{"id":"W4311138500","doi":"10.1016/j.patter.2022.100661","title":"Energy efficiency as a normative account for predictive coding","year":2022,"lang":"en","type":"article","venue":"Patterns","topic":"Embodied and Extended Cognition","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Normative; Predictive coding; Coding (social sciences); Computer science; Psychology; Sociology; Political science; Law; Social science","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":[0.0008746194,0.0004128412,0.000327943,0.0004709241,0.0004994425,0.002041757,0.001028874,0.001339671,0.007494177],"category_scores_gemma":[0.006085673,0.0002160967,0.000386532,0.0003821917,0.001900975,0.003077956,0.001163433,0.001332149,0.0007963671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006141939,"about_ca_system_score_gemma":0.0003595525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004790773,"about_ca_topic_score_gemma":0.0007144207,"domain_scores_codex":[0.9997129,0.00008029632,0.0000153972,0.00006779435,0.00008775944,0.00003586863],"domain_scores_gemma":[0.9986032,0.0007515928,0.0001266911,0.0002691601,0.0001975481,0.00005193531],"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":[0.00002639805,0.00002404602,0.0005068977,0.0000509764,0.00002068061,0.00008001844,0.0001290657,0.01334404,0.002111615,0.9701413,0.001488841,0.01207613],"study_design_scores_gemma":[0.000008797172,0.000013101,0.0002947361,0.00001952969,0.000009379806,0.0000633235,0.00004146458,0.05253021,0.001742596,0.9415573,0.003707881,0.00001173296],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1170194,0.001523891,0.7002941,0.0145946,0.0006658679,0.00004291164,0.0003014871,0.0004272904,0.1651304],"genre_scores_gemma":[0.970411,0.0005770403,0.02261951,0.0005683321,0.00008377391,0.00003971509,0.0000953831,0.00008844071,0.005516877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007494177,"threshold_uncertainty_score":0.02507055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03027001359060463,"score_gpt":0.2729704860171762,"score_spread":0.2427004724265715,"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."}}