{"id":"W810786515","doi":"","title":"A Computational Model of Memory, Attention, and Word Learning","year":2012,"lang":"en","type":"article","venue":"","topic":"Child and Animal Learning Development","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Forgetting; Novelty; Computer science; Word (group theory); Probabilistic logic; Cognitive psychology; Artificial intelligence; Computational model; Natural language processing; Word learning; Presentation (obstetrics); Psychology; Vocabulary; Linguistics; Social psychology","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":[0.000539163,0.000446976,0.0006895251,0.0005797382,0.0005276783,0.0012951,0.002765638,0.001315199,0.004119427],"category_scores_gemma":[0.002890015,0.0004738424,0.001067073,0.0008229528,0.001253757,0.003116498,0.0009647907,0.001348782,0.0005185923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131546,"about_ca_system_score_gemma":0.001110535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008310817,"about_ca_topic_score_gemma":0.005792896,"domain_scores_codex":[0.999733,0.00007501541,0.00001297041,0.00006514438,0.00006562506,0.00004812377],"domain_scores_gemma":[0.9987612,0.0008496587,0.00008717764,0.00009735706,0.0001364637,0.0000682251],"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":[0.0001052535,0.00009614628,0.001962641,0.00009034597,0.00006348757,0.0002381671,0.0002246897,0.5885893,0.001949909,0.3747682,0.001946819,0.0299651],"study_design_scores_gemma":[0.00001800473,0.00002327751,0.0003805621,0.000005416971,0.00001420469,0.00007588288,0.000009979579,0.8519157,0.0002019878,0.1464972,0.0008479988,0.000009708183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1759356,0.001216967,0.7950566,0.004294474,0.0001289523,0.00007265346,0.0007581419,0.0006180016,0.02191866],"genre_scores_gemma":[0.898948,0.0009420767,0.08686049,0.0002656027,0.0001658309,0.0002494105,0.0004169775,0.00006293606,0.0120887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008310817,"threshold_uncertainty_score":0.01652491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02673779888059535,"score_gpt":0.287193940086845,"score_spread":0.2604561412062497,"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."}}