{"id":"W4386929819","doi":"10.1101/2023.09.19.558526","title":"Serotonin predictively encodes value","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Receptor Mechanisms and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Government of Ontario","keywords":"Surprise; Serotonergic; Salience (neuroscience); Reinforcement learning; Neuroscience; Psychology; Population; Tonic (physiology); Cognitive psychology; Computer science; Serotonin; Artificial intelligence; Communication; Biology","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":[0.0002767557,0.0001644948,0.0002259607,0.0001502503,0.0001612475,0.0008888007,0.0004299898,0.0004534057,0.002700836],"category_scores_gemma":[0.002167856,0.0001610751,0.0002149252,0.0001150593,0.0007406104,0.0009147906,0.0003395664,0.0005615526,0.0002875833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006608287,"about_ca_system_score_gemma":0.0003641631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001310318,"about_ca_topic_score_gemma":0.0008535815,"domain_scores_codex":[0.9998686,0.00003331034,0.000007353983,0.00003781326,0.00003372833,0.00001915386],"domain_scores_gemma":[0.9992161,0.0003825335,0.0001099314,0.000139141,0.0001003604,0.00005181373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008030033,0.0001474495,0.01978126,0.0003257081,0.0001950795,0.0006912366,0.0003773365,0.3604235,0.2432631,0.2900968,0.002736979,0.08115847],"study_design_scores_gemma":[0.00003171289,0.0001553891,0.008440289,0.00002754441,0.000036006,0.0001577904,0.00005498717,0.8879405,0.02412999,0.07760642,0.00138462,0.00003484913],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8160062,0.0002846249,0.1682745,0.001207665,0.0001208656,0.00002537117,0.0003858815,0.0007227235,0.01297206],"genre_scores_gemma":[0.9966291,0.00003589466,0.00274897,0.00002872443,0.000005317836,0.000003823454,0.00003420013,0.00001390206,0.0005000309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002700836,"threshold_uncertainty_score":0.00903517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01586337429953945,"score_gpt":0.2279938836107975,"score_spread":0.2121305093112581,"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."}}