{"id":"W3111260761","doi":"10.1038/s42003-020-01465-4","title":"Movement errors during skilled motor performance engage distinct prediction error mechanisms","year":2020,"lang":"en","type":"article","venue":"Communications Biology","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Ministero dello Sviluppo Economico; Government of Canada; Fonds de Recherche du Québec - Santé; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Salience (neuroscience); Functional magnetic resonance imaging; Computer science; Valence (chemistry); Psychology; Computation; Cognitive psychology; Mean squared prediction error; Assertion; Artificial intelligence; Neuroscience; Machine learning; Algorithm; Physics","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":[0.0002319578,0.0002239141,0.0001684011,0.0002092114,0.00009329608,0.0003483835,0.0001421231,0.0002700483,0.000914378],"category_scores_gemma":[0.001314756,0.0001222541,0.0001250744,0.0001289976,0.000282953,0.0003403573,0.000343391,0.000316875,0.00009730673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000177595,"about_ca_system_score_gemma":0.0001566399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000446068,"about_ca_topic_score_gemma":0.0007349731,"domain_scores_codex":[0.9998481,0.00002227454,0.00001062847,0.00003748182,0.0000571547,0.0000243193],"domain_scores_gemma":[0.9994426,0.0001972721,0.0002142848,0.00004530328,0.00004953028,0.00005088802],"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":[0.0007497069,0.0001274963,0.0208271,0.00007921401,0.00005053106,0.0001207141,0.0002517205,0.001348106,0.9407626,0.0007747373,0.00008783449,0.03482018],"study_design_scores_gemma":[0.00003301499,0.000808242,0.8309267,0.0000215265,0.00004650517,0.0003160774,0.0001503875,0.01584259,0.1490763,0.002338483,0.00041117,0.00002898647],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910951,0.0001225776,0.007435378,0.00004726588,0.00001139779,0.000014405,0.0000475314,0.00004808246,0.001178433],"genre_scores_gemma":[0.998166,0.00004714453,0.001381462,0.00001030485,0.000005939391,0.000007442166,0.00003135813,0.00000896752,0.0003413908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000914378,"threshold_uncertainty_score":0.003058851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2127940108630454,"score_gpt":0.3705313617621654,"score_spread":0.15773735089912,"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."}}