{"id":"W3201618097","doi":"10.3389/fpsyg.2021.705668","title":"Spoken Word Segmentation in First and Second Language: When ERP and Behavioral Measures Diverge","year":2021,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Concordia University; Montreal Neurological Institute and Hospital; Employment and Social Development Canada; McGill University; Centre for Research on Brain Language and Music","funders":"USAF Phillips Laboratory; Fonds de Recherche du Québec-Société et Culture","keywords":"Psychology; Speech segmentation; Segmentation; Syllable; Text segmentation; Task (project management); Linguistics; Utterance; First language; Cognitive psychology; Natural language processing; Speech recognition; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007819784,0.0003095953,0.0003450762,0.0004314787,0.0001838859,0.0008797207,0.0001321071,0.0006131328,0.001648398],"category_scores_gemma":[0.004946983,0.0002045438,0.0001624504,0.000303567,0.0006198803,0.0007898964,0.0007444527,0.0005532883,0.0003410324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002080038,"about_ca_system_score_gemma":0.0001601457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00135647,"about_ca_topic_score_gemma":0.002517558,"domain_scores_codex":[0.9994887,0.0001074884,0.00004021036,0.0001762289,0.0001183019,0.00006903831],"domain_scores_gemma":[0.9985076,0.0007047108,0.0002829799,0.00009933671,0.0002308773,0.0001745144],"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":[0.003357511,0.0002620638,0.09305463,0.0004331044,0.0001326718,0.0009876165,0.008149966,0.0001469368,0.8528065,0.0004155077,0.0002391406,0.04001432],"study_design_scores_gemma":[0.0000298239,0.0007145514,0.9752461,0.0000258129,0.00004212962,0.0008770436,0.002687309,0.0003097393,0.0190027,0.0004864975,0.0005575325,0.00002069124],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963909,0.0002151636,0.0009600993,0.00003041827,0.00001314853,0.00002682189,0.0000743757,0.00001774669,0.002271279],"genre_scores_gemma":[0.9980635,0.0001177165,0.0009444919,0.0000890426,0.00001049887,0.00005562003,0.0001606401,0.00002139,0.0005370742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001648398,"threshold_uncertainty_score":0.005514443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03116702015243511,"score_gpt":0.3259923938111032,"score_spread":0.2948253736586681,"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."}}