{"id":"W4361302108","doi":"10.1093/cercor/bhad092","title":"Generalization of cognitive maps across space and time","year":2023,"lang":"en","type":"article","venue":"Cerebral Cortex","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institutes of Health","keywords":"Ventromedial prefrontal cortex; Psychology; Cognitive map; Cognitive psychology; Cognition; Generalization; Hippocampus; Prefrontal cortex; Sequence learning; Neuroscience; Artificial intelligence; Computer science; Mathematics","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":[0.0003664771,0.0002419143,0.0001508729,0.0005056518,0.0001777221,0.0007253506,0.0003479289,0.0002224537,0.0008517507],"category_scores_gemma":[0.001965402,0.0001766356,0.0002561524,0.000252683,0.0007110599,0.00132411,0.0008762831,0.0004055964,0.00006919398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003198839,"about_ca_system_score_gemma":0.0002469342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001722425,"about_ca_topic_score_gemma":0.001571255,"domain_scores_codex":[0.9997898,0.00002688012,0.00001040241,0.00007815569,0.00007077518,0.00002406737],"domain_scores_gemma":[0.9992357,0.000152901,0.0002885825,0.0002024963,0.00007046032,0.00004970464],"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":[0.0007734734,0.0003033244,0.1242041,0.0002608954,0.0004323176,0.0004474041,0.003432164,0.04639093,0.4614076,0.03568809,0.0008524041,0.3258073],"study_design_scores_gemma":[0.00004110876,0.001378965,0.7218516,0.00005989652,0.0001453497,0.0008667552,0.001854756,0.07576759,0.1043671,0.08888324,0.004661015,0.0001225834],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9635629,0.0001714923,0.03325643,0.00008909714,0.00001005302,0.00001404371,0.00006675346,0.00009404109,0.002735017],"genre_scores_gemma":[0.9965661,0.00006671525,0.003068888,0.00001082992,0.000003209532,0.000008503842,0.00004613947,0.000004994442,0.0002246284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001722425,"threshold_uncertainty_score":0.003424764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06102444531886946,"score_gpt":0.3189250469368194,"score_spread":0.2579006016179499,"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."}}