{"id":"W3101430149","doi":"","title":"Optimal visual search based on a model of target detectability in natural images","year":2020,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Computer science; Visual search; Observer (physics); Computer vision; Bayesian probability; Pattern recognition (psychology); Eye tracking; Psychophysics; Target acquisition; Ground truth; Human visual system model; Visual perception; Machine learning; Perception; Image (mathematics)","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":[0.001158618,0.000414698,0.0007041962,0.0006017814,0.000217131,0.0009285406,0.001022047,0.0008713076,0.001171298],"category_scores_gemma":[0.00633911,0.0004379541,0.0006951565,0.0003434324,0.001140811,0.001614246,0.0006732636,0.0007599479,0.0002172256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001852275,"about_ca_system_score_gemma":0.0007472945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009968284,"about_ca_topic_score_gemma":0.006022662,"domain_scores_codex":[0.9995018,0.0001399046,0.00001903856,0.0001861234,0.00007577428,0.00007728369],"domain_scores_gemma":[0.9981022,0.001135352,0.000341593,0.0001227052,0.0002067053,0.00009143617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003218006,0.0001002813,0.004263623,0.0001116262,0.00008337024,0.0001607466,0.0003643602,0.9341924,0.01553119,0.02747653,0.000547436,0.01684659],"study_design_scores_gemma":[0.000006914991,0.00002559844,0.001201204,0.000004495982,0.000005415927,0.00002716494,0.000008068116,0.9928672,0.0003998712,0.005397878,0.00004905551,0.000007135816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3521502,0.0002730906,0.6441545,0.000422812,0.00001487867,0.00004396876,0.0001104384,0.0002554495,0.002574606],"genre_scores_gemma":[0.9809495,0.00008303,0.01730946,0.00005251174,0.000008459372,0.00004724445,0.00005683106,0.00003363586,0.001459384],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009968284,"threshold_uncertainty_score":0.01982051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02855786317148687,"score_gpt":0.2893435360575016,"score_spread":0.2607856728860147,"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."}}