{"id":"W4414497836","doi":"10.31234/osf.io/c73ws_v2","title":"Modality-Specific Consolidation Shapes Long-Term Retention in Statistical Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Canadian Institutes of Health Research; Killam Trusts","keywords":"Consolidation (business); Statistical learning; Sensory system; Statistical hypothesis testing; Statistical analysis; Learning effect","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005326949,0.00017356,0.0003779165,0.0002438607,0.0001111454,0.0005634194,0.0003743661,0.0002710347,0.001752704],"category_scores_gemma":[0.002823832,0.000195453,0.000205602,0.0001227341,0.0004100626,0.0006934071,0.0007283866,0.0005489243,0.0002858971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001737026,"about_ca_system_score_gemma":0.0002537731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005037325,"about_ca_topic_score_gemma":0.0008959345,"domain_scores_codex":[0.9997079,0.00003421508,0.00002817762,0.0001114364,0.00006836294,0.00004995129],"domain_scores_gemma":[0.9986145,0.0002927414,0.0003721678,0.0002929783,0.0001963294,0.0002312747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00177396,0.0004626385,0.0513497,0.0001612063,0.0000853607,0.0001292579,0.0007141686,0.0005015685,0.8673886,0.0002626277,0.0002249455,0.07694593],"study_design_scores_gemma":[0.00004779217,0.006701797,0.7746207,0.00004382365,0.0001369866,0.000547715,0.0005089985,0.003937216,0.2107446,0.001404709,0.001268151,0.00003758953],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983772,0.0002333978,0.000928754,0.00001961011,0.000007537967,0.000007511838,0.00005330581,0.00002695483,0.0003457573],"genre_scores_gemma":[0.9987942,0.00008904748,0.0005357242,0.00001072014,0.000004898728,0.00001350332,0.00008138682,0.0000108672,0.0004596446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001752704,"threshold_uncertainty_score":0.005863369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02537903014380819,"score_gpt":0.2927338232392527,"score_spread":0.2673547930954445,"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."}}