{"id":"W4213422567","doi":"10.1152/jn.00631.2020","title":"Motor skill learning decreases movement variability and increases planning horizon","year":2022,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft","keywords":"Movement (music); Motor learning; Motor planning; Horizon; Motor skill; Time horizon; Motor control; Control (management); Computer science; Movement control; Psychology; Cognitive psychology; Artificial intelligence; Physical medicine and rehabilitation; Neuroscience; Mathematics; Mathematical optimization; 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":[0.0003526507,0.0004020835,0.0003355268,0.0001860022,0.0001125117,0.0004368613,0.000397911,0.0003489205,0.002621695],"category_scores_gemma":[0.004614826,0.0001261774,0.0001689193,0.0001684848,0.000302208,0.000684101,0.0004714264,0.0005450127,0.0002624842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000244078,"about_ca_system_score_gemma":0.000525419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001370829,"about_ca_topic_score_gemma":0.001389439,"domain_scores_codex":[0.9997613,0.00004127918,0.00001672892,0.00007794714,0.00005732397,0.00004550021],"domain_scores_gemma":[0.9972098,0.001631492,0.0005704704,0.0003351308,0.0001162426,0.0001367404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001515686,0.002068504,0.01102911,0.0004230055,0.0001575109,0.000213502,0.0001741321,0.4222602,0.2412072,0.009571632,0.001213709,0.3101659],"study_design_scores_gemma":[0.0001516873,0.002368791,0.0228074,0.00003429712,0.00009420412,0.0001733995,0.00004715638,0.8888339,0.06577949,0.01804748,0.001614672,0.0000476414],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8744502,0.0002370466,0.1198973,0.0003859447,0.00004324352,0.00002770051,0.0001125108,0.000562739,0.004283318],"genre_scores_gemma":[0.9877706,0.0000646885,0.01136459,0.00003511603,0.000009548919,0.00001363238,0.00004353518,0.00002657422,0.0006718049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002621695,"threshold_uncertainty_score":0.008770406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02034697340269134,"score_gpt":0.2529639758415723,"score_spread":0.2326170024388809,"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."}}