{"id":"W4403561142","doi":"10.1016/j.jneumeth.2024.110299","title":"Pupillometry is sensitive to speech masking during story listening: A commentary on the critical role of modeling temporal trends","year":2024,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Baycrest Hospital","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Pupillometry; Active listening; Masking (illustration); Psychology; Backward masking; Cognitive psychology; Speech recognition; Computer science; Natural language processing; Communication; Art; Neuroscience; Pupil; Literature; Perception","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01246214,0.0009102044,0.001746097,0.001559733,0.001800019,0.002895225,0.005063097,0.03115841,0.001553894],"category_scores_gemma":[0.05974486,0.0007042821,0.001941387,0.000816111,0.009109978,0.004725827,0.001963516,0.03826158,0.001687665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003209583,"about_ca_system_score_gemma":0.002521277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006069681,"about_ca_topic_score_gemma":0.006542324,"domain_scores_codex":[0.9929848,0.001936507,0.001469699,0.001631769,0.001759966,0.0002172618],"domain_scores_gemma":[0.8972041,0.09208732,0.001679754,0.001544478,0.006827046,0.0006573224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005106694,0.00007671354,0.002167721,0.002546736,0.0003815549,0.006033509,0.00327292,0.0009904495,0.003475735,0.0429584,0.8157265,0.1218592],"study_design_scores_gemma":[0.0002599855,0.0002862179,0.004443435,0.005368707,0.0003449428,0.008185906,0.001347995,0.001780969,0.00372716,0.0666751,0.9072288,0.0003507193],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.00143421,0.05537595,0.003440144,0.8717088,0.06516702,0.00002073997,0.0001530502,0.000102111,0.002597965],"genre_scores_gemma":[0.03782733,0.02817546,0.003458272,0.7082664,0.2188026,0.0001078378,0.00005941185,0.0001813386,0.003121314],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.03115841,"threshold_uncertainty_score":0.06590688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08670841836863975,"score_gpt":0.4223855219028231,"score_spread":0.3356771035341833,"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."}}