{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003606082,0.0001188544,0.0001747608,0.0001939305,0.00008644116,0.0002564286,0.0002965734,0.00005502534,0.000001787093],"category_scores_gemma":[0.00008376754,0.0001033103,0.00005102115,0.0006473733,0.00003213668,0.002641784,0.00005225802,0.0002169693,0.00001177763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005308134,"about_ca_system_score_gemma":0.0001047852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001821601,"about_ca_topic_score_gemma":3.503449e-7,"domain_scores_codex":[0.9984931,0.00009522631,0.000548768,0.0001771304,0.0004986674,0.0001871623],"domain_scores_gemma":[0.9993669,0.0000261217,0.0001886616,0.0001225618,0.0002246199,0.00007110667],"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":[0.0000531504,0.00003572595,0.0003450699,0.0004408643,0.000001185224,4.66535e-7,0.001595725,0.9826558,0.005152229,0.0001052018,0.0000183315,0.009596269],"study_design_scores_gemma":[0.0003711788,0.0001816744,0.0005313299,0.00004656244,8.8359e-7,0.000002012929,0.0001908097,0.9867138,0.01184239,0.00000561151,0.00001080566,0.000102958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3588964,0.00001876617,0.6398847,0.0004652148,0.0001556075,0.0002480354,0.000003744284,0.000149656,0.0001778806],"genre_scores_gemma":[0.9966479,2.514489e-7,0.002919691,0.0003779848,0.0000187667,0.00001851502,0.000006187745,0.000003935011,0.000006765521],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6377515,"threshold_uncertainty_score":0.4212872,"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."}}