{"id":"W2109034827","doi":"10.1123/mcj.8.2.213","title":"Contextual Interference: Single Task versus Multi-task Learning","year":2004,"lang":"en","type":"article","venue":"Motor Control","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Task (project management); Motor learning; Transfer (computing); Interference (communication); Transfer of learning; Computer science; Group (periodic table); Psychology; Artificial intelligence; Channel (broadcasting); Telecommunications; Neuroscience; Chemistry; 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":[0.0001686871,0.0002561004,0.0003078466,0.0001110724,0.0002630185,0.0001500665,0.0003125158,0.0001062874,0.00008707008],"category_scores_gemma":[0.001704994,0.0002357928,0.0001533459,0.0001495323,0.0001282881,0.0003084626,0.0000425412,0.0003575739,0.0004508055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001536848,"about_ca_system_score_gemma":0.00007447595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001101458,"about_ca_topic_score_gemma":0.00005991926,"domain_scores_codex":[0.9981156,0.0001903416,0.0003439245,0.0005441543,0.0003194815,0.0004864752],"domain_scores_gemma":[0.9989389,0.0003968852,0.0001756121,0.0002401591,0.00007685586,0.000171618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"randomized_trial","study_design_scores_codex":[0.0009475655,0.0001989077,0.00006479044,0.000005928067,0.00001598245,0.0000415005,0.0004924834,0.001245031,0.9850886,0.001340199,0.000009984026,0.01054897],"study_design_scores_gemma":[0.3457755,0.02031044,0.02514018,0.0005576459,0.0005799349,0.0001391605,0.002640238,0.2461893,0.195441,0.00191555,0.1559756,0.00533541],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7681249,0.0002854693,0.2225214,0.001540757,0.00258176,0.001472026,0.00008512494,0.0006946021,0.002693966],"genre_scores_gemma":[0.9974889,0.00000749199,0.0001872666,0.0007774049,0.0002417522,0.00008708333,0.000004632613,0.00003282707,0.00117263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7896476,"threshold_uncertainty_score":0.9615353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04594317249280206,"score_gpt":0.2626275066151109,"score_spread":0.2166843341223089,"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."}}