{"id":"W3097105308","doi":"10.1016/j.neuron.2020.10.013","title":"The Anterior Cingulate Cortex Predicts Future States to Mediate Model-Based Action Selection","year":2020,"lang":"en","type":"article","venue":"Neuron","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":187,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Neurological Disorders and Stroke; Fundação para a Ciência e a Tecnologia; European Research Council; National Institutes of Health; Max-Planck-Gesellschaft; CANDU Owners Group; University of Oxford; Gatsby Charitable Foundation; Australian Research Council; Wellcome Trust; Alexander von Humboldt-Stiftung","keywords":"Anterior cingulate cortex; Action selection; Task (project management); Optogenetics; Neuroscience; Action (physics); Psychology; Reinforcement learning; Reinforcement; Cognitive psychology; Cingulate cortex; Selection (genetic algorithm); Computer science; Control (management); Artificial intelligence; Cognition; Social psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000216848,0.0003201838,0.0001657586,0.0001540569,0.0001463795,0.0006923018,0.0002932308,0.0002983792,0.001122211],"category_scores_gemma":[0.001027224,0.0002015428,0.0002507692,0.00008464896,0.0004584363,0.0004132659,0.0003719633,0.0005264226,0.0001800651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005417521,"about_ca_system_score_gemma":0.000585216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005533362,"about_ca_topic_score_gemma":0.007692214,"domain_scores_codex":[0.9998753,0.00002044161,0.000005962569,0.00004223889,0.00003138374,0.00002463932],"domain_scores_gemma":[0.9996899,0.00008358729,0.00008980821,0.00005096274,0.00003409883,0.00005156155],"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":[0.000396869,0.0001161993,0.03076211,0.0001306474,0.0002049927,0.0003397648,0.0002330553,0.03320037,0.8766723,0.0127507,0.00226386,0.04292903],"study_design_scores_gemma":[0.00009797452,0.0005492213,0.2743876,0.00007682676,0.0002639194,0.0004236134,0.0002376429,0.4918859,0.2020689,0.02369581,0.006217071,0.00009553734],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.924827,0.0002879686,0.06256586,0.000763472,0.0001131709,0.00003652022,0.0005188598,0.0005728228,0.0103143],"genre_scores_gemma":[0.9934974,0.00008345215,0.005368537,0.00003874665,0.000007263082,0.00001139397,0.0001264532,0.00002928142,0.0008375114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005533362,"threshold_uncertainty_score":0.0110023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02306771113408369,"score_gpt":0.2530412953734754,"score_spread":0.2299735842393917,"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."}}