{"id":"W2523172278","doi":"10.3389/fnhum.2016.00474","title":"Development of Effective Connectivity during Own- and Other-Race Face Processing: A Granger Causality Analysis","year":2016,"lang":"en","type":"article","venue":"Frontiers in Human Neuroscience","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Fundamental Research Funds for the Central Universities; National Institutes of Health; National Natural Science Foundation of China","keywords":"Race (biology); Face (sociological concept); Psychology; Causality (physics); Cognitive psychology; Facial recognition system; Developmental psychology; Artificial intelligence; Computer science; Pattern recognition (psychology); Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004143936,0.0001563529,0.0002797905,0.0004076414,0.0003122315,0.00004562947,0.0002058238,0.00005265841,0.00001966821],"category_scores_gemma":[0.0005202912,0.0001190716,0.00005000608,0.001019667,0.0005634088,0.00036556,0.00008204413,0.0001054179,0.00000112915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009442608,"about_ca_system_score_gemma":0.00003654093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001140358,"about_ca_topic_score_gemma":0.00006981102,"domain_scores_codex":[0.9981841,0.0002396735,0.000277238,0.0006914847,0.0003184825,0.000288968],"domain_scores_gemma":[0.9994297,0.00009086394,0.0001745483,0.0001857738,0.00002942773,0.0000897027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003051323,0.00008305495,0.1003449,0.00004154617,0.000002385962,0.000003137417,0.0016397,0.00003645645,0.8846695,0.00001943461,0.00000819103,0.01312118],"study_design_scores_gemma":[0.0004880605,0.00004352815,0.5986051,0.00007521602,0.00002123296,0.000004829033,0.0001468047,0.001135483,0.3987372,0.000128616,0.0003908648,0.0002231377],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9531465,0.00001608806,0.04610585,0.00005748628,0.0001582351,0.0003087126,0.00001782147,0.00004484604,0.0001444669],"genre_scores_gemma":[0.9989432,0.00001369094,0.0006827512,0.0001139119,0.000007800826,0.00003999791,2.060192e-7,0.000009416222,0.0001890403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4982601,"threshold_uncertainty_score":0.4855599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04153683673385056,"score_gpt":0.3013950963790586,"score_spread":0.259858259645208,"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."}}