{"id":"W2771542434","doi":"10.1016/j.neuroimage.2017.11.004","title":"How landmark suitability shapes recognition memory signals for objects in the medial temporal lobes","year":2017,"lang":"en","type":"article","venue":"NeuroImage","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Baycrest Hospital; University of Toronto; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Christoffel Blinden Mission","keywords":"Landmark; Perirhinal cortex; Recognition memory; Context (archaeology); Psychology; Voxel; Pattern recognition (psychology); Object (grammar); Artificial intelligence; Computer science; Cognitive psychology; Neuroscience; Cognition; Geography","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":[0.0005204954,0.000283166,0.0004091113,0.0004317322,0.0002811765,0.002463011,0.0007327114,0.0008492869,0.002824013],"category_scores_gemma":[0.002760422,0.0004306136,0.0004260356,0.00035715,0.00107584,0.001912574,0.0007090404,0.0007085196,0.0005492326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004177592,"about_ca_system_score_gemma":0.0004418144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001636579,"about_ca_topic_score_gemma":0.002145666,"domain_scores_codex":[0.999838,0.00002345951,0.00001050294,0.00004593811,0.00004205785,0.00003997168],"domain_scores_gemma":[0.9993331,0.0001880351,0.0002290347,0.0001198665,0.00005521178,0.0000747778],"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":[0.001609305,0.00008376288,0.02744887,0.0001582114,0.0001809318,0.0005318514,0.000681605,0.00333968,0.9130436,0.006250487,0.001269265,0.04540239],"study_design_scores_gemma":[0.0002205339,0.0007225256,0.6694148,0.00006146158,0.0003084815,0.002449589,0.001543405,0.03723376,0.2320523,0.05291067,0.002923195,0.000159301],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9891418,0.0002851477,0.006899341,0.000324813,0.00003882488,0.000006107378,0.0001329739,0.00008830221,0.003082637],"genre_scores_gemma":[0.9971952,0.0001193188,0.001324887,0.00005210396,0.00001356439,0.000005416794,0.0001152858,0.00008890763,0.00108519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002824013,"threshold_uncertainty_score":0.009447277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.192147517545686,"score_gpt":0.3418651462067754,"score_spread":0.1497176286610895,"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."}}