{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001316093,0.0007645899,0.001394335,0.001540326,0.0005530695,0.005117964,0.001650814,0.00305994,0.006687661],"category_scores_gemma":[0.00301834,0.0009612244,0.001275355,0.0009912244,0.002576041,0.007015197,0.00160222,0.001690621,0.0008198434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00127214,"about_ca_system_score_gemma":0.0006681312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001796758,"about_ca_topic_score_gemma":0.001288571,"domain_scores_codex":[0.9993926,0.0001321808,0.00004017008,0.0001625745,0.0001715964,0.0001009161],"domain_scores_gemma":[0.997866,0.00122897,0.0002124908,0.000237433,0.0002918635,0.0001633187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.001353021,0.0004179183,0.004035937,0.0009720525,0.0004810453,0.0004467628,0.0008601951,0.02091038,0.1072419,0.7580081,0.003468203,0.1018046],"study_design_scores_gemma":[0.0002367847,0.0002826013,0.01108199,0.00007655792,0.0001779803,0.0004986557,0.0002439979,0.1221934,0.009975186,0.8519514,0.003144013,0.0001374646],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2456913,0.02995447,0.6306212,0.01683884,0.001293039,0.0001580228,0.0004827369,0.0006104806,0.07434987],"genre_scores_gemma":[0.933293,0.003612351,0.05309723,0.000719787,0.0008706579,0.00009295674,0.0001081856,0.0001126469,0.008093053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006687661,"threshold_uncertainty_score":0.02237248,"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."}}