{"id":"W4255611489","doi":"10.31234/osf.io/u8kvj","title":"Hierarchical drift diffusion modeling uncovers multisensory benefit in numerosity discrimination tasks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neuroscience and Music Perception","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Numerosity adaptation effect; Computer science; Bayesian probability; Diffusion; Sample (material); Estimation theory; Hierarchical database model; Sample size determination; Estimation; Artificial intelligence; Algorithm; Biological system; Statistics; Mathematics; Data mining; Chemistry; Engineering; Physics","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":[0.00313845,0.0004209254,0.0006397406,0.0005412294,0.0002678912,0.0008449914,0.0006854999,0.0006402663,0.001720605],"category_scores_gemma":[0.01462158,0.0002890112,0.0007511571,0.000503904,0.0006147973,0.00128796,0.001130955,0.001156126,0.0002359607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000767939,"about_ca_system_score_gemma":0.0006040556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004856733,"about_ca_topic_score_gemma":0.004440942,"domain_scores_codex":[0.9995257,0.0001847245,0.00002896569,0.0001426851,0.00006675735,0.00005124109],"domain_scores_gemma":[0.9953648,0.003179485,0.0004922433,0.0006142471,0.0001926913,0.0001565676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002300423,0.0005695898,0.06466812,0.0006257027,0.0005019422,0.0004514569,0.00184684,0.5382565,0.1573834,0.101955,0.004493798,0.1269471],"study_design_scores_gemma":[0.0000482197,0.00006949228,0.01710803,0.000015875,0.00004187211,0.00007508943,0.00004617271,0.9332323,0.005140573,0.04364006,0.0005397892,0.00004249201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8706686,0.0002252496,0.1262999,0.0004329168,0.00002866801,0.0000388402,0.0003642535,0.0002742062,0.00166735],"genre_scores_gemma":[0.9778433,0.00008873305,0.02085063,0.00006294864,0.0000111325,0.00003104153,0.0003196753,0.00007117482,0.0007212794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004856733,"threshold_uncertainty_score":0.01659793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0667368876769727,"score_gpt":0.3025045140270455,"score_spread":0.2357676263500728,"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."}}