{"id":"W3197782019","doi":"10.1167/jov.21.9.2285","title":"Deep Neural Network Selectivity for Global Shape","year":2021,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Aesthetic Perception and Analysis","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Coherence (philosophical gambling strategy); Artificial intelligence; Convolutional neural network; Computer science; Stylized fact; Pattern recognition (psychology); Deep learning; Object (grammar); Spatial coherence; Deep neural networks; Artificial neural network; Computer vision; Mathematics","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":[0.0004828847,0.0006645122,0.000292047,0.0003148303,0.0001943467,0.0006612902,0.0004872056,0.000469579,0.00347299],"category_scores_gemma":[0.001688886,0.0002265104,0.0003800602,0.0002820741,0.0005784806,0.0007462018,0.0005298546,0.0007092181,0.0007451171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007605884,"about_ca_system_score_gemma":0.0004115561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004024779,"about_ca_topic_score_gemma":0.005637863,"domain_scores_codex":[0.9996918,0.0000217794,0.00001961757,0.0001209632,0.00008349615,0.00006217541],"domain_scores_gemma":[0.9994097,0.0002176434,0.0001041005,0.0001065982,0.0001057821,0.00005613251],"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":[0.001503177,0.0003534086,0.0464527,0.0006589533,0.0002338524,0.0003357711,0.0001417929,0.1676568,0.5636749,0.004396838,0.005175473,0.2094164],"study_design_scores_gemma":[0.0001055223,0.000707911,0.09471531,0.0001000943,0.0001435549,0.0005984292,0.0001213214,0.679526,0.209927,0.007881278,0.006098801,0.00007488712],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959093,0.0005989416,0.02929898,0.0003543236,0.00007478417,0.0000488107,0.001740058,0.0006242623,0.008166763],"genre_scores_gemma":[0.9890066,0.0001773498,0.007726087,0.0001314463,0.00001483625,0.00003107403,0.001549248,0.00005628389,0.001306971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004024779,"threshold_uncertainty_score":0.01161832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02807382374188205,"score_gpt":0.3325232087963844,"score_spread":0.3044493850545024,"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."}}