{"id":"W2810352421","doi":"10.3758/s13415-018-0620-6","title":"Cognitive changes in conjunctive rule-based category learning: An ERP approach","year":2018,"lang":"en","type":"article","venue":"Cognitive Affective & Behavioral Neuroscience","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; Baycrest Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Categorization; Psychology; Cognition; Cognitive psychology; Working memory; Concept learning; Set (abstract data type); Event-related potential; Perception; Rule-based system; Executive functions; Artificial intelligence; Computer science","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":[0.0004276272,0.000330404,0.0002402799,0.0004124797,0.0001602043,0.0006550199,0.0005690286,0.0005575348,0.004292706],"category_scores_gemma":[0.003493154,0.0001995529,0.000215808,0.0005402639,0.0005614507,0.0008776932,0.0003627775,0.0009853847,0.0003741498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002663374,"about_ca_system_score_gemma":0.0002387331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001503471,"about_ca_topic_score_gemma":0.001047362,"domain_scores_codex":[0.9998074,0.00002540636,0.000006918606,0.00005787842,0.00007840133,0.0000240204],"domain_scores_gemma":[0.9990281,0.000582892,0.00009509509,0.0001038189,0.0001292366,0.0000609467],"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":[0.0009978599,0.000818916,0.01967328,0.0003222589,0.00008774683,0.0009098459,0.001193567,0.00218566,0.7761391,0.01088507,0.001374275,0.1854124],"study_design_scores_gemma":[0.0001399478,0.0007325264,0.8932506,0.0000586055,0.0001772508,0.001664837,0.001086892,0.01544821,0.05515204,0.02924743,0.002982731,0.00005896193],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9532301,0.000608299,0.02256138,0.0004156483,0.0001151817,0.0001788403,0.0006896672,0.0001475291,0.02205332],"genre_scores_gemma":[0.9899656,0.0002877307,0.006778518,0.0001656549,0.00005570547,0.00008246471,0.0001761764,0.00006510704,0.002423028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004292706,"threshold_uncertainty_score":0.01436049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07152959177013324,"score_gpt":0.3652622548472847,"score_spread":0.2937326630771515,"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."}}