{"id":"W2559277598","doi":"10.1523/jneurosci.0763-16.2016","title":"Memory Transformation Enhances Reinforcement Learning in Dynamic Environments","year":2016,"lang":"en","type":"article","venue":"Journal of Neuroscience","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Schematic; Computer science; Episodic memory; Memory consolidation; Salience (neuroscience); Adaptive memory; Transformation (genetics); Artificial intelligence; Cognition; Psychology; Neuroscience","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":[0.0002123793,0.0002339401,0.0002021377,0.00009724801,0.0001098838,0.0004639089,0.000456639,0.0003201392,0.001873464],"category_scores_gemma":[0.001338276,0.0001208325,0.0002368897,0.00009032396,0.000392515,0.0009166556,0.000631207,0.0005331417,0.0002210407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002626136,"about_ca_system_score_gemma":0.000271532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003821143,"about_ca_topic_score_gemma":0.0004286013,"domain_scores_codex":[0.9999045,0.00001916973,0.000006432326,0.00002988155,0.00002239684,0.00001753484],"domain_scores_gemma":[0.999625,0.0001263521,0.00007897358,0.00007765117,0.00004461003,0.00004742183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005637924,0.001021118,0.009067792,0.0002499214,0.0001259042,0.0005481385,0.0003622547,0.3389385,0.3867795,0.03492923,0.001043029,0.2263708],"study_design_scores_gemma":[0.00005577445,0.0009504893,0.00616607,0.00001583464,0.00005098713,0.0002516131,0.00006659648,0.854353,0.09729236,0.03761544,0.003150037,0.00003185348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8664551,0.0001540532,0.1256853,0.0002937847,0.00004502523,0.00002912305,0.00004404283,0.0007894299,0.006504159],"genre_scores_gemma":[0.9906507,0.00004088858,0.008637719,0.00003346174,0.000003785503,0.000009571894,0.00002637602,0.00001888476,0.0005785004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001873464,"threshold_uncertainty_score":0.006267369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01410416141288427,"score_gpt":0.2480868169701692,"score_spread":0.2339826555572849,"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."}}