{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005702853,0.0001379671,0.0001292967,0.0005644954,0.0005633518,0.0001574842,0.0004726406,0.00003938369,0.00006320565],"category_scores_gemma":[0.00279565,0.0001296248,0.00003220545,0.003241644,0.001605758,0.0009628166,0.0001931693,0.000192075,0.00006053512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001418595,"about_ca_system_score_gemma":0.0003692425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002230593,"about_ca_topic_score_gemma":0.00002663512,"domain_scores_codex":[0.997911,0.0000898011,0.0002284768,0.0008671824,0.0004521466,0.000451379],"domain_scores_gemma":[0.9993786,0.000173595,0.00006946339,0.0001884105,0.0001052409,0.00008465972],"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":[0.00001296925,0.00008982136,0.0004704277,0.000003652719,1.815849e-7,0.00000922422,0.0002478916,0.00001973447,0.9646333,0.005315825,0.00002647452,0.02917052],"study_design_scores_gemma":[0.001334733,0.000277922,0.03794004,0.0001404152,0.000009464653,0.000006673251,0.0005603661,0.01173022,0.9458022,0.001589663,0.0002976709,0.0003105984],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664945,0.000006112759,0.009098805,0.000179142,0.0007294751,0.0004125474,0.00001161525,0.00007579311,0.02299202],"genre_scores_gemma":[0.9957753,0.00001722291,0.00001071621,0.002696312,0.00002889041,0.00003795909,0.000001830838,0.000005584428,0.001426121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03746961,"threshold_uncertainty_score":0.5916481,"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."}}