{"id":"W2980055757","doi":"10.1111/tops.12474","title":"Five Ways in Which Computational Modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning","year":2019,"lang":"en","type":"review","venue":"Topics in Cognitive Science","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Science Foundation Graduate Research Fellowship Program; Economic and Social Research Council; National Institute on Deafness and Other Communication Disorders; Nvidia; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Agence Nationale de la Recherche; National Science Foundation","keywords":"Computer science; Artificial intelligence; Domain (mathematical analysis); Computational model; Grammar; Focus (optics); Cognitive science; Lift (data mining); Visualization; Cognition; Machine learning; Psychology; Mathematics; Linguistics","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":[0.005986845,0.0009339222,0.001304863,0.004648478,0.0007617351,0.003513622,0.001811066,0.002719884,0.001921105],"category_scores_gemma":[0.008191071,0.0005515997,0.0007585952,0.003816033,0.005723375,0.01135393,0.00243944,0.006623464,0.0009431199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830621,"about_ca_system_score_gemma":0.003401869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002363598,"about_ca_topic_score_gemma":0.004051104,"domain_scores_codex":[0.9990482,0.0004980028,0.00009877666,0.0001128721,0.0001893982,0.00005289423],"domain_scores_gemma":[0.9912989,0.00715781,0.0002440797,0.0004080527,0.0006158073,0.0002754999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005939078,0.00008534161,0.0005342404,0.009194525,0.0001866768,0.0003078749,0.001574591,0.001677776,0.0003921545,0.3506663,0.0231054,0.6122156],"study_design_scores_gemma":[0.00003429499,0.00006400789,0.0005366763,0.009610103,0.00009512777,0.0005678459,0.0008705867,0.000928674,0.000493784,0.6260829,0.36064,0.00007591731],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006666619,0.9624918,0.01004955,0.02094864,0.0004447974,0.00001369255,0.00002748904,0.00004894338,0.0053084],"genre_scores_gemma":[0.01386932,0.9690921,0.01233772,0.003445956,0.0005298316,0.00005666589,0.00004683676,0.00002845131,0.0005930396],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005986845,"threshold_uncertainty_score":0.03166181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1544697381748184,"score_gpt":0.4419161260583294,"score_spread":0.287446387883511,"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."}}