{"id":"W4310191282","doi":"10.1101/2022.11.27.517985","title":"Long-term, multi-event surprise enhances autobiographical memory","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Sport Psychology and Performance","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Surprise; Odds; Event (particle physics); Autobiographical memory; Psychology; Cognitive psychology; Term (time); Relevance (law); Long-term memory; Computer science; Cognition; Social psychology; Recall; Neuroscience; Machine learning; Logistic regression","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":[0.0003521112,0.000187726,0.0002377928,0.0002273245,0.0001130886,0.0005789116,0.0001714149,0.0002640918,0.003744613],"category_scores_gemma":[0.002673855,0.0001317311,0.0001328314,0.0001415459,0.000188662,0.0004545152,0.0005613902,0.000347361,0.0002561065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001776369,"about_ca_system_score_gemma":0.0001474744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007295033,"about_ca_topic_score_gemma":0.0009129251,"domain_scores_codex":[0.999862,0.00002422221,0.000006910846,0.00004132186,0.00004043422,0.00002522041],"domain_scores_gemma":[0.9990672,0.0003558628,0.0001878745,0.0001256827,0.00009083068,0.0001724879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.006464852,0.000993727,0.2226933,0.0004815167,0.0002958475,0.0005234032,0.001198395,0.004890983,0.6214563,0.001622712,0.002938683,0.1364402],"study_design_scores_gemma":[0.00003621732,0.00160807,0.9488265,0.00003471981,0.00006353288,0.0002277944,0.0004918518,0.011491,0.03253042,0.002993863,0.001669229,0.00002690981],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971686,0.00009205886,0.001191544,0.00005476866,0.00002179677,0.000003869053,0.0001162386,0.00003553988,0.001315609],"genre_scores_gemma":[0.9989729,0.00003963367,0.0003848734,0.00002711389,0.00001205305,0.000004124865,0.0001031964,0.000008002211,0.000448197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003744613,"threshold_uncertainty_score":0.01252693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02417083373514077,"score_gpt":0.2971314419821734,"score_spread":0.2729606082470327,"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."}}