{"id":"W4239063395","doi":"10.31234/osf.io/qm2se","title":"Auditory perceptual learning depends on temporal regularity and certainty","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University; Baycrest Hospital","funders":"","keywords":"Snippet; Perception; Noise (video); Perceptual learning; Certainty; Computer science; Speech recognition; Psychology; Artificial intelligence; Mathematics; Information retrieval; Neuroscience","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":[0.00120635,0.0003513031,0.0005518447,0.0003249807,0.0002305678,0.001479766,0.0006090797,0.0005163899,0.001861413],"category_scores_gemma":[0.01523851,0.0004968641,0.0003199345,0.0001759059,0.000947825,0.001572074,0.001586358,0.001332597,0.0002598428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003591868,"about_ca_system_score_gemma":0.000417543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006325666,"about_ca_topic_score_gemma":0.0005544902,"domain_scores_codex":[0.9984516,0.0001811865,0.0001563402,0.0003784576,0.0007173438,0.0001150023],"domain_scores_gemma":[0.9921095,0.003274187,0.002215021,0.0011917,0.0005551798,0.0006544431],"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":[0.001737753,0.0003064628,0.01887699,0.000222517,0.000123832,0.0002083166,0.0004144257,0.0038931,0.9111283,0.00218529,0.0001974026,0.06070564],"study_design_scores_gemma":[0.0002480263,0.003338898,0.5977037,0.0001106613,0.000243689,0.001542898,0.0005148943,0.05061294,0.3184888,0.0237124,0.003258246,0.0002248624],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975683,0.0003539202,0.01855189,0.0001196497,0.00003804699,0.0000309963,0.00005550616,0.0001024356,0.005064567],"genre_scores_gemma":[0.9939269,0.0001402698,0.005263785,0.00004535468,0.00001898817,0.00001471667,0.0000700972,0.00005645429,0.000463552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001861413,"threshold_uncertainty_score":0.006379902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0554230907598006,"score_gpt":0.2931456220446463,"score_spread":0.2377225312848457,"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."}}