{"id":"W2036936746","doi":"10.1167/12.9.1283","title":"Individual differences in the visual strategies underlying facial expression categorization","year":2012,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais; Université de Montréal","funders":"","keywords":"Categorization; Facial expression; Artificial intelligence; Pattern recognition (psychology); Eye tracking; Task (project management); Population; Psychology; Cognitive psychology; Face (sociological concept); Computer science; Medicine","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":[0.001245223,0.0002732669,0.0002789518,0.0006416197,0.0001308498,0.0005733723,0.0001722847,0.0003620014,0.001947101],"category_scores_gemma":[0.007997522,0.0002161595,0.0001921293,0.0002012834,0.0003759092,0.0004451704,0.0004291739,0.0002987119,0.0003389536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009295037,"about_ca_system_score_gemma":0.00008866287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007765978,"about_ca_topic_score_gemma":0.0007778321,"domain_scores_codex":[0.9992847,0.0001287815,0.00008728141,0.0002285989,0.0001857234,0.00008487047],"domain_scores_gemma":[0.9964289,0.001739904,0.0006932078,0.000566413,0.0003700387,0.0002016007],"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":[0.002308996,0.0003867448,0.3503859,0.0001845815,0.0003749344,0.0002390665,0.003047654,0.002296495,0.5581621,0.0005648969,0.0006287274,0.08141995],"study_design_scores_gemma":[0.00001397227,0.0003304395,0.9881897,0.000006063557,0.00002206133,0.0002052969,0.0002006571,0.001949787,0.00847208,0.0003343755,0.0002551584,0.00002052226],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975919,0.00006370895,0.001455972,0.00001521851,0.000005417843,0.00002217476,0.00007414659,0.00001605903,0.0007553895],"genre_scores_gemma":[0.998406,0.0000339398,0.0009731618,0.00001305151,0.000003912937,0.00002198941,0.00008703086,0.00001357926,0.0004472923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001947101,"threshold_uncertainty_score":0.006585419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1520462281731711,"score_gpt":0.3889667216506906,"score_spread":0.2369204934775195,"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."}}