{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003635189,0.0002998144,0.0002941377,0.0002759479,0.0001113449,0.00027971,0.0002057404,0.0004564582,0.0008717016],"category_scores_gemma":[0.002012462,0.0001365522,0.0001480249,0.0002558347,0.0003627434,0.0004234689,0.0003446184,0.000339876,0.0002392576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001015025,"about_ca_system_score_gemma":0.0001387067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003363079,"about_ca_topic_score_gemma":0.0005099485,"domain_scores_codex":[0.9997862,0.0000303714,0.00001590129,0.00006166297,0.00008143852,0.00002457077],"domain_scores_gemma":[0.9992336,0.0004343979,0.0001143784,0.00004980209,0.0001210517,0.00004687906],"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":[0.0005460694,0.00007474433,0.005920161,0.0001136011,0.00001438098,0.0005893339,0.0003621696,0.00009442867,0.9748557,0.0002001846,0.0000424456,0.01718668],"study_design_scores_gemma":[0.0000824781,0.00120777,0.6954308,0.00003482461,0.0001144376,0.005483375,0.000753706,0.004770834,0.2891739,0.001296488,0.001609708,0.00004156441],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928808,0.0002237051,0.005563955,0.00002367499,0.00001227955,0.00003896647,0.00009354429,0.00003627986,0.001126619],"genre_scores_gemma":[0.9943183,0.00020658,0.004612949,0.00003501413,0.00001702568,0.00003929453,0.0001430049,0.00003218997,0.000595646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008717016,"threshold_uncertainty_score":0.002916098,"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."}}