{"id":"W2905190603","doi":"10.1007/s00221-018-5456-3","title":"Correcting for natural visuo-proprioceptive matching errors based on reward as opposed to error feedback does not lead to higher retention","year":2018,"lang":"en","type":"article","venue":"Experimental Brain Research","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of British Columbia","funders":"Canadian Institutes of Health Research; Ministerie van Economische Zaken, Landbouw en Innovatie; Stichting voor de Technische Wetenschappen; Natural Sciences and Engineering Research Council of Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Proprioception; Matching (statistics); Visual feedback; Task (project management); Motor learning; Psychology; Hand position; Computer science; Cognitive psychology; Artificial intelligence; Neuroscience; Mathematics; Statistics","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":[0.001031732,0.0004599807,0.0006393995,0.0001683973,0.0001523174,0.0005970852,0.0006970981,0.0006147856,0.003846331],"category_scores_gemma":[0.006733308,0.0001887451,0.0002560355,0.0001163523,0.0004396675,0.0009921413,0.0006321004,0.0008190735,0.0006630401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001605544,"about_ca_system_score_gemma":0.0003553004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005870538,"about_ca_topic_score_gemma":0.0005034056,"domain_scores_codex":[0.9992524,0.00008740696,0.0001007722,0.0001737687,0.0002681676,0.0001175549],"domain_scores_gemma":[0.9971102,0.0009450604,0.0007337256,0.0006841652,0.0002883085,0.000238439],"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":[0.002231549,0.0009397433,0.004726063,0.0003316958,0.00006103665,0.00008250889,0.0002281214,0.0007557739,0.9203358,0.0002796897,0.0002220457,0.06980594],"study_design_scores_gemma":[0.0001755883,0.0103612,0.06634548,0.00005270096,0.00009313071,0.0004598867,0.0001551231,0.01268318,0.9061679,0.001620935,0.001815252,0.00006969416],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895449,0.0001664907,0.009035296,0.00006445505,0.00003797683,0.00002009784,0.00003692253,0.0002392928,0.0008545754],"genre_scores_gemma":[0.9942474,0.00006223021,0.004316048,0.00003720485,0.000008426578,0.00002416933,0.00006923101,0.00006273666,0.00117253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003846331,"threshold_uncertainty_score":0.01286721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1393456867851594,"score_gpt":0.4277210716177097,"score_spread":0.2883753848325503,"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."}}