{"id":"W1992214258","doi":"10.3758/bf03193948","title":"What influences visual search efficiency? Disentangling contributions of preattentive and postattentive processes","year":2007,"lang":"en","type":"article","venue":"Perception & Psychophysics","topic":"Neural and Behavioral Psychology Studies","field":"Neuroscience","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Trent University; York University; University of Waterloo","funders":"University of Waterloo","keywords":"Visual search; Matching (statistics); Psychology; Artificial intelligence; Computer science; Cognitive psychology; Pattern recognition (psychology); Mathematics; Statistics","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":[0.0005107194,0.0003214611,0.0006129041,0.0002764477,0.0002063143,0.001608907,0.0003791667,0.0006527397,0.002514459],"category_scores_gemma":[0.005311555,0.0005795849,0.0003620566,0.0002440205,0.0004953078,0.001797373,0.0003845281,0.0006277571,0.0003337175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003334663,"about_ca_system_score_gemma":0.0004494851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001420952,"about_ca_topic_score_gemma":0.002122153,"domain_scores_codex":[0.9997131,0.00004041471,0.00002123394,0.00009062239,0.0000648186,0.00006974104],"domain_scores_gemma":[0.9971536,0.001902869,0.0004199622,0.000175894,0.0001310623,0.0002164507],"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":[0.003146521,0.0004893873,0.05192035,0.0005285455,0.0003821448,0.0002544385,0.0007447278,0.002735883,0.8815112,0.003040008,0.000988862,0.0542579],"study_design_scores_gemma":[0.0002695622,0.0007075808,0.8673717,0.00008277669,0.0004157573,0.0004559605,0.0005174622,0.02388155,0.09580068,0.008637425,0.001763505,0.00009605928],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922422,0.0005321551,0.00322725,0.0002329694,0.00003517748,0.00001892929,0.00008204868,0.00005210777,0.003577311],"genre_scores_gemma":[0.9968035,0.0003270846,0.001565561,0.0001197482,0.00002551883,0.00001648978,0.0000916752,0.0001429086,0.0009074838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002514459,"threshold_uncertainty_score":0.008411705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0713681735982804,"score_gpt":0.4257558953011163,"score_spread":0.3543877217028359,"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."}}