{"id":"W2567962159","doi":"10.1167/16.12.337","title":"How you use it matters: Object Function Guides Attention during Visual Search in Scenes","year":2016,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Object (grammar); Function (biology); Perception; Action (physics); Visual search; Set (abstract data type); Psychology; Visual Objects; Cognition; Context (archaeology); Cognitive psychology; Affect (linguistics); Visual perception; Feature (linguistics); Cognitive science; Computer science; Communication; Artificial intelligence; Neuroscience; Geography","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":[0.0005083353,0.0001849469,0.0002328504,0.0002930494,0.0002365577,0.001041317,0.0002639528,0.0004590673,0.001437154],"category_scores_gemma":[0.006282473,0.0002461188,0.0001598839,0.0001824492,0.0004309681,0.00137059,0.0003674738,0.0003016217,0.0002117085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002823736,"about_ca_system_score_gemma":0.000255672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002562876,"about_ca_topic_score_gemma":0.002865268,"domain_scores_codex":[0.999707,0.00007672353,0.00001257733,0.0001004978,0.00006011214,0.00004307204],"domain_scores_gemma":[0.9983371,0.0008470429,0.0003886409,0.0001305862,0.0001299385,0.0001664858],"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":[0.002124076,0.00036087,0.1215188,0.0003921023,0.0001287149,0.0003532252,0.008482899,0.0009197755,0.7441232,0.004179925,0.002035417,0.115381],"study_design_scores_gemma":[0.000100377,0.0009612572,0.9486356,0.00005374752,0.0001339187,0.0003886146,0.001568558,0.008166323,0.02980062,0.008090692,0.002043467,0.0000568653],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934967,0.0002626351,0.002989571,0.0001819373,0.00001271282,0.00001444146,0.00003708048,0.00003995885,0.002964988],"genre_scores_gemma":[0.996858,0.00009961744,0.002408811,0.00008707884,0.000008512548,0.00001084398,0.00004759084,0.0000337145,0.0004457367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002562876,"threshold_uncertainty_score":0.005095899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0317140707781886,"score_gpt":0.3165998314630355,"score_spread":0.2848857606848469,"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."}}