{"id":"W2951509525","doi":"10.1038/srep40201","title":"Neural codes of seeing architectural styles","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Fusiform face area; Encoding (memory); Representation (politics); Computer science; Perception; Visual cortex; Face perception; Architecture; Face (sociological concept); Style (visual arts); Visual perception; Artificial intelligence; Artificial neural network; Psychology; Neuroscience; Geography; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0001514647,0.000141661,0.0001045708,0.0003202644,0.0001258333,0.0006834265,0.0001705365,0.0002839911,0.001727079],"category_scores_gemma":[0.001224005,0.0001190179,0.0001710604,0.0001730978,0.0004211983,0.000435145,0.0004378414,0.000265001,0.0001958319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002762021,"about_ca_system_score_gemma":0.000151361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003208852,"about_ca_topic_score_gemma":0.003958298,"domain_scores_codex":[0.9999182,0.00001088232,0.000002772929,0.0000266578,0.0000145433,0.00002690609],"domain_scores_gemma":[0.9997582,0.00008220185,0.00004966082,0.00003131456,0.00004109836,0.00003753407],"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":[0.0005513169,0.00005719029,0.06123336,0.0002044285,0.0001181381,0.0004191469,0.001879892,0.006300073,0.6964663,0.01146622,0.002133801,0.2191702],"study_design_scores_gemma":[0.00001934169,0.00012202,0.9313582,0.0000503418,0.00005419202,0.0005070387,0.0005716964,0.01895758,0.03275741,0.01354037,0.00203225,0.00002955087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747974,0.0002784635,0.01255272,0.0001784559,0.00003919404,0.00001899263,0.0003027591,0.00007934998,0.01175276],"genre_scores_gemma":[0.9964934,0.0001185821,0.001955338,0.00003577034,0.000007666255,0.000007401817,0.0001103736,0.000008572463,0.001262833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003208852,"threshold_uncertainty_score":0.00638032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06710172425818178,"score_gpt":0.3195042865039718,"score_spread":0.2524025622457901,"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."}}