{"id":"W4317438807","doi":"10.1371/journal.pcbi.1010808","title":"Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals","year":2023,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Global Health Research; Canadian Institute for Advanced Research","funders":"Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Wellcome; Canadian Institute for Advanced Research; Medical Research Council; University of Oxford; Wellcome Trust","keywords":"Hebbian theory; Computer science; Artificial intelligence; Context (archaeology); Artificial neural network; Task (project management); Gating; Forgetting; Heuristics; Machine learning; Pattern recognition (psychology)","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":[0.0007072078,0.0003569723,0.0003783063,0.0002149935,0.0001755525,0.0006175287,0.0008167739,0.0008355953,0.001286217],"category_scores_gemma":[0.002989706,0.0003737703,0.0003514415,0.0001740157,0.0008842175,0.0009704229,0.000557044,0.001110609,0.0001977483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005477974,"about_ca_system_score_gemma":0.0005688226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004590873,"about_ca_topic_score_gemma":0.007079947,"domain_scores_codex":[0.9997948,0.00007164909,0.000007119101,0.00006994057,0.00002172718,0.00003475824],"domain_scores_gemma":[0.9991062,0.0005425793,0.0001042806,0.000100076,0.00007438517,0.00007231432],"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":[0.00014447,0.00007130251,0.002042715,0.00003652207,0.00003642113,0.00007253727,0.0001400854,0.9535875,0.006338902,0.0159392,0.0003097606,0.02128054],"study_design_scores_gemma":[0.000005803809,0.00001425771,0.0003430221,0.000001738683,0.000002644282,0.00001141809,0.000003267467,0.9887685,0.0003778502,0.010377,0.00009145292,0.000003051304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3430031,0.000200827,0.653347,0.0005704712,0.00003911598,0.0000345827,0.00008605471,0.0003235056,0.002395283],"genre_scores_gemma":[0.9694044,0.00007467872,0.02880882,0.00005840864,0.000009278422,0.00003930817,0.0000318173,0.00002186586,0.00155138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004590873,"threshold_uncertainty_score":0.009128273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0408393312688971,"score_gpt":0.2604761208785358,"score_spread":0.2196367896096387,"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."}}