{"id":"W4391117034","doi":"10.1609/aaaiss.v2i1.27686","title":"Bridging Cognitive Architectures and Generative Models with Vector Symbolic Algebras","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Symposium Series","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Generative grammar; Bridging (networking); Computer science; Cognition; Cognitive science; Cognitive architecture; Cognitive model; Artificial intelligence; Set (abstract data type); Generative model; Theoretical computer science; 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":[0.00391028,0.0008575403,0.0008912558,0.002206829,0.00115037,0.005291768,0.002042393,0.001602409,0.006159743],"category_scores_gemma":[0.01264999,0.0007145038,0.002214116,0.00185935,0.007772648,0.009741972,0.00367059,0.00395735,0.0009446132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00253856,"about_ca_system_score_gemma":0.001423198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003113047,"about_ca_topic_score_gemma":0.003008113,"domain_scores_codex":[0.9974971,0.001454812,0.0001453754,0.000324363,0.0003939648,0.0001842881],"domain_scores_gemma":[0.992135,0.005397291,0.0005653257,0.001124417,0.0004197465,0.0003581111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005362367,0.000005671188,0.00008988634,0.00001589237,0.00000789042,0.0000196417,0.0001540775,0.005684034,0.00007426285,0.9909882,0.0001446363,0.002810569],"study_design_scores_gemma":[0.000003068835,0.00000349082,0.00002848297,0.000008548761,0.000002225234,0.00001242116,0.00002348654,0.02398681,0.00005064933,0.9750289,0.000846755,0.000005081603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01507916,0.000877096,0.9692042,0.001923219,0.00006153795,0.00003736066,0.0001340578,0.0002248658,0.01245846],"genre_scores_gemma":[0.6590453,0.001903369,0.3287664,0.0007889751,0.0004096318,0.0003330523,0.0004478864,0.0002552422,0.008050125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006159743,"threshold_uncertainty_score":0.02067977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008378286763685062,"score_gpt":0.2084268212199097,"score_spread":0.2000485344562246,"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."}}