{"id":"W585637379","doi":"10.1371/journal.pcbi.1004342","title":"Posterior Probability Matching and Human Perceptual Decision Making","year":2015,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Multisensory perception and integration","field":"Psychology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Posterior probability; Matching (statistics); Computer science; Observer (physics); Artificial intelligence; Perception; Pattern recognition (psychology); Mathematics; Statistics; Machine learning; Bayesian probability; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007385683,0.0005322463,0.0006491146,0.00110312,0.0004366496,0.002838356,0.001150281,0.001560675,0.002380652],"category_scores_gemma":[0.05277432,0.0005288493,0.0007702591,0.0008017279,0.003868277,0.003650403,0.00151526,0.001455073,0.0004920193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066788,"about_ca_system_score_gemma":0.0007149561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002171581,"about_ca_topic_score_gemma":0.0005281037,"domain_scores_codex":[0.9935147,0.002972479,0.0002997125,0.001385199,0.001452378,0.0003755328],"domain_scores_gemma":[0.9761921,0.01715878,0.003147277,0.001838261,0.001071068,0.0005925001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001911821,0.0005240766,0.05546967,0.0005641417,0.000486811,0.0006633989,0.006946466,0.1658894,0.03512551,0.4061081,0.002963642,0.323347],"study_design_scores_gemma":[0.00007619919,0.000357685,0.04025583,0.00006813497,0.00004142404,0.0003914131,0.0003758076,0.2584984,0.003336346,0.6942425,0.002210722,0.000145606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5708886,0.001723377,0.3994735,0.001853481,0.0001171133,0.0001325957,0.0001271256,0.0004605038,0.02522366],"genre_scores_gemma":[0.9653137,0.0003446459,0.03301537,0.0001640445,0.00005238298,0.00005904671,0.00007168335,0.00004596545,0.0009331115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007385683,"threshold_uncertainty_score":0.0390597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1399680368606955,"score_gpt":0.4047692429283204,"score_spread":0.2648012060676249,"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."}}