{"id":"W4402912291","doi":"10.1167/jov.24.10.993","title":"N170 and N250 sensitivity to diagnostic facial information during whole-face recognition","year":2024,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Face (sociological concept); Sensitivity (control systems); Facial recognition system; Artificial intelligence; Computer science; Pattern recognition (psychology); Engineering; Linguistics; Philosophy","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":[],"consensus_categories":[],"category_scores_codex":[0.0004678747,0.00007132781,0.0001193162,0.0003617337,0.0000795911,0.0004367907,0.00007100251,0.00003788494,0.00001446661],"category_scores_gemma":[0.0003378307,0.00005897977,0.00007268418,0.0003262784,0.000009335296,0.001756625,0.00006872921,0.0001453204,0.0002203457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004198468,"about_ca_system_score_gemma":0.00002815512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003949984,"about_ca_topic_score_gemma":0.00000483262,"domain_scores_codex":[0.999205,0.00006169959,0.0002727336,0.00009106809,0.0002703876,0.00009912626],"domain_scores_gemma":[0.9993799,0.000193208,0.00009243449,0.00006583589,0.0001527305,0.0001159103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001348296,0.00002559153,0.00009185607,0.00005938565,0.00002336259,0.0000951344,0.001113356,0.0002981577,0.01338046,0.00002964053,0.0009845973,0.983885],"study_design_scores_gemma":[0.003174923,0.002175493,0.3268544,0.007046131,0.0003482081,0.004350385,0.001552565,0.5404541,0.04614125,0.006322122,0.06001735,0.001563059],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8239402,0.00009374377,0.171887,0.003516281,0.0003628664,0.00005539898,0.000007233186,0.00003561852,0.0001017088],"genre_scores_gemma":[0.9961091,0.0001398581,0.003437509,0.0001836151,0.00009786706,6.16572e-7,0.000002524091,0.000002784352,0.00002609154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9823219,"threshold_uncertainty_score":0.4211983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009445116193350794,"score_gpt":0.2552765547033836,"score_spread":0.2458314385100328,"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."}}