{"id":"W2560923582","doi":"10.3389/fpsyg.2016.02010","title":"It Pays to Go Off-Track: Practicing with Error-Augmenting Haptic Feedback Facilitates Learning of a Curve-Tracing Task","year":2016,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; York University; University of Toronto; Toronto Rehabilitation Institute","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tracing; Task (project management); Haptic technology; Motor learning; Computer science; Learning curve; Error detection and correction; Transfer of learning; Motor skill; Artificial intelligence; Word error rate; Dreyfus model of skill acquisition; Simulation; Machine learning; Psychology; Algorithm; Engineering","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":[0.000571028,0.0001942211,0.0003519284,0.0003131689,0.00009849651,0.00002236512,0.0002409393,0.00009302366,0.00003924998],"category_scores_gemma":[0.001530619,0.0001431964,0.00005384523,0.0003850824,0.0001494586,0.000360816,0.00003847999,0.0002545186,0.00006592717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005990181,"about_ca_system_score_gemma":0.00003059617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001723755,"about_ca_topic_score_gemma":0.00003074756,"domain_scores_codex":[0.9977986,0.0003466422,0.0004684567,0.0006417331,0.0002395873,0.0005050417],"domain_scores_gemma":[0.9988357,0.0004420338,0.0003092751,0.000266723,0.00005348057,0.0000927861],"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":[0.0008568001,0.0001735186,0.06324711,0.00004792043,0.00002906296,0.000057536,0.003613271,0.0006554072,0.7437633,0.0001265999,0.001752354,0.1856771],"study_design_scores_gemma":[0.04863762,0.01311432,0.3853314,0.006166587,0.0004540959,0.0007369567,0.01864436,0.04891258,0.1036955,0.01417789,0.3541149,0.006013751],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.576322,0.0001882892,0.4147811,0.004095913,0.000900864,0.0004581191,0.000008993521,0.000065463,0.003179283],"genre_scores_gemma":[0.9896512,0.0000691389,0.008506903,0.0008238616,0.00004271072,0.00003844358,0.000001022482,0.00002629835,0.0008404118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6400678,"threshold_uncertainty_score":0.5839379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02846121491004753,"score_gpt":0.2999891749500828,"score_spread":0.2715279600400352,"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."}}