{"id":"W2972861459","doi":"10.1017/s0140525x19001018","title":"Tinkering with cognitive gadgets: Cultural evolutionary psychology meets active inference","year":2019,"lang":"en","type":"article","venue":"Behavioral and Brain Sciences","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Perspective (graphical); Cognition; Evolutionary psychology; Cognitive science; Psychology; Through-the-lens metering; Epistemology; Lens (geology); Social psychology; Computer science; Artificial intelligence; Biology; Philosophy; Neuroscience","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":[0.0002478147,0.0001072205,0.0001192173,0.00004305972,0.0007041674,0.000098663,0.000169158,0.00007442805,0.0003406052],"category_scores_gemma":[0.00005829095,0.00006692282,0.00002700546,0.0003669816,0.001418351,0.0009187867,0.00004637853,0.0001040699,0.00006068965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003306053,"about_ca_system_score_gemma":0.00009854844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001504116,"about_ca_topic_score_gemma":0.001249367,"domain_scores_codex":[0.9987821,0.0001076681,0.00009978025,0.0003445351,0.0003464793,0.0003194476],"domain_scores_gemma":[0.9995856,0.00008098812,0.00006819728,0.00005116149,0.0001202826,0.00009378971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004216897,0.000550552,0.5222097,0.00003086953,0.00004555527,0.00006916541,0.1570429,0.00001385005,0.02822091,0.04265777,0.002878078,0.245859],"study_design_scores_gemma":[0.001334986,0.00164313,0.7995238,0.0002429826,0.00005831262,0.00005106489,0.1793108,0.00005929612,0.0004894758,0.002127223,0.01433965,0.0008192553],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816259,0.0002349813,0.000008535012,0.002707557,0.0001367722,0.0002311505,0.00000851721,0.00005371943,0.01499284],"genre_scores_gemma":[0.9980361,0.00004666038,0.0002481707,0.0003888976,0.00006166298,0.00001548321,0.000007044312,0.000002674567,0.001193325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2773142,"threshold_uncertainty_score":0.5415957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05660776125343223,"score_gpt":0.4116852724534345,"score_spread":0.3550775112000022,"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."}}