{"id":"W2622927283","doi":"10.1121/1.4987548","title":"Using electroencephalography as a tool to understand auditory perception: Event-related and time-frequency analyses","year":2017,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Electroencephalography; Computer science; EEG-fMRI; Auditory cortex; Perception; Magnetoencephalography; Neuroscience; Speech recognition; Pattern recognition (psychology); Artificial intelligence; Psychology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008714213,0.0007077981,0.0003497754,0.002316263,0.0002647604,0.001765806,0.0004869553,0.001265192,0.001255083],"category_scores_gemma":[0.00183451,0.0002533684,0.0004128774,0.001551741,0.001826225,0.00284168,0.00071299,0.001396156,0.0006021151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000319594,"about_ca_system_score_gemma":0.0002689584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007808335,"about_ca_topic_score_gemma":0.0006765082,"domain_scores_codex":[0.9997678,0.00006833589,0.00002170562,0.00004767621,0.00008069877,0.00001372289],"domain_scores_gemma":[0.9995281,0.0003210147,0.00003570969,0.00003940876,0.00005741338,0.00001834808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001510243,0.0001561091,0.01155575,0.001122366,0.0001990422,0.001160531,0.001115805,0.006786157,0.08786765,0.1343205,0.008825717,0.7467394],"study_design_scores_gemma":[0.00006845476,0.000413493,0.06358195,0.0008602961,0.0002011393,0.006220763,0.00161616,0.0479749,0.04018006,0.6414824,0.1971209,0.000279693],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0332915,0.05440277,0.880062,0.006148127,0.0007432178,0.0001777072,0.0005555329,0.001044611,0.02357456],"genre_scores_gemma":[0.2617892,0.06991115,0.6535582,0.002841556,0.002151238,0.0002877861,0.0003921685,0.0002260275,0.008842602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002316263,"threshold_uncertainty_score":0.004608572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03904073851666514,"score_gpt":0.3209501153205517,"score_spread":0.2819093768038866,"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."}}