{"id":"W3201124393","doi":"10.1038/s41467-021-25409-6","title":"Computational models of category-selective brain regions enable high-throughput tests of selectivity","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Quest for Intelligence, Massachusetts Institute of Technology; Multidisciplinary University Research Initiative; Office of Naval Research; Simons Foundation; National Eye Institute; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital; National Institutes of Health; National Science Foundation","keywords":"Computer science; Fusiform face area; Categorization; Computational model; Encoding (memory); Domain (mathematical analysis); Artificial intelligence; Cognition; Machine learning; Pattern recognition (psychology); Neuroscience; Psychology; Face perception; Perception; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001773135,0.0001069081,0.0002018273,0.0001181957,0.0002549124,0.00001621641,0.0004383671,0.0002024133,0.00006447455],"category_scores_gemma":[0.001083518,0.0001156191,0.00008701594,0.001056158,0.0002681648,0.0002699525,0.0001758514,0.0006710597,0.00001305488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006731853,"about_ca_system_score_gemma":0.0003248828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001012482,"about_ca_topic_score_gemma":0.0007041372,"domain_scores_codex":[0.9985403,0.0005336959,0.0002727113,0.0002550693,0.0002552046,0.000143022],"domain_scores_gemma":[0.9966904,0.001595817,0.0001917018,0.0007949488,0.0006789599,0.00004818265],"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":[0.00002801613,0.001292359,0.0004133152,0.00004778116,0.0000332137,0.000001564644,0.001606132,0.01258381,0.4877664,0.481982,0.01266768,0.00157771],"study_design_scores_gemma":[0.001354707,0.0001598491,0.01801203,0.0001231411,0.00007349811,0.0001066714,0.0004616385,0.05524448,0.5306137,0.3866118,0.006750416,0.0004881013],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7498901,0.003717339,0.05081878,0.06413034,0.0007616795,0.001935871,0.001650328,0.0005620393,0.1265335],"genre_scores_gemma":[0.9897416,0.0003288653,0.008670681,0.0007542348,0.00001436803,0.00002527386,0.000166692,0.00001340938,0.0002848959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2398514,"threshold_uncertainty_score":0.471481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.066854971757876,"score_gpt":0.3383050113209141,"score_spread":0.2714500395630381,"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."}}