{"id":"W3120116539","doi":"10.1101/2020.04.07.030189","title":"Implicit motor learning within three trials","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Implicit learning; Motor learning; Cognitive psychology; Psychology; Adaptation (eye); Physical medicine and rehabilitation; Motor skill; Asymptote; Computer science; Developmental psychology; Neuroscience; Cognition; Mathematics; Medicine","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":[0.001278766,0.0004981967,0.0006955601,0.0002461824,0.0001996025,0.000779483,0.0007016172,0.0007014588,0.005806891],"category_scores_gemma":[0.01552224,0.0002253375,0.0002676185,0.0002953136,0.0005940467,0.0009290733,0.001242039,0.001702535,0.001018943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003794142,"about_ca_system_score_gemma":0.0003553827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007134462,"about_ca_topic_score_gemma":0.0007923677,"domain_scores_codex":[0.9984224,0.0001646856,0.0001783523,0.0004928259,0.0005798477,0.0001619919],"domain_scores_gemma":[0.9926174,0.003686624,0.0008237955,0.001911545,0.0006002809,0.0003602589],"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":[0.004668218,0.001699915,0.02220879,0.000730757,0.0002513703,0.0002772441,0.0007092453,0.01642582,0.6707925,0.004041777,0.001114959,0.2770794],"study_design_scores_gemma":[0.0003317201,0.006518505,0.3378256,0.0002082222,0.0001897849,0.001208002,0.0002572919,0.2637555,0.3584549,0.02376292,0.007234005,0.0002533725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9536787,0.0003910805,0.04016507,0.0001215025,0.0001406177,0.0001964622,0.0003624382,0.0007211479,0.004223013],"genre_scores_gemma":[0.99247,0.00005063324,0.004756592,0.0000463179,0.00001426257,0.0000805539,0.00025097,0.0001106901,0.002219931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005806891,"threshold_uncertainty_score":0.01942599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06056927289251919,"score_gpt":0.263213323153014,"score_spread":0.2026440502604949,"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."}}