{"id":"W7133015284","doi":"","title":"Acquiring Information from Bayesian Surprise in Cognitive Linear Gaussian Dynamic Systems","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Surprise; Novelty; Bayesian probability; Gaussian process; State (computer science); Gaussian; Cognition; Credibility","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001724723,0.0007756116,0.001053961,0.0005083535,0.0005202398,0.001469742,0.0008965066,0.0009868254,0.0009551384],"category_scores_gemma":[0.00849842,0.0003934679,0.0006102438,0.0005083595,0.001246945,0.001500939,0.001606535,0.001353507,0.0001465819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001283106,"about_ca_system_score_gemma":0.001489515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005753849,"about_ca_topic_score_gemma":0.002901353,"domain_scores_codex":[0.9991628,0.0002262294,0.00004532663,0.0001869803,0.0002693731,0.0001091666],"domain_scores_gemma":[0.9959132,0.002969295,0.0004508536,0.0001699422,0.0003524275,0.0001440997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001600274,0.00004532289,0.001156926,0.00008044557,0.00003717962,0.0001095928,0.000158894,0.936909,0.002435189,0.02235803,0.0002822598,0.03626718],"study_design_scores_gemma":[0.000006476187,0.00003044746,0.0001910669,0.000003721708,0.000007598809,0.0000174768,0.000009327755,0.9905705,0.0006565565,0.00839249,0.0001063749,0.000007988075],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07413863,0.0002418161,0.9227155,0.0002472408,0.00001925729,0.00003915176,0.00002903478,0.0003214359,0.002247919],"genre_scores_gemma":[0.9432541,0.0001865003,0.05534031,0.00008643889,0.00001953097,0.00006543225,0.00005175777,0.0000311588,0.0009648029],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005753849,"threshold_uncertainty_score":0.01144069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02310125704390877,"score_gpt":0.3348049534629697,"score_spread":0.311703696419061,"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."}}