{"id":"W1965135781","doi":"10.1016/j.brainres.2008.02.054","title":"The role of category learning in the acquisition and retention of perceptual expertise: A behavioral and neurophysiological study","year":2008,"lang":"en","type":"article","venue":"Brain Research","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":116,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Institute of Mental Health; James S. McDonnell Foundation","keywords":"Categorization; Psychology; Perception; Cognitive psychology; Event-related potential; Perceptual learning; Neurophysiology; Neural correlates of consciousness; Electroencephalography; Developmental psychology; Neuroscience; Cognition; Computer science; Artificial intelligence","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":[0.000969393,0.00004753135,0.00007502583,0.00008035154,0.0003460264,0.00002192402,0.0001070905,0.0000354656,0.00003545743],"category_scores_gemma":[0.0004378519,0.00002803167,0.00001811532,0.0002237432,0.0006041622,0.00007128093,0.00007551483,0.000291221,0.000003871412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008618678,"about_ca_system_score_gemma":0.00001545413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001535699,"about_ca_topic_score_gemma":0.00002180514,"domain_scores_codex":[0.9971187,0.002028559,0.000141393,0.0001846009,0.0003781111,0.0001486881],"domain_scores_gemma":[0.9991648,0.0006337438,0.00002981134,0.000098062,0.00004992012,0.00002367841],"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":[0.0001213292,0.0002155708,0.009185714,0.000004580204,4.286498e-7,0.000007636614,0.01164466,0.000002836717,0.9617396,0.00007396108,0.00003076518,0.01697293],"study_design_scores_gemma":[0.0004256099,0.00158788,0.9570388,0.000009185076,0.000001572346,0.0000336173,0.03341847,0.000840786,0.006255697,0.0002553636,0.00008651677,0.00004655674],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991115,0.0000607558,0.00000136555,0.0002673269,0.000008803238,0.0003386845,0.00000146259,0.000006632264,0.0002034555],"genre_scores_gemma":[0.9995687,0.0002625479,0.000002833801,0.00002828286,0.0000125293,0.00003281575,0.000001396904,0.000003900384,0.00008699727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9554839,"threshold_uncertainty_score":0.266139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2166619408341922,"score_gpt":0.4076244852812677,"score_spread":0.1909625444470755,"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."}}