{"id":"W4309347770","doi":"10.1016/j.cognition.2022.105319","title":"Characterising and dissecting human perception of scene complexity","year":2022,"lang":"en","type":"article","venue":"Cognition","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council; Cancer Research UK","keywords":"Perception; Scene statistics; Artificial intelligence; Set (abstract data type); Computational complexity theory; Semantics (computer science); Computational model; Computer science; Visual perception; Psychology; Pattern recognition (psychology); Algorithm","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":[0.0001677322,0.00005211956,0.00007070355,0.00006226148,0.0006442446,0.00002860822,0.00004337337,0.00001478367,0.0009063271],"category_scores_gemma":[0.00007615417,0.00005885755,0.00001844308,0.0001187204,0.00006331867,0.0001231763,0.00005970074,0.0001051632,0.000005912486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001858011,"about_ca_system_score_gemma":0.000005440995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003173725,"about_ca_topic_score_gemma":7.745714e-7,"domain_scores_codex":[0.9993582,0.0001168886,0.0001199467,0.0001702892,0.0001529383,0.00008168194],"domain_scores_gemma":[0.999786,0.00002013139,0.00008808739,0.00004746798,0.00003228511,0.00002600724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001462614,0.00003547792,0.0001116561,0.00002682859,3.680817e-7,6.889181e-7,0.0008013741,5.5766e-7,0.9835845,0.0001843926,0.000004128544,0.01523544],"study_design_scores_gemma":[0.0009421856,0.0005951292,0.1179044,0.00009555906,0.00003093399,0.0001188415,0.00519762,0.003149738,0.8444486,0.02705275,0.0001229921,0.0003411607],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977317,0.000002653116,0.00101261,0.00005362336,0.0001243505,0.00007966507,0.00001760882,0.00005256063,0.0009252101],"genre_scores_gemma":[0.9994065,0.000003485319,0.000149106,0.0003000391,0.00004391445,0.000009557686,0.00003133481,0.00000766065,0.00004839903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1391358,"threshold_uncertainty_score":0.9923646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1626395179017274,"score_gpt":0.3623257445009052,"score_spread":0.1996862265991778,"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."}}