{"id":"W4405966708","doi":"10.1038/s41467-026-71428-6","title":"Binding items to contexts through conjunctive neural representations with the Method of Loci","year":2024,"lang":"en","type":"preprint","venue":"Nature Communications","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital","funders":"National Science Foundation","keywords":"Mnemonic; Prefrontal cortex; Schematic; Computer science; Schema (genetic algorithms); Representation (politics); Psychology; Cognitive psychology; Artificial intelligence; Cognition; Information retrieval; Neuroscience","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":[0.0005874618,0.000340685,0.000250037,0.0005221287,0.0002863527,0.00136357,0.001023335,0.0004493021,0.002593836],"category_scores_gemma":[0.003568192,0.0004087622,0.0005022489,0.0004433017,0.001555159,0.00335782,0.001700466,0.0009950176,0.0003412467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004666692,"about_ca_system_score_gemma":0.0003440836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0010759,"about_ca_topic_score_gemma":0.002186422,"domain_scores_codex":[0.9994795,0.0001126673,0.0000307614,0.0002475264,0.00009344993,0.0000362338],"domain_scores_gemma":[0.9990827,0.0002966913,0.0002101881,0.0003171089,0.00004753434,0.00004580847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005584128,0.0001517316,0.01954031,0.0005317508,0.0002554502,0.0006867777,0.003973362,0.009455698,0.5146213,0.07868406,0.001559237,0.3699819],"study_design_scores_gemma":[0.000175514,0.001262923,0.1176282,0.0002058036,0.0004065153,0.003759264,0.001891104,0.2854175,0.2588581,0.3016491,0.02848344,0.000262402],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.310493,0.0003752497,0.680092,0.0004371205,0.00005963051,0.0001468328,0.0002216462,0.0009856391,0.007188802],"genre_scores_gemma":[0.7260239,0.0001846025,0.2720486,0.00009224226,0.00002483598,0.00009596774,0.0001172112,0.00009444909,0.001318262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002593836,"threshold_uncertainty_score":0.008677304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1413027676877387,"score_gpt":0.4361824174184017,"score_spread":0.2948796497306629,"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."}}