{"id":"W4414451803","doi":"10.1101/2025.09.23.674728","title":"Exploring the impact of social relevance on the cortical tracking of speech: viability and temporal response characterisation","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"Research Ireland; Trinity College Dublin; University College Dublin","keywords":"Neurocomputational speech processing; Speech perception; Speech processing; Perception; Artificial neural network; Active listening; Relevance (law); Voice activity detection","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":[0.0005903161,0.0002822883,0.0001915016,0.0003481962,0.0001917788,0.0007306245,0.0001810281,0.0003224796,0.002367075],"category_scores_gemma":[0.006146164,0.000143267,0.0002177864,0.0001792435,0.0006819762,0.0005294908,0.0007744782,0.0004015999,0.0002216806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00015252,"about_ca_system_score_gemma":0.000148001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006028872,"about_ca_topic_score_gemma":0.0007283249,"domain_scores_codex":[0.9997411,0.00009500971,0.00001331256,0.00005884701,0.00005537805,0.0000362324],"domain_scores_gemma":[0.9983692,0.001108114,0.000156341,0.0001420821,0.0001476972,0.00007658893],"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":[0.0008365288,0.0000599231,0.01030514,0.00025117,0.00005896584,0.0003026539,0.001006937,0.00203195,0.9400044,0.001229798,0.0002271835,0.04368546],"study_design_scores_gemma":[0.00006105503,0.001144073,0.7309716,0.0001471513,0.0002370492,0.001601034,0.002176604,0.03906097,0.2120438,0.009635308,0.002845422,0.00007587177],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794605,0.0002666916,0.01567865,0.0001215376,0.00002843976,0.0000335381,0.0001445005,0.00008496821,0.004181091],"genre_scores_gemma":[0.9980513,0.00005817717,0.001518315,0.0000211046,0.00001414873,0.0000189093,0.00004696129,0.00002018172,0.0002509088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002367075,"threshold_uncertainty_score":0.007918656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0572562850619966,"score_gpt":0.2758160198359457,"score_spread":0.2185597347739491,"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."}}