{"id":"W4411209042","doi":"10.1016/j.cell.2025.05.025","title":"Dopamine encodes deep network teaching signals for individual learning trajectories","year":2025,"lang":"en","type":"article","venue":"Cell","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council; Gatsby Charitable Foundation; Medical Research Council; Canadian Institute for Advanced Research; Royal Society; UK Research and Innovation; Wellcome Trust; European Research Council; Human Frontier Science Program","keywords":"Biology; Optogenetics; Dopamine; Neuroscience; Dopaminergic; Associative learning; Machine learning; Artificial intelligence; Stimulus (psychology); Cognitive psychology; Computer science; 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":[0.0002925221,0.0003164628,0.0002728899,0.00021449,0.0002583326,0.0005808345,0.0005439381,0.0004523333,0.001026881],"category_scores_gemma":[0.001478795,0.0002789055,0.0002812964,0.0001519264,0.0006738257,0.0008422546,0.0005136692,0.001036468,0.0001206455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009460741,"about_ca_system_score_gemma":0.0006366131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002983585,"about_ca_topic_score_gemma":0.005138073,"domain_scores_codex":[0.9999164,0.00001220957,0.000003710157,0.00003174642,0.0000153952,0.00002058695],"domain_scores_gemma":[0.999715,0.00009159571,0.00006868914,0.00003640817,0.00004076845,0.00004754674],"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":[0.0001448268,0.0001147526,0.02538891,0.00008447203,0.0001199263,0.0002314343,0.0002008595,0.7451875,0.1212737,0.05485931,0.001154903,0.05123943],"study_design_scores_gemma":[0.00000376429,0.0000276011,0.003764504,0.000004982909,0.00001209929,0.00002955556,0.0000172283,0.9794422,0.004134406,0.01227005,0.0002817638,0.00001184117],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7546646,0.0001321782,0.2421816,0.0004034083,0.00002929901,0.0000174151,0.000187799,0.0002327598,0.002150968],"genre_scores_gemma":[0.9903295,0.00005484741,0.008577671,0.00002119747,0.000002916132,0.00001651002,0.00005615648,0.00002414104,0.0009170612],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002983585,"threshold_uncertainty_score":0.006864309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02159981098291369,"score_gpt":0.2603644705444803,"score_spread":0.2387646595615666,"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."}}