895,0.0000846242,0.0002241787,0.002179775],"genre_scores_gemma":[0.9856114,0.0001165969,0.01314009,0.00005922135,0.00001374298,0.00006355894,0.000194876,0.0000310047,0.0007695265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001909801,"threshold_uncertainty_score":0.007387996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0650413428093914,"score_gpt":0.3108450141653392,"score_spread":0.2458036713559478,"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."}}