{"id":"W4400748180","doi":"10.1101/2024.07.16.603693","title":"Hearing and cognitive decline in aging differentially impact neural tracking of context-supported versus random speech across linguistic timescales","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Max Planck Research School for Advanced Methods in Process and Systems Engineering; International Max Planck Research School for Environmental, Cellular and Molecular Microbiology; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Context (archaeology); Linguistic context; Cognition; Psychology; Cognitive aging; Cognitive psychology; Cognitive decline; Tracking (education); Speech perception; Linguistics; Speech recognition; Computer science; Audiology; Neuroscience; History; Linguistic analysis; Perception; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003573215,0.0002306479,0.0002142398,0.000344867,0.00009253462,0.0003549319,0.0001172777,0.0002496409,0.001255572],"category_scores_gemma":[0.001092014,0.0000767916,0.0001282835,0.000145852,0.0001913607,0.0002931746,0.0002589994,0.0002023684,0.0001161539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001057011,"about_ca_system_score_gemma":0.000111702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001500149,"about_ca_topic_score_gemma":0.002326318,"domain_scores_codex":[0.9999375,0.00001163674,0.000007779856,0.00001444428,0.00001549472,0.00001312148],"domain_scores_gemma":[0.999679,0.0000931414,0.0001082581,0.00002250092,0.00005756637,0.00003966687],"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":[0.004551237,0.0004317152,0.1836107,0.0003689227,0.0001984245,0.0007486894,0.0009128169,0.0005165992,0.7284667,0.0001343266,0.0004020654,0.07965778],"study_design_scores_gemma":[0.00001665126,0.0008089073,0.9788365,0.00002177409,0.00006329142,0.0003922424,0.0003149151,0.0005666423,0.01848481,0.0001970831,0.0002875862,0.000009473803],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99887,0.0003515086,0.0003697594,0.00002247632,0.000004382875,0.000006434183,0.00008815826,0.000009106351,0.000278159],"genre_scores_gemma":[0.9990632,0.0001377988,0.0004264421,0.0000282942,0.000005938632,0.00001139517,0.00006708121,0.000002692635,0.0002570799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001500149,"threshold_uncertainty_score":0.00420028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03430167879095226,"score_gpt":0.31431262014439,"score_spread":0.2800109413534378,"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."}}