{"id":"W4200627451","doi":"10.1152/jn.00229.2021","title":"Clustering analysis of movement kinematics in reinforcement learning","year":2021,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cluster analysis; Reinforcement learning; Kinematics; Task (project management); Movement (music); Motor learning; Artificial intelligence; Trajectory; Reinforcement; Computer science; Psychology; Machine learning; Cognitive psychology; Engineering; Social psychology","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":[0.001759895,0.0003907638,0.0005741174,0.001193637,0.0003967633,0.000523773,0.0006300918,0.0004299152,0.000624328],"category_scores_gemma":[0.007075869,0.0002732422,0.0005396766,0.0007756419,0.0005578109,0.0004119765,0.0004774249,0.0005991859,0.0002074627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007151105,"about_ca_system_score_gemma":0.0005943666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003471139,"about_ca_topic_score_gemma":0.003433519,"domain_scores_codex":[0.9991315,0.0003657596,0.00005556105,0.0001941175,0.0001892697,0.00006387138],"domain_scores_gemma":[0.9975377,0.001194528,0.0003287364,0.0003570095,0.0004999741,0.00008216718],"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":[0.0004828969,0.000303954,0.01912794,0.0001877003,0.0002883014,0.0001557847,0.0005167597,0.7017,0.06843267,0.009627233,0.0007942361,0.1983825],"study_design_scores_gemma":[0.000006063255,0.00005966274,0.01115868,0.000007997348,0.000009793808,0.00004857346,0.00003067489,0.9795235,0.005029819,0.00372717,0.0003717226,0.00002627614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3188465,0.0001783989,0.679013,0.00009470484,0.00002568066,0.0001524147,0.0001886066,0.000607124,0.0008937256],"genre_scores_gemma":[0.888791,0.00005175666,0.1100243,0.00001490009,0.00000593627,0.000142428,0.0002623381,0.00008332919,0.0006239098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003471139,"threshold_uncertainty_score":0.009307384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03167447653695956,"score_gpt":0.2719593260173036,"score_spread":0.240284849480344,"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."}}