{"id":"W2084220850","doi":"10.1097/wnr.0b013e3283320e3f","title":"Facial expression decoding as a function of emotional meaning status: ERP evidence","year":2009,"lang":"en","type":"article","venue":"Neuroreport","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Deutscher Akademischer Austauschdienst; Johns Hopkins University","keywords":"N400; Facial expression; Psychology; Event-related potential; Emotional expression; Cognitive psychology; Emotionality; Expression (computer science); Meaning (existential); Cognition; Developmental psychology; Neuroscience; Communication; Computer science","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":[0.000157332,0.0001156476,0.000128835,0.0001213973,0.0001440461,0.00003577305,0.0000794183,0.00006072368,0.0008967605],"category_scores_gemma":[0.001406877,0.0001111467,0.00008133213,0.0002160316,0.00004229991,0.0005342228,0.00002400633,0.0001605831,0.0001191614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002906639,"about_ca_system_score_gemma":0.00007876318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006421446,"about_ca_topic_score_gemma":0.00000131638,"domain_scores_codex":[0.9984441,0.0001002037,0.0003558232,0.0003781024,0.0005103752,0.0002113702],"domain_scores_gemma":[0.9993193,0.0001139278,0.0002296335,0.0001682809,0.00007088981,0.00009799608],"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":[0.0001274132,0.00005339324,0.001358607,0.000008707745,5.499539e-7,0.00002571378,0.0001895944,0.0000554594,0.9644924,0.0002125242,0.0002725943,0.03320308],"study_design_scores_gemma":[0.0004837715,0.0006378085,0.1909374,0.0003489394,0.00002108813,0.000278459,0.0001116577,0.0008097721,0.7988623,0.003395736,0.003824963,0.0002880487],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846837,0.000009143663,0.001029714,0.0001636301,0.0004765574,0.0001507129,0.000004834731,0.0001112438,0.01337047],"genre_scores_gemma":[0.9983568,0.00009002862,0.0002082712,0.0009888974,0.00009330143,0.000004297356,0.000006263631,0.000008880736,0.0002432145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1895788,"threshold_uncertainty_score":0.9818898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1124800431853275,"score_gpt":0.3402714996427375,"score_spread":0.22779145645741,"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."}}