{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008817809,0.0005286048,0.0003156752,0.001946478,0.0002229465,0.001135586,0.0004529924,0.0005318928,0.00108001],"category_scores_gemma":[0.006863703,0.0001816346,0.0004542111,0.0008002753,0.0007215882,0.001377365,0.001124155,0.0005229546,0.0002197102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004648325,"about_ca_system_score_gemma":0.0002264303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003582717,"about_ca_topic_score_gemma":0.007159469,"domain_scores_codex":[0.9992326,0.0002116796,0.00004838705,0.0002890173,0.0001540611,0.00006423302],"domain_scores_gemma":[0.9965969,0.001896885,0.0005579384,0.0004851733,0.000296743,0.0001664419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002477926,0.0006008449,0.5368896,0.002333679,0.0008859999,0.0006241807,0.00518767,0.0622555,0.1320325,0.008058406,0.009884656,0.2387691],"study_design_scores_gemma":[0.00004164394,0.0003121666,0.799754,0.00009724472,0.00008219049,0.0005374506,0.001485605,0.1743684,0.009366647,0.009292276,0.004559038,0.0001033845],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9657856,0.0005295346,0.02726111,0.0001463376,0.00001933888,0.00007620613,0.002800509,0.0002123969,0.003168995],"genre_scores_gemma":[0.9811161,0.0001601085,0.01348315,0.00003900488,0.00001637524,0.0000444136,0.004831958,0.00003643618,0.0002725241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003582717,"threshold_uncertainty_score":0.007123709,"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."}}