{"id":"W3006469780","doi":"10.1152/jn.00613.2019","title":"Reaching decisions during ongoing movements","year":2020,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Context (archaeology); Action (physics); Computer science; Internal model; Deliberation; Task (project management); Process (computing); Preference; Cognitive psychology; Movement (music); Point (geometry); Psychology; Control (management); Artificial intelligence; Mathematics; 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":[0.0006979805,0.0002369141,0.000259073,0.000416434,0.0003747745,0.001074553,0.0003085121,0.0006177594,0.003809322],"category_scores_gemma":[0.008643892,0.0002192239,0.000284188,0.0002479072,0.0005537727,0.0009818873,0.000740017,0.0004290458,0.0005072414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002983015,"about_ca_system_score_gemma":0.0002903045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001083593,"about_ca_topic_score_gemma":0.00137581,"domain_scores_codex":[0.9993112,0.0001611442,0.00004105335,0.0002132752,0.0002112183,0.00006210327],"domain_scores_gemma":[0.9975159,0.001447489,0.0004881529,0.0001919224,0.0001815791,0.0001749616],"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":[0.002142098,0.0002562853,0.1179759,0.0005937855,0.0003883216,0.002166464,0.008470301,0.02889237,0.4600866,0.02146843,0.001752543,0.3558069],"study_design_scores_gemma":[0.0001656307,0.001562572,0.8010936,0.0002361696,0.0002137572,0.001686103,0.002216706,0.0985105,0.02235348,0.06012301,0.01165366,0.0001848521],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774227,0.000567993,0.0103565,0.0001370673,0.0000205199,0.00003882438,0.00007417663,0.00008364643,0.01129857],"genre_scores_gemma":[0.9969288,0.0001118323,0.001739295,0.00001815417,0.00001203925,0.00001207902,0.00006050149,0.00001207865,0.001105376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003809322,"threshold_uncertainty_score":0.01274347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06030328987521991,"score_gpt":0.2741416108476148,"score_spread":0.2138383209723949,"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."}}