{"id":"W2167613311","doi":"10.1016/j.cogsys.2016.04.002","title":"Neural implementation of probabilistic models of cognition","year":2016,"lang":"en","type":"preprint","venue":"Cognitive Systems Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Bayes' theorem; Computer science; Artificial intelligence; Probabilistic logic; Artificial neural network; Bayesian probability; Machine learning; Bayesian inference; Inference; Matching (statistics); Bayesian network; Mathematics; Statistics","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":[0.0008229837,0.0002950497,0.0003784788,0.0003818102,0.0002467014,0.001657559,0.001283724,0.001033265,0.004335703],"category_scores_gemma":[0.006904865,0.0003850743,0.0005194888,0.0004175832,0.0006665828,0.002950209,0.0008046151,0.001363348,0.000359324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006554786,"about_ca_system_score_gemma":0.0005693709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002123059,"about_ca_topic_score_gemma":0.00216702,"domain_scores_codex":[0.9996409,0.0001314339,0.00001788878,0.00007685005,0.00009078858,0.0000420731],"domain_scores_gemma":[0.998697,0.0007156823,0.0001328703,0.0001883026,0.0001847942,0.00008141407],"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":[0.0001131783,0.00007101378,0.00147094,0.0001267978,0.0001190022,0.00008500712,0.0001862373,0.2391586,0.005802081,0.6816434,0.002365868,0.06885789],"study_design_scores_gemma":[0.000008338631,0.00001324005,0.000588895,0.000008957536,0.00001310673,0.00003356906,0.00001763409,0.5798622,0.0005365572,0.4182436,0.0006625512,0.00001120396],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1130966,0.0007069553,0.8570538,0.00374932,0.0002730481,0.00002667263,0.0003425616,0.0004719445,0.02427901],"genre_scores_gemma":[0.9598893,0.0004157045,0.03541605,0.0001478602,0.00008856213,0.00003422903,0.0001361867,0.00004095171,0.003831202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004335703,"threshold_uncertainty_score":0.01450437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1968756832622374,"score_gpt":0.43981464435565,"score_spread":0.2429389610934126,"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."}}