{"id":"W2296089504","doi":"","title":"Simulating the N400 ERP component as semantic network error: Insights from a feature-based connectionist attractor model of word meaning.","year":2013,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"N400; Connectionism; Computer science; Semantics (computer science); Semantic memory; Component (thermodynamics); Natural language processing; Word (group theory); Artificial intelligence; Speech recognition; Cognition; Artificial neural network; Event-related potential; Psychology; Linguistics; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006799207,0.0002951827,0.0002000726,0.0002876009,0.0001682157,0.0003413766,0.0006009532,0.00068404,0.001271845],"category_scores_gemma":[0.004781084,0.0001621503,0.0003360081,0.0001599285,0.0004359559,0.0009544743,0.000427424,0.0005505885,0.00007387047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005084157,"about_ca_system_score_gemma":0.0004205995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003132805,"about_ca_topic_score_gemma":0.003113257,"domain_scores_codex":[0.9998821,0.00005555827,0.00000536804,0.00002134212,0.00002184558,0.00001383283],"domain_scores_gemma":[0.9990526,0.0006843061,0.0000804857,0.00008480643,0.00005990522,0.00003793234],"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":[0.0003385658,0.0002146941,0.01072514,0.00007519133,0.00008752036,0.0003725816,0.0003620512,0.9220099,0.02106925,0.03332278,0.0005469832,0.01087536],"study_design_scores_gemma":[0.00001399721,0.00003759813,0.00110786,0.000001623255,0.000006134717,0.00002830468,0.000009739557,0.9882916,0.0005938362,0.009836107,0.00006866264,0.000004627262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8866393,0.00003997211,0.1090093,0.0003216314,0.00003160595,0.00006218364,0.0001164054,0.0001166121,0.003663003],"genre_scores_gemma":[0.9911087,0.00001513059,0.008476324,0.00001467688,0.000002752748,0.00002872021,0.00002764805,0.00001223179,0.000313974],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003132805,"threshold_uncertainty_score":0.006229162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1115129767921871,"score_gpt":0.3091727927992304,"score_spread":0.1976598160070433,"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."}}