{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001040919,0.00008940826,0.00008462527,0.00007761656,0.0004782114,0.00003122802,0.0001885161,0.0000160706,0.000280612],"category_scores_gemma":[0.00009753559,0.0000895997,0.00005602806,0.0001292923,0.00002409408,0.0001332176,0.0001287196,0.0001049104,0.00002408893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008445299,"about_ca_system_score_gemma":0.00003048374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005773605,"about_ca_topic_score_gemma":0.000006425679,"domain_scores_codex":[0.9990316,0.00007664396,0.0001236731,0.0002731389,0.0002578744,0.0002370593],"domain_scores_gemma":[0.9995383,0.0002083862,0.00007558013,0.0001079185,0.00002808316,0.00004170251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001301127,0.001867429,0.002779273,0.0002324899,0.00006310217,0.0001935461,0.03044667,0.004954366,0.1381941,0.7449443,0.006765017,0.06825855],"study_design_scores_gemma":[0.003426944,0.002944234,0.0005752011,0.00008266835,0.00008648232,0.0002331555,0.007326509,0.1413274,0.5278924,0.2896719,0.02535111,0.001082062],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4213658,0.00004638391,0.2390244,0.001959295,0.002922573,0.001379967,0.00311511,0.0006059013,0.3295806],"genre_scores_gemma":[0.9965051,0.000009328062,0.00001362594,0.002635299,0.00008836687,0.0004560243,0.00002331824,0.0000129469,0.0002560138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5751393,"threshold_uncertainty_score":0.3678063,"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."}}