{"id":"W2910203464","doi":"10.1152/jn.00041.2019","title":"Sequence learning is driven by improvements in motor planning","year":2019,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Motor planning; Motor learning; Neuroscience; Sequence (biology); Computer science; Sequence learning; Psychology; Communication; Cognitive psychology; Artificial intelligence; Biology","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":[0.0003393379,0.0004092777,0.0003034206,0.0002001382,0.0001046997,0.0003110144,0.0003039209,0.0002940054,0.003106469],"category_scores_gemma":[0.00125444,0.0001932891,0.0002474857,0.0001303897,0.0004504908,0.0005213865,0.0003378628,0.0006763412,0.0004481079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001833694,"about_ca_system_score_gemma":0.000287102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006403814,"about_ca_topic_score_gemma":0.000763906,"domain_scores_codex":[0.9998036,0.00003064638,0.00001629215,0.00007725359,0.0000490535,0.00002310308],"domain_scores_gemma":[0.9991813,0.0001917651,0.0003300745,0.0001065164,0.00008861089,0.0001016682],"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":[0.0007087251,0.0002431173,0.006607317,0.0003183887,0.00005657736,0.0001626457,0.00008878198,0.001728731,0.9067755,0.000558465,0.0002507214,0.08250117],"study_design_scores_gemma":[0.0001511248,0.006681435,0.3777968,0.0001083642,0.0001331332,0.001393302,0.000144596,0.01869293,0.5819654,0.005788107,0.007083258,0.00006149276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9746017,0.001332041,0.02032089,0.0001624327,0.00004698558,0.0000567063,0.0001748385,0.0002808125,0.003023781],"genre_scores_gemma":[0.9866347,0.0007729585,0.01026981,0.00006218076,0.00002154845,0.00005882896,0.0002055613,0.00005174496,0.001922684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003106469,"threshold_uncertainty_score":0.01039219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03072188912966535,"score_gpt":0.2778968652346169,"score_spread":0.2471749761049515,"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."}}