{"id":"W2609213948","doi":"10.3758/s13423-017-1280-1","title":"Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach","year":2017,"lang":"en","type":"article","venue":"Psychonomic Bulletin & Review","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Anterior cingulate cortex; Neurophysiology; Neuroscience; Psychology; Artificial neural network; Neuroimaging; Computational model; Cortex (anatomy); Action (physics); Function (biology); Sequence (biology); Artificial intelligence; Computer science; Cognition","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":[0.000613115,0.0005430497,0.0005289619,0.0004136478,0.0001002138,0.0008297667,0.0009154912,0.0006100403,0.0007584576],"category_scores_gemma":[0.001244855,0.0002837465,0.0006290535,0.0006263207,0.0004048568,0.001128615,0.0003222976,0.0007723465,0.0001989906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004268714,"about_ca_system_score_gemma":0.0002777311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00182881,"about_ca_topic_score_gemma":0.001794096,"domain_scores_codex":[0.9999114,0.00002299028,0.000006702899,0.00003040554,0.00001951005,0.000008939768],"domain_scores_gemma":[0.9997388,0.0001441492,0.00003455802,0.00002470598,0.00004705029,0.00001079684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002315945,0.00009536178,0.004159573,0.001288855,0.000830129,0.00036532,0.0002641291,0.1973583,0.0514361,0.04445571,0.003058825,0.696456],"study_design_scores_gemma":[0.00003231849,0.0001648259,0.01578845,0.0001635784,0.0003871285,0.000634489,0.00008738867,0.8966708,0.009916279,0.06964398,0.006428985,0.00008175326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09285386,0.0629378,0.8359275,0.001677896,0.0003355129,0.00008243932,0.0003601581,0.0004010596,0.00542384],"genre_scores_gemma":[0.8358911,0.0561089,0.1032281,0.0001990024,0.0006668312,0.0001083781,0.0005207768,0.00009609189,0.003180892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00182881,"threshold_uncertainty_score":0.003636301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0704533811467741,"score_gpt":0.3460847737596154,"score_spread":0.2756313926128413,"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."}}