{"id":"W2906978576","doi":"10.1016/j.bandl.2018.12.005","title":"The effects of distractor set-size on neural tracking of attended speech","year":2019,"lang":"en","type":"article","venue":"Brain and Language","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psychology; Distraction; Speech recognition; Perception; Speech perception; Neurocomputational speech processing; Electroencephalography; Stimulus (psychology); Entrainment (biomusicology); Cognitive psychology; Set (abstract data type); Active listening; Communication; Computer science; Neuroscience; Acoustics; Rhythm","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":[0.0001127088,0.00008326214,0.0001273804,0.00001881518,0.00003671317,0.00002855234,0.0001753706,0.00002946699,0.00001882356],"category_scores_gemma":[0.0005484826,0.0000504869,0.00004579279,0.00006319321,0.0000797207,0.00004858083,0.00004077891,0.0000992612,0.000005016989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003280836,"about_ca_system_score_gemma":0.000005299609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001670474,"about_ca_topic_score_gemma":0.000006790814,"domain_scores_codex":[0.999337,0.000090136,0.0001264971,0.0001682727,0.0001434034,0.0001347065],"domain_scores_gemma":[0.9966872,0.003006581,0.00008295714,0.0001878898,0.000007611976,0.00002771661],"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":[0.00003519023,0.00003058704,0.0004464452,0.0001162346,0.000003645393,0.00002014408,0.002125705,9.743931e-7,0.9789621,0.000610539,0.0003137481,0.01733471],"study_design_scores_gemma":[0.0004003096,0.0002764662,0.01758932,0.00007815859,0.000003326398,0.00001570726,0.0004093918,0.0001964814,0.9804428,0.00005654128,0.0004569294,0.00007459085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976519,0.0001114495,0.000003772,0.0004022191,0.0002693372,0.0001674268,0.00001060362,0.00001816165,0.001365071],"genre_scores_gemma":[0.9983329,0.000003995988,0.00001282143,0.000578471,0.00003442753,0.000001201312,5.103291e-7,0.000007387527,0.001028318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01726012,"threshold_uncertainty_score":0.2058796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01143622261818593,"score_gpt":0.2700268798815532,"score_spread":0.2585906572633672,"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."}}