{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009767651,0.0009163055,0.0009974001,0.001382828,0.0003798606,0.001402703,0.001493785,0.0008718628,0.0001036918],"category_scores_gemma":[0.0007977855,0.001082807,0.000219475,0.002157818,0.00009709426,0.002341996,0.0004085185,0.001447421,0.002443118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006640354,"about_ca_system_score_gemma":0.0006614432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00586393,"about_ca_topic_score_gemma":0.0006945716,"domain_scores_codex":[0.994213,0.0003982087,0.001807203,0.0009883194,0.001460821,0.001132485],"domain_scores_gemma":[0.995603,0.0008507735,0.001720436,0.001004015,0.0005132263,0.000308519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001527724,0.00004384276,0.001649667,0.0007783533,0.0001978257,0.0001122926,0.1479877,0.8394209,0.00006497838,0.0003533426,0.00009755044,0.009140756],"study_design_scores_gemma":[0.001112086,0.0001534954,0.0132473,0.004627689,0.00008990648,0.000003789985,0.05010316,0.9293745,0.0000681868,0.00003725801,0.0002038089,0.000978874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06026262,0.0002860932,0.9259274,0.000161472,0.006445476,0.001781326,0.00002707025,0.0004853454,0.004623175],"genre_scores_gemma":[0.9771777,0.0004806656,0.003144688,0.00008038551,0.000225697,0.0001631592,0.004224094,0.0001330666,0.01437048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9227827,"threshold_uncertainty_score":0.9996339,"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."}}