{"id":"W4297990189","doi":"10.1371/journal.pcbi.1010579","title":"Modeling enculturated bias in entrainment to rhythmic patterns","year":2022,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Engineering and Physical Sciences Research Council; Arts and Humanities Research Council","keywords":"Rhythm; Entrainment (biomusicology); Computer science; Inference; Artificial intelligence; Interval (graph theory); Categorization; Mathematics; Physics","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":[0.0009073932,0.0003409076,0.0003088809,0.0002792088,0.0001845697,0.0005887577,0.0009161312,0.0007845355,0.001560642],"category_scores_gemma":[0.006872609,0.0003805081,0.0004792022,0.0002385681,0.0007418749,0.0008081964,0.0007117684,0.001045653,0.0001375544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00078213,"about_ca_system_score_gemma":0.0004959239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007925064,"about_ca_topic_score_gemma":0.008888658,"domain_scores_codex":[0.9998031,0.00007147935,0.00000821896,0.00006367692,0.00002342892,0.00003006732],"domain_scores_gemma":[0.997911,0.001477055,0.0002644277,0.0001626954,0.00009248092,0.00009230205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009223896,0.00003629253,0.007152209,0.00003230252,0.00003761459,0.00009574053,0.0001310616,0.9701704,0.004215508,0.01063966,0.0002562247,0.007140715],"study_design_scores_gemma":[0.00000860639,0.00001295376,0.00100239,0.000002257958,0.000003582408,0.00001313782,0.000006970644,0.9948419,0.0003930808,0.003643452,0.0000673035,0.000004347855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8013586,0.0001153925,0.1956535,0.0002604267,0.00001792915,0.00003301357,0.0002631847,0.0001794856,0.002118347],"genre_scores_gemma":[0.9846117,0.00004521606,0.0142689,0.00003234048,0.00000650236,0.00003139004,0.0001322395,0.00003376098,0.0008379204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007925064,"threshold_uncertainty_score":0.01575786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1298813788254958,"score_gpt":0.3133258597916617,"score_spread":0.1834444809661659,"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."}}