{"id":"W4319311207","doi":"10.1167/jov.23.2.4","title":"Sensitivity to naturalistic texture relies primarily on high spatial frequencies","year":2023,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Eye Institute; York University","keywords":"Artificial intelligence; Spatial frequency; Computer vision; Property (philosophy); Sensitivity (control systems); Texture (cosmology); Computer science; Perception; Pattern recognition (psychology); Psychophysics; Human visual system model; Visual perception; Image texture; Image (mathematics); Image processing; Physics; Psychology; Optics; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005200027,0.0001079432,0.0001735772,0.0002508342,0.0001769769,0.0001081537,0.0001246844,0.00007524502,0.00006769248],"category_scores_gemma":[0.001548411,0.00007657987,0.00007103862,0.0003945616,0.00003285394,0.0001890525,0.00004979961,0.0003223526,0.0003370468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006003459,"about_ca_system_score_gemma":0.00004968276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001796009,"about_ca_topic_score_gemma":0.000008264788,"domain_scores_codex":[0.9986702,0.0001589612,0.0002431479,0.0001669574,0.0005939733,0.0001668042],"domain_scores_gemma":[0.9992293,0.0002559503,0.0001738999,0.0001081594,0.0001189038,0.0001138114],"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":[0.000138238,0.00003609,0.000007829989,0.00001259191,0.000001132384,0.0001482122,0.0003075965,0.0003605752,0.9740255,0.0004821408,0.006570698,0.01790936],"study_design_scores_gemma":[0.001459594,0.006184607,0.1518769,0.001137639,0.00004119573,0.0007001678,0.0003789985,0.005999492,0.8052049,0.01322145,0.01310444,0.0006906459],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916202,0.000005939638,0.001781976,0.004861829,0.001354202,0.00006671499,0.000009006536,0.00006387552,0.0002361899],"genre_scores_gemma":[0.9951687,0.00003160283,0.0003475746,0.003554674,0.000307632,4.127387e-7,0.000001180134,0.00001264657,0.0005756015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1688206,"threshold_uncertainty_score":0.4332168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04132213142213622,"score_gpt":0.3404053274011614,"score_spread":0.2990831959790252,"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."}}