{"id":"W2042445563","doi":"10.1080/713755917","title":"On the Cognitive Basis of Observational Learning: Development of Mechanisms for the Detection and Correction of Errors","year":2000,"lang":"en","type":"article","venue":"The Quarterly Journal of Experimental Psychology Section A","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":110,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Observer (physics); Systematic error; Cognition; Task (project management); Cognitive psychology; Psychology; Observational study; Motor skill; Error detection and correction; Computer science; Motor learning; Observational learning; Artificial intelligence; Machine learning; Developmental psychology; Statistics; Algorithm; Mathematics; Mathematics education; Neuroscience; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.004336189,0.0003838604,0.0003793164,0.0006242269,0.0002632398,0.001798305,0.001356399,0.001434024,0.001302289],"category_scores_gemma":[0.0330685,0.0004019266,0.0004990732,0.0002989016,0.003414301,0.002565691,0.001252262,0.001411239,0.0002287273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005396992,"about_ca_system_score_gemma":0.001080631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001744137,"about_ca_topic_score_gemma":0.0009971486,"domain_scores_codex":[0.9983829,0.0004230018,0.0001063568,0.0004636818,0.000480522,0.000143491],"domain_scores_gemma":[0.9834082,0.00856504,0.003147003,0.003053674,0.001394561,0.000431665],"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":[0.001729621,0.001318582,0.1017786,0.001134912,0.0005755415,0.001071835,0.009063347,0.02332943,0.2991289,0.1410298,0.001584078,0.4182551],"study_design_scores_gemma":[0.0004060784,0.004002464,0.2688628,0.0005613022,0.0003704255,0.002644264,0.001952362,0.1647056,0.2186648,0.3275948,0.009719419,0.0005156962],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6684385,0.001303302,0.3174113,0.00209252,0.0001353996,0.0001466957,0.00007501448,0.0004473584,0.009950047],"genre_scores_gemma":[0.9677106,0.0005197984,0.03060104,0.0001599141,0.00002861313,0.000047325,0.00005949203,0.00002295561,0.000850359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004336189,"threshold_uncertainty_score":0.02293223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06874878164890552,"score_gpt":0.3282578693161833,"score_spread":0.2595090876672778,"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."}}