{"id":"W2141811341","doi":"10.3758/bf03194806","title":"Target detection and localization in visual search: A dual systems perspective","year":2003,"lang":"en","type":"article","venue":"Perception & Psychophysics","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Science Foundation","keywords":"Computer vision; Dorsum; Perspective (graphical); Dual (grammatical number); Artificial intelligence; Orientation (vector space); Computer science; Visual search; Visual space; Communication; Psychology; Neuroscience; Perception; Mathematics; Biology; Anatomy; Geometry","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":[0.0002691096,0.0001769443,0.0001618289,0.0001915127,0.0002457002,0.0001625345,0.00005544441,0.0001117033,0.0001443148],"category_scores_gemma":[0.0001102359,0.0001834165,0.00003929854,0.0006519317,0.0001011931,0.0004085011,0.00001417345,0.000236869,0.0001827857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001713328,"about_ca_system_score_gemma":0.00003439638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006384811,"about_ca_topic_score_gemma":0.000010279,"domain_scores_codex":[0.9982255,0.0003990701,0.0002241074,0.0005399609,0.0003472074,0.0002641896],"domain_scores_gemma":[0.999574,0.00003883402,0.00006364063,0.0001255216,0.00009676308,0.0001012529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008430473,0.0002812201,0.0002087539,0.00004500044,0.000002328458,0.000003663655,0.007383807,0.001950816,0.9756595,0.009879249,0.00003911654,0.004462251],"study_design_scores_gemma":[0.006623279,0.002083084,0.0135842,0.0003253478,0.00004979955,0.0003113981,0.06994032,0.7226121,0.1160129,0.06094334,0.005149813,0.002364397],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8723961,0.00002560618,0.1231915,0.00007975324,0.0006102088,0.0003658439,0.000005320348,0.0001379962,0.003187702],"genre_scores_gemma":[0.9988616,0.0000926779,0.000164719,0.0004553355,0.0001338702,0.00003254599,0.000003178291,0.00002919953,0.0002269359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8596466,"threshold_uncertainty_score":0.7479508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04929200184699417,"score_gpt":0.3407868400901556,"score_spread":0.2914948382431614,"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."}}