{"id":"W2063170107","doi":"10.3389/fnhum.2013.00763","title":"Detecting self-produced speech errors before and after articulation: an ERP investigation","year":2013,"lang":"en","type":"article","venue":"Frontiers in Human Neuroscience","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Employment and Social Development Canada; Queen's University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Speech production; Speech recognition; Error-related negativity; Speech error; Computer science; Articulation (sociology); Stimulus (psychology); Task (project management); Event-related potential; Electroencephalography; Psychology; Cognitive psychology; Cognition; Neuroscience","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":[0.0002706239,0.0001811947,0.0001685615,0.0001895488,0.0003735108,0.0001688301,0.0003218968,0.00007794123,0.00002595984],"category_scores_gemma":[0.000427055,0.0001705364,0.00002748101,0.0004127493,0.0005519283,0.0009407945,0.0001110679,0.0002644181,0.000004919668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002646924,"about_ca_system_score_gemma":0.00002418331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005282298,"about_ca_topic_score_gemma":0.00008351293,"domain_scores_codex":[0.9979656,0.000206305,0.0002672581,0.0009203239,0.0002289105,0.0004116244],"domain_scores_gemma":[0.9993101,0.00002899046,0.0001117388,0.0003679609,0.00002997226,0.0001512008],"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":[0.000005609969,0.00004104917,0.1216968,0.00001349307,3.581337e-7,0.0001913664,0.003680443,0.00005557201,0.8711416,0.0000172804,0.00007854627,0.003077842],"study_design_scores_gemma":[0.0007780563,0.0006901038,0.4472668,0.00005286003,0.00001731797,0.0006381169,0.0007170604,0.03431434,0.5014663,0.01311804,0.0001835076,0.0007574376],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980625,0.00001795018,0.0001672028,0.0003156438,0.0007990726,0.0004339808,0.000001560816,0.0001557151,0.0000463804],"genre_scores_gemma":[0.9950094,0.000003964708,0.002569453,0.002188243,0.00008703668,0.00003141694,0.00000125746,0.00001731437,0.00009195979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3696753,"threshold_uncertainty_score":0.6954274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02073753046510628,"score_gpt":0.2619859571834,"score_spread":0.2412484267182937,"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."}}