{"id":"W3183361846","doi":"10.31219/osf.io/whdkx","title":"Learning exceptions to the rule in human and model via hippocampal encoding","year":2021,"lang":"en","type":"article","venue":"","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Categorization; Similarity (geometry); Task (project management); Encoding (memory); Sequence learning; Replicate; Psychology; Concept learning; Cognitive psychology; Artificial intelligence; Sequence (biology); Computational model; Natural language processing; Representation (politics); Computer science; Learning rule; Machine learning; Artificial neural network; Mathematics","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":[0.0002264408,0.0001246404,0.0002185561,0.0001995019,0.0001317797,0.0009652366,0.0004713037,0.0003046188,0.001218634],"category_scores_gemma":[0.001586334,0.0001834694,0.0003575285,0.0001482925,0.0007781294,0.00114214,0.0003108158,0.0003536401,0.0002005013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004687164,"about_ca_system_score_gemma":0.0005197584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005124597,"about_ca_topic_score_gemma":0.006982838,"domain_scores_codex":[0.9999174,0.00001945059,0.000004019069,0.00002928656,0.00002079328,0.000009044769],"domain_scores_gemma":[0.9995307,0.0001834462,0.00007757813,0.000137889,0.00004324235,0.00002712885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003835856,0.0001319586,0.03149185,0.0002569488,0.0001607539,0.0008225736,0.001825613,0.5727084,0.07676678,0.1810443,0.002223258,0.1321841],"study_design_scores_gemma":[0.00002677436,0.0001422609,0.0136317,0.00003067067,0.00003652463,0.0004466774,0.0001811943,0.8149642,0.01575139,0.1516563,0.003086162,0.00004604077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8281736,0.0001337471,0.1653346,0.0002194566,0.00002129974,0.00001604699,0.0002588162,0.0005010722,0.005341249],"genre_scores_gemma":[0.9695612,0.00008399057,0.02936017,0.00002152381,0.000003076296,0.00001213976,0.0001395995,0.00003381947,0.0007842794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005124597,"threshold_uncertainty_score":0.01018953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02650501175351864,"score_gpt":0.3068259782453661,"score_spread":0.2803209664918474,"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."}}