{"id":"W2061677771","doi":"10.1167/9.8.453","title":"Emotional anti-faces reveal contrastive coding of facial expressions","year":2010,"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":"Baycrest Hospital; University of Toronto","funders":"","keywords":"Disgust; Facial expression; Psychology; Cognitive psychology; Facial Action Coding System; Emotional expression; Categorization; Perception; Communication; Social psychology; Anger; Computer science; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002252619,0.0002070389,0.0001632918,0.0002246272,0.0001056315,0.0002390403,0.0002282769,0.000231867,0.0015307],"category_scores_gemma":[0.0009239097,0.0001119277,0.0001806782,0.00007524771,0.0003073809,0.0002208136,0.0003828547,0.0007091623,0.000174824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000132711,"about_ca_system_score_gemma":0.00009144934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002923361,"about_ca_topic_score_gemma":0.0003506624,"domain_scores_codex":[0.9998362,0.00001695155,0.000008654076,0.0000351245,0.00006783725,0.00003514959],"domain_scores_gemma":[0.9996803,0.0001132637,0.00005574,0.00004654178,0.00005510616,0.00004913346],"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":[0.0001351204,0.00001101376,0.0005003865,0.00002008474,0.000004477713,0.00006588973,0.00003856444,0.00004183751,0.9960116,0.0002132107,0.00004321752,0.002914518],"study_design_scores_gemma":[0.00004222908,0.0005388409,0.2842162,0.00002159975,0.00005208815,0.002501159,0.0001850192,0.005529918,0.7028902,0.002008905,0.001979542,0.00003436905],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903714,0.0001688811,0.005332238,0.0000795027,0.0000533931,0.00001665295,0.0001397527,0.0000355253,0.003802594],"genre_scores_gemma":[0.9951726,0.00009477011,0.003510871,0.0001169339,0.000019733,0.00002374113,0.0001648784,0.00003125413,0.0008652872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0015307,"threshold_uncertainty_score":0.005120754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03988566775598206,"score_gpt":0.3385730178798995,"score_spread":0.2986873501239174,"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."}}