{"id":"W4385481140","doi":"10.1371/journal.pcbi.1011280","title":"Hybrid predictive coding: Inferring, fast and slow","year":2023,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Research Council; Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Canadian Institute for Advanced Research; Directorate for Biological Sciences; Medical Research Council; National Institutes of Natural Sciences; Dr Mortimer and Theresa Sackler Foundation; University of Sussex","keywords":"Inference; Computer science; Artificial intelligence; Machine learning; Coding (social sciences); Artificial neural network; Bayesian inference; Algorithm; Pattern recognition (psychology); Bayesian probability; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001219147,0.0005473699,0.0005423958,0.0004159477,0.0004118572,0.001297695,0.001848359,0.000874766,0.001566778],"category_scores_gemma":[0.004719536,0.0004369963,0.0005723177,0.0005006696,0.001826984,0.002543835,0.001335442,0.001624713,0.0002786878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009363078,"about_ca_system_score_gemma":0.0007272447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004697782,"about_ca_topic_score_gemma":0.003980381,"domain_scores_codex":[0.9995582,0.0001167696,0.00002141629,0.0001143745,0.0001257181,0.00006354268],"domain_scores_gemma":[0.997897,0.001326715,0.0001731247,0.0003039569,0.000220094,0.00007907893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002319822,0.00007422004,0.002018178,0.000100114,0.00009755231,0.0002058348,0.0003403462,0.7036194,0.0213632,0.1511117,0.00137441,0.119463],"study_design_scores_gemma":[0.000004474361,0.00001433859,0.0001224099,0.000004595659,0.000009848631,0.00002769901,0.000007461027,0.968654,0.001721403,0.02916323,0.0002625609,0.000007833598],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04592423,0.0002327641,0.9502935,0.000353271,0.00003712767,0.00003099463,0.00004322785,0.0004557568,0.002628995],"genre_scores_gemma":[0.8905509,0.0002217195,0.1058237,0.00017596,0.00004489224,0.00008168443,0.00006889817,0.00009784406,0.002934458],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004697782,"threshold_uncertainty_score":0.009340823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03957638592692329,"score_gpt":0.2667193103713958,"score_spread":0.2271429244444726,"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."}}