{"id":"W2040625029","doi":"10.1016/j.humov.2014.11.005","title":"How one breaks Fitts’s Law and gets away with it: Moving further and faster involves more efficient online control","year":2014,"lang":"en","type":"article","venue":"Human Movement Science","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Research Manitoba; University of Toronto; University of Manitoba","funders":"Research and Innovation Foundation; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canada Foundation for Innovation","keywords":"Fitts's law; Context (archaeology); Kinematics; Movement (music); Computer science; Control (management); Motor control; Psychology; Cognitive psychology; Position (finance); Law; Physical medicine and rehabilitation; Artificial intelligence; Communication; Neuroscience; Biology; Political science; Physics; 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":[0.001134055,0.0004629697,0.0005350729,0.0003978167,0.0005784914,0.00332484,0.001029727,0.001587486,0.01590726],"category_scores_gemma":[0.0109593,0.0004203157,0.0004581231,0.000348629,0.002866442,0.009296534,0.001354807,0.00204217,0.002645574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004339635,"about_ca_system_score_gemma":0.0007115717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001705642,"about_ca_topic_score_gemma":0.001121892,"domain_scores_codex":[0.9990318,0.0001961157,0.0000488313,0.0004326459,0.0002033997,0.00008727197],"domain_scores_gemma":[0.9975709,0.0009306601,0.0002554728,0.0007509137,0.0002767003,0.0002153508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006955339,0.000424174,0.006749207,0.0003727467,0.0003577624,0.0003525418,0.002324693,0.02311194,0.05108551,0.3971129,0.0252097,0.4922034],"study_design_scores_gemma":[0.0001121511,0.0002266617,0.005704161,0.00009167531,0.0001371095,0.0003045913,0.000820817,0.1143297,0.01099832,0.8464637,0.02067925,0.0001319953],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2824768,0.002158998,0.5776047,0.02771576,0.002557998,0.00007754801,0.0002260488,0.002820062,0.1043621],"genre_scores_gemma":[0.9226597,0.0004436527,0.06158536,0.001406811,0.0001888338,0.00003924307,0.00008385574,0.0008192647,0.01277327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01590726,"threshold_uncertainty_score":0.05321509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02471722416864399,"score_gpt":0.2403016254110472,"score_spread":0.2155844012424032,"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."}}