{"id":"W4412501729","doi":"10.1101/2025.07.15.664921","title":"Neural signatures of engagement and event segmentation during story listening in background noise","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cognitive Science and Education Research","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto; Western University","funders":"Canadian Institutes of Health Research; Canada First Research Excellence Fund; Canada Research Chairs","keywords":"Active listening; Noise (video); Event (particle physics); Segmentation; Psychology; Speech recognition; Computer science; Communication; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001050594,0.0002665567,0.0002920536,0.0006694132,0.000227717,0.0001536021,0.0004003638,0.0001550257,0.0000338135],"category_scores_gemma":[0.0007399184,0.000291634,0.00006596834,0.0007131058,0.00020374,0.0002464536,0.0006722143,0.0009184462,0.000004605451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002722259,"about_ca_system_score_gemma":0.0004845659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005341528,"about_ca_topic_score_gemma":0.000004211517,"domain_scores_codex":[0.9972679,0.0005252766,0.000394855,0.0008850571,0.0005233918,0.0004034904],"domain_scores_gemma":[0.9986653,0.0003105766,0.0002475742,0.0004281864,0.0002150229,0.0001333479],"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":[0.0000330717,0.0001149839,0.007521217,0.0005024358,0.000008186144,0.0000176454,0.0001289023,0.0004654012,0.9910597,0.0001111486,0.00002131782,0.00001603414],"study_design_scores_gemma":[0.0003612199,0.00002688359,0.2878104,0.0003912218,0.0000157602,7.440414e-9,0.00008592598,0.0009390822,0.7100102,0.000001550622,0.0001089592,0.0002488028],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977123,0.0004463798,0.00009675797,0.0002416918,0.00065282,0.0007093118,0.00006730086,0.00005141385,0.00002200869],"genre_scores_gemma":[0.9989703,0.0002551025,0.0003407736,0.0001463743,0.00007779735,0.0001589689,1.286146e-7,0.00001925226,0.00003130703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2810495,"threshold_uncertainty_score":0.9999536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04452331941344376,"score_gpt":0.3135510571612595,"score_spread":0.2690277377478157,"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."}}