{"id":"W4318263364","doi":"10.1080/13825585.2023.2170966","title":"Older adults can use memory for distinctive objects, but not distinctive scenes, to rescue associative memory deficits","year":2023,"lang":"en","type":"article","venue":"Aging Neuropsychology and Cognition","topic":"Memory and Neural Mechanisms","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; Baycrest Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Ministry of Economic Development and Innovation; James S. McDonnell Foundation","keywords":"Optimal distinctiveness theory; Psychology; Stimulus (psychology); Content-addressable memory; Cognitive neuroscience of visual object recognition; Recognition memory; Associative property; Cognitive psychology; Object (grammar); Episodic memory; False memory; Cognition; Recall; Artificial intelligence; Computer science; Neuroscience; Artificial neural network; Social psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003237745,0.0003148887,0.0003082268,0.0002289447,0.0006856003,0.00007326731,0.0001860465,0.0001434317,0.00002646247],"category_scores_gemma":[0.003048479,0.0003132451,0.0000844125,0.0005817503,0.0001672258,0.0002525421,0.000124367,0.0003501144,0.0001116381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003286661,"about_ca_system_score_gemma":0.00002689725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002163432,"about_ca_topic_score_gemma":0.00007669906,"domain_scores_codex":[0.9970758,0.0005753339,0.0003236374,0.001193643,0.0002348748,0.0005966919],"domain_scores_gemma":[0.9973922,0.00183617,0.0001656727,0.0002549392,0.0001576839,0.000193309],"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":[0.003788253,0.0003603588,0.0003869025,0.0001460468,0.00004743586,0.0006523334,0.008469282,0.0000221034,0.9202916,0.0006916369,0.001005428,0.06413864],"study_design_scores_gemma":[0.002730007,0.000834675,0.09566475,0.0002075182,0.0001071455,0.0001084169,0.001409422,0.0004688064,0.8963019,0.001518044,0.00004490529,0.0006044725],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938132,0.000006778148,0.0005388469,0.001304655,0.001469669,0.001262715,0.0005245641,0.000340667,0.0007388935],"genre_scores_gemma":[0.9892923,0.00002817763,0.00006589638,0.008773341,0.0001825529,0.0003505717,0.00007324682,0.00005473739,0.001179172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09527785,"threshold_uncertainty_score":0.999932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07341447307309439,"score_gpt":0.3212118139199335,"score_spread":0.2477973408468391,"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."}}