{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006448532,0.0003097387,0.0003190889,0.0002459701,0.0002088827,0.0007341589,0.000791116,0.0005378941,0.001232538],"category_scores_gemma":[0.00412599,0.0002212364,0.0003993043,0.0002220568,0.0006632639,0.001704927,0.0004461299,0.0008861426,0.0002173176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006912661,"about_ca_system_score_gemma":0.0004570098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001716321,"about_ca_topic_score_gemma":0.002277931,"domain_scores_codex":[0.9998215,0.00005859837,0.000008153329,0.00005417246,0.00004008264,0.00001764483],"domain_scores_gemma":[0.9986809,0.0008550787,0.0001226761,0.0002415912,0.0000669694,0.00003279356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001882066,0.00008759418,0.0102681,0.0001327621,0.0001276541,0.00008031771,0.00009723645,0.8536767,0.04707775,0.05426972,0.0006938283,0.03330014],"study_design_scores_gemma":[0.000005435829,0.00001919813,0.0009496075,0.000002398719,0.000007822832,0.00001723039,0.00001092459,0.9711642,0.005194604,0.02245085,0.0001721846,0.000005478394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6007935,0.0001543767,0.3940627,0.0005570648,0.00003024718,0.00002838066,0.0003158405,0.0005675802,0.003490289],"genre_scores_gemma":[0.9627247,0.00006723663,0.0366602,0.00004166775,0.000007097321,0.00003066009,0.0001244974,0.00002627448,0.0003176302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001716321,"threshold_uncertainty_score":0.005015492,"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."}}