{"id":"W4233776532","doi":"10.31234/osf.io/873y9","title":"Cognitive Changes in Conjunctive Rule-Based Category Learning: An ERP Approach","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Baycrest Hospital","funders":"","keywords":"Categorization; Cognition; Psychology; Cognitive psychology; Working memory; Set (abstract data type); Event-related potential; Concept learning; Rule-based system; Perception; Computer science; Natural language processing; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001280195,0.0005377115,0.0006878047,0.0004585955,0.0003551912,0.0008785918,0.001816507,0.000461828,0.00002981113],"category_scores_gemma":[0.0002487063,0.0004955681,0.0001465073,0.0001180815,0.0001220831,0.0003503644,0.001162351,0.002004586,0.00008699027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001966604,"about_ca_system_score_gemma":0.0004473087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002489516,"about_ca_topic_score_gemma":0.0001644373,"domain_scores_codex":[0.9963882,0.0006392334,0.000364208,0.001500761,0.00050204,0.0006055815],"domain_scores_gemma":[0.9976341,0.0002087642,0.0006015373,0.0009827514,0.0004027417,0.0001701232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003693785,0.002335962,0.05805156,0.002010443,0.0007326168,0.000811158,0.04915221,0.1534635,0.0006954477,0.5612189,0.000290755,0.1708681],"study_design_scores_gemma":[0.002783858,0.001325359,0.02876098,0.003075359,0.00009256904,0.00003438776,0.007492038,0.9180052,0.00781735,0.006822146,0.01935648,0.004434278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01965876,0.0002442516,0.9028329,0.0002559255,0.00138971,0.0009605016,0.000006547853,0.0005142702,0.07413711],"genre_scores_gemma":[0.9755207,0.00001304622,0.006318543,0.0001566422,0.0003831549,0.0002432538,0.00008089506,0.00004542077,0.01723833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9558619,"threshold_uncertainty_score":0.9997496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06875867273594038,"score_gpt":0.3001363274018202,"score_spread":0.2313776546658798,"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."}}