{"id":"W4246454874","doi":"10.31124/advance.12464144","title":"Understanding Attention as a Brain Process Using Biased Competition Model and Visual Psychophysics","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Psychophysics; Competition (biology); Process (computing); Cognitive psychology; Psychology; Cognitive science; Computer science; Neuroscience; Perception","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002413094,0.000213984,0.0002133984,0.000165647,0.0002286603,0.0005995095,0.0003568629,0.0001021646,0.000004150002],"category_scores_gemma":[0.00004141191,0.0002208803,0.00007546716,0.000414749,0.00008319061,0.0006436256,0.0007224506,0.0002928003,0.00001889642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001169767,"about_ca_system_score_gemma":0.0002763508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001998963,"about_ca_topic_score_gemma":0.000007793178,"domain_scores_codex":[0.9982686,0.00005090384,0.0002135934,0.0008328045,0.0003963677,0.0002377503],"domain_scores_gemma":[0.9993787,0.00005170347,0.0001601451,0.0001669282,0.0001190656,0.0001234656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000089817,0.0004891783,0.0007184219,0.001948298,0.00019451,0.00007413512,0.01080969,0.05151987,0.1890955,0.7154333,0.0005911222,0.02903613],"study_design_scores_gemma":[0.0001754253,0.00002921499,0.00004084944,0.000226099,0.000009711551,0.000004788792,0.0003530136,0.7551384,0.0001871005,0.2436158,0.00000232169,0.0002172484],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1262274,0.000008684661,0.868297,0.002636703,0.0001959247,0.0002862176,0.00000444007,0.0001720211,0.002171617],"genre_scores_gemma":[0.9853356,0.00000959663,0.01327154,0.001249931,0.0000750456,0.00001032372,0.0000122618,0.00001013368,0.00002552608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8591082,"threshold_uncertainty_score":0.9007238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2458835899935405,"score_gpt":0.3766681991923062,"score_spread":0.1307846091987656,"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."}}