{"id":"W4407100281","doi":"10.1111/cogs.70040","title":"Virtual Partners Improve Synchronization in Human−Machine Trios","year":2025,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Synchronization (alternating current); Computer science; Rhythm; Virtual machine; Virtual actor; Psychology; Human–computer interaction; Virtual reality; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006916489,0.0003764311,0.000245206,0.0002413131,0.0002259254,0.0006165857,0.0002631964,0.0002817269,0.002403977],"category_scores_gemma":[0.004710395,0.0001520852,0.000252643,0.00009980318,0.0004123056,0.000759232,0.001810986,0.000202317,0.0003286106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001185607,"about_ca_system_score_gemma":0.0001588206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002899999,"about_ca_topic_score_gemma":0.0003443931,"domain_scores_codex":[0.9995346,0.0002153832,0.00003336247,0.00009235943,0.00008063918,0.00004364677],"domain_scores_gemma":[0.9989243,0.0005174868,0.0001805102,0.0001767571,0.00005325563,0.0001476521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00403853,0.001158361,0.05755489,0.0005016212,0.0003327564,0.001024293,0.006873234,0.120069,0.5929969,0.009719689,0.0009865891,0.2047442],"study_design_scores_gemma":[0.0005175794,0.01446465,0.1566744,0.0001159048,0.0004012516,0.001630783,0.004758378,0.6139607,0.1713569,0.02383472,0.01203683,0.000247955],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9649961,0.00005512273,0.0332793,0.00003374586,0.00002180048,0.0000273494,0.00002059876,0.0001112457,0.001454741],"genre_scores_gemma":[0.9964308,0.00002292426,0.003190569,0.000006225274,0.000002795702,0.00001239861,0.00002274367,0.000011075,0.0003004326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002403977,"threshold_uncertainty_score":0.008042097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03248449266685236,"score_gpt":0.3562218836914574,"score_spread":0.323737391024605,"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."}}