{"id":"W7115593364","doi":"10.1016/j.jvcir.2025.104686","title":"Aligning computational and human perceptions of image complexity: A dual-task framework for prediction and localization","year":2025,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Science Basic Research Program of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computational complexity theory; Image (mathematics); Computational model; Perception; Pattern recognition (psychology); Focus (optics); Human visual system model; Gaze","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":[0.001764801,0.0005735688,0.0006734126,0.0008972205,0.0003268085,0.002134746,0.001086832,0.0009619394,0.002471934],"category_scores_gemma":[0.01183625,0.0004658463,0.0005910894,0.0006727893,0.001039469,0.003019963,0.001729179,0.001407373,0.0002303187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007575149,"about_ca_system_score_gemma":0.0007372758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003524592,"about_ca_topic_score_gemma":0.002520996,"domain_scores_codex":[0.9992734,0.0002432606,0.00003025876,0.0002346164,0.0001266278,0.00009185373],"domain_scores_gemma":[0.9973733,0.00137355,0.0003922928,0.0003925091,0.0002638038,0.0002045485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002639084,0.001194891,0.03177968,0.0007126502,0.0005544983,0.0003064391,0.001972498,0.1897119,0.178648,0.1695854,0.003689412,0.4192055],"study_design_scores_gemma":[0.00006299458,0.0002354956,0.02351266,0.00002656437,0.0000648501,0.0001050547,0.0001700669,0.8356227,0.007779972,0.1314784,0.0008829843,0.00005830693],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2298916,0.0004193523,0.7617095,0.0009293668,0.00008808042,0.0001105113,0.0003145205,0.0003184885,0.006218459],"genre_scores_gemma":[0.9430959,0.00008344439,0.05597398,0.00006212284,0.00003010692,0.00005149359,0.0001031311,0.00004752061,0.0005523505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003524592,"threshold_uncertainty_score":0.009333313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03404564278988789,"score_gpt":0.3991100589905331,"score_spread":0.3650644162006453,"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."}}