{"id":"W4361214375","doi":"10.1038/s41598-023-32068-8","title":"Adapting to visuomotor rotations in stepped increments increases implicit motor learning","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motor learning; Computer science; Motor skill; Physical medicine and rehabilitation; Cognitive psychology; Artificial intelligence; Neuroscience; Psychology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001611806,0.0001452558,0.0001780124,0.0007008097,0.0005616192,0.0003710539,0.000168124,0.00004060436,0.00009792396],"category_scores_gemma":[0.004515481,0.0001448197,0.00006492825,0.002082664,0.00005512795,0.0003507781,0.0001451804,0.0001432955,0.0003126641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006689264,"about_ca_system_score_gemma":0.0001108333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002871777,"about_ca_topic_score_gemma":0.0001076313,"domain_scores_codex":[0.9971383,0.0001799374,0.0005813016,0.0009138377,0.0006952368,0.0004913692],"domain_scores_gemma":[0.9988737,0.0002203859,0.0002417548,0.000396092,0.00008333311,0.0001847607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000154185,0.00004563047,0.006511096,0.000009023764,0.000001680051,0.0002852968,0.0004218459,0.002754968,0.9863922,0.00007418869,0.0003603505,0.003128306],"study_design_scores_gemma":[0.00221816,0.0005378806,0.5799068,0.0005410799,0.0000470769,0.0002727644,0.002856337,0.1421663,0.1791487,0.00768217,0.08284077,0.001782044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958173,0.000006664999,0.0003329248,0.0001666301,0.001599125,0.0008407214,0.00000471434,0.0002828868,0.0009490343],"genre_scores_gemma":[0.9922184,0.000001536607,0.0002014311,0.0001075656,0.00006608367,0.0001940888,0.00002246589,0.00002124369,0.007167124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8072435,"threshold_uncertainty_score":0.5905578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03916075082961375,"score_gpt":0.2948378245102653,"score_spread":0.2556770736806516,"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."}}