{"id":"W2072978705","doi":"10.1016/j.brainres.2007.04.024","title":"Hierarchical error processing: Different errors, different systems","year":2007,"lang":"en","type":"article","venue":"Brain Research","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":104,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Michael Smith Health Research BC","keywords":"Joystick; Error-related negativity; Posterior parietal cortex; Computer science; Motor system; Psychology; Premotor cortex; Motor control; Anterior cingulate cortex; N100; Event-related potential; Neuroscience; Electroencephalography; Cognition; Simulation; Medicine","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":[0.001178391,0.0002501846,0.0003201457,0.0003415212,0.0007539838,0.0001379155,0.0006580892,0.0001440973,0.00006140932],"category_scores_gemma":[0.0007663796,0.0001596135,0.00008996321,0.0006016103,0.0009910729,0.00009833885,0.0004064275,0.001138236,0.0001560274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009590369,"about_ca_system_score_gemma":0.00002826877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003087898,"about_ca_topic_score_gemma":0.00005748245,"domain_scores_codex":[0.9955641,0.0006254205,0.0003850157,0.0008480043,0.001299531,0.001277915],"domain_scores_gemma":[0.9979535,0.00115848,0.00005855485,0.0003943759,0.0001092649,0.0003258256],"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":[0.0003175944,0.0009702919,0.01317362,0.0001111653,0.00000579943,0.0004517352,0.0004917728,9.638895e-7,0.939782,0.001925083,0.02274095,0.02002903],"study_design_scores_gemma":[0.002849085,0.002278844,0.4469718,0.0003443878,0.00002294058,0.0003282759,0.001931387,0.0005429924,0.4796249,0.003616177,0.06022885,0.001260414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856845,0.0002046793,0.00008121438,0.009452461,0.0005280841,0.0006390762,0.0000135113,0.0001423434,0.003254121],"genre_scores_gemma":[0.9856187,0.00001885335,0.000009065769,0.0004819589,0.0002772826,0.00007988358,0.000002663309,0.00002969398,0.01348192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4601571,"threshold_uncertainty_score":0.650885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4038031685612457,"score_gpt":0.501703958225746,"score_spread":0.09790078966450033,"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."}}