{"id":"W2155851653","doi":"10.1093/cercor/bhg111","title":"N170 or N1? Spatiotemporal Differences between Object and Face Processing Using ERPs","year":2003,"lang":"en","type":"article","venue":"Cerebral Cortex","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":671,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Psychology; Object (grammar); Segmentation; Face (sociological concept); Electrophysiology; Face perception; Communication; Artificial intelligence; Cognitive psychology; Pattern recognition (psychology); Computer science; Neuroscience; Perception","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":[0.0001011963,0.0001618078,0.000188453,0.00007849081,0.0002897558,0.0001640264,0.00008668459,0.00008861196,0.0004709467],"category_scores_gemma":[0.0002292777,0.0001272938,0.00003373264,0.0002470757,0.0001293955,0.0003888043,0.00002609621,0.0001604051,0.00005145607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003021965,"about_ca_system_score_gemma":0.00009511732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000424128,"about_ca_topic_score_gemma":0.00004015602,"domain_scores_codex":[0.9987994,0.000146849,0.000200732,0.0003855712,0.0002085919,0.0002588367],"domain_scores_gemma":[0.9995826,0.00007015318,0.0001008613,0.0001015916,0.00002795409,0.0001168628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001088625,0.0001530248,0.5389948,0.0003185751,0.00001305302,0.00003089424,0.002770193,0.00001021529,0.2608454,0.0003273636,0.0002339976,0.1961936],"study_design_scores_gemma":[0.002286306,0.0005945656,0.8603737,0.0004372048,0.0001084382,0.0002443478,0.001960545,0.01181693,0.1159707,0.00215578,0.00256901,0.001482486],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961605,0.00003908941,0.001409953,0.00006996895,0.0001318081,0.0001972186,0.00001680023,0.00008798897,0.001886645],"genre_scores_gemma":[0.9984301,0.00002138681,0.0004630735,0.0002340377,0.00006338153,0.000004048162,0.00000435348,0.00001649791,0.0007630726],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3213789,"threshold_uncertainty_score":0.5190892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1329413510801465,"score_gpt":0.3249086843788601,"score_spread":0.1919673332987136,"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."}}