{"id":"W4410337375","doi":"10.3758/s13421-025-01727-8","title":"Using retrieval contingencies to understand memory integration and inference","year":2025,"lang":"en","type":"article","venue":"Memory & Cognition","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation","keywords":"Psychology; Inference; Cognitive psychology; Cognitive science; Cognition; Artificial intelligence; Neuroscience; Computer science","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":[0.001670788,0.0004218127,0.0005155634,0.0009143784,0.0005473971,0.002691951,0.001441391,0.0009357624,0.005833948],"category_scores_gemma":[0.006922367,0.0007290053,0.000794638,0.0004330049,0.002074378,0.008529616,0.001208034,0.002458676,0.0006029929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008628799,"about_ca_system_score_gemma":0.0007191614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001248874,"about_ca_topic_score_gemma":0.001076271,"domain_scores_codex":[0.9992626,0.000132535,0.00005979754,0.0002113557,0.0002346629,0.00009903011],"domain_scores_gemma":[0.9976941,0.001036143,0.0003071906,0.0006094731,0.0001881009,0.0001649475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0007494053,0.0007335605,0.01353778,0.0003211916,0.000363023,0.0007057202,0.001285355,0.02058774,0.08611658,0.7324892,0.001765758,0.1413446],"study_design_scores_gemma":[0.00006264705,0.0001994009,0.01911449,0.00005265027,0.0001126195,0.0005157761,0.000247136,0.07601988,0.01433828,0.8868113,0.002458765,0.00006710499],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5206789,0.001414117,0.4056717,0.002359624,0.0003837387,0.0001688866,0.0003430056,0.0007215619,0.06825851],"genre_scores_gemma":[0.9661474,0.0003318904,0.03108977,0.0001922584,0.00009094395,0.0000499628,0.0001556647,0.00008770818,0.001854263],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005833948,"threshold_uncertainty_score":0.01951653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.207761594766975,"score_gpt":0.3713400448843135,"score_spread":0.1635784501173385,"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."}}