{"id":"W2011355503","doi":"10.1167/8.6.707","title":"The use of spatio-temporal Information in decoding facial expression of emotions","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Facial expression; Categorization; Happiness; Psychology; Computer science; Expression (computer science); Cognitive psychology; Octave (electronics); Feeling; Speech recognition; Artificial intelligence; Communication; Social psychology","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":[0.0005729167,0.0003197019,0.0002986897,0.0008948164,0.0002245133,0.0006637571,0.0002193941,0.0003672876,0.00149363],"category_scores_gemma":[0.006059914,0.0002070795,0.0002239203,0.0005830125,0.0002212223,0.0009956937,0.0004383572,0.0002842707,0.0004818295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001883541,"about_ca_system_score_gemma":0.000179516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002116442,"about_ca_topic_score_gemma":0.004143833,"domain_scores_codex":[0.9997155,0.00005487143,0.00001609038,0.00007643199,0.00009264868,0.00004452392],"domain_scores_gemma":[0.9991278,0.0004077527,0.0001252786,0.00008503634,0.0001996616,0.00005450226],"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":[0.001822698,0.0001448227,0.05795226,0.0003698081,0.00009604485,0.0001928769,0.001079359,0.004689024,0.684228,0.0009700279,0.001219163,0.2472359],"study_design_scores_gemma":[0.00004487728,0.0004704878,0.783973,0.0000914364,0.0002330337,0.0009680399,0.0008601398,0.1186389,0.09039659,0.00237655,0.001858212,0.00008875193],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.952783,0.0004899701,0.040052,0.00009952602,0.0000393536,0.0000797834,0.0005604436,0.0001373067,0.005758798],"genre_scores_gemma":[0.9816146,0.0003424142,0.01707145,0.00003787168,0.00002654483,0.00004217066,0.0003001366,0.00003228992,0.0005324114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002116442,"threshold_uncertainty_score":0.004996717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06941013808723402,"score_gpt":0.330355224446351,"score_spread":0.260945086359117,"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."}}