{"id":"W2753113298","doi":"10.1167/17.10.56","title":"Limits to Attentional Selection of Features","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Interference (communication); Perception; Population; Orientation (vector space); Correlation; Selection (genetic algorithm); Artificial intelligence; Psychology; Mathematics; Computer vision; Computer science; Cognitive psychology; Communication; Telecommunications; Geometry; Medicine","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":[0.002518235,0.000420471,0.0007882381,0.000750515,0.0006427912,0.002923978,0.001723776,0.001302407,0.00435369],"category_scores_gemma":[0.02483023,0.0006032006,0.0005634677,0.0004666976,0.001487019,0.004203775,0.004190141,0.001374061,0.001033357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009716654,"about_ca_system_score_gemma":0.0007195892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002148775,"about_ca_topic_score_gemma":0.001241823,"domain_scores_codex":[0.9962365,0.0004691393,0.0002288126,0.001183986,0.00148557,0.0003959617],"domain_scores_gemma":[0.9836853,0.009530876,0.001231702,0.003284722,0.001260158,0.001007263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001470482,0.0003581411,0.03419651,0.001118806,0.0004458457,0.0007010371,0.003824496,0.005833384,0.483682,0.094667,0.005966925,0.3677354],"study_design_scores_gemma":[0.0005112392,0.001521816,0.3941837,0.0006638997,0.0004588579,0.00503163,0.002461336,0.08682837,0.0924534,0.3767696,0.03871072,0.0004053684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8257346,0.005633913,0.08202475,0.002250753,0.0001868414,0.0001221996,0.0001845907,0.0007311463,0.08313117],"genre_scores_gemma":[0.9878907,0.0007314512,0.008417253,0.0006929984,0.0001288718,0.0001520925,0.0001225416,0.00015526,0.001708854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00435369,"threshold_uncertainty_score":0.01456457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02058315071286738,"score_gpt":0.3263769404684207,"score_spread":0.3057937897555533,"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."}}