Defining ‘Successful’ Episodic Memory Ageing: Implications of Methodological Heterogeneity

Defining ‘Successful’ Episodic Memory Ageing: Implications of Methodological Heterogeneity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Defining ‘Successful’ Episodic Memory Ageing: Implications of Methodological Heterogeneity Marta Garo-Pascual, Darya Frank, Cristina Ramponi, Bogdan Draganski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8916228/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Neuroscience & Biobehavioral Reviews → Version 1 posted You are reading this latest preprint version Abstract Episodic memory changes as we age ranging from marked decline in Alzheimer's disease to exceptional preservation in some older adults. Older individuals with episodic memory exceeding typical age-related performance are often termed ‘superagers’. Previous studies have used varying age ranges, reference benchmarks to distinguish successful from typical ageing or memory assessment tasks. Despite major advances in the field, the marked heterogeneity in defining superageing can hinder progress if not considered when interpreting results. We conducted a scoping review, using systematic searches of MEDLINE and Scopus, identifying 78 eligible studies. This review investigates the main sources of variability in superager definition across the following domains: age criteria, episodic memory criteria, and other cognitive criteria. We demonstrate how variation in each domain alters the composition of the target group and the implications for interpreting cognitive and neural mechanisms of superageing. Rather than establishing a hierarchy of definitions, we propose a conceptual framework to facilitate cross-study comparison and identify gaps in the literature. Understanding the implications of each selection criterion will enhance the interpretability of findings and accelerate insights into the superageing phenotype, ultimately contributing to better understanding of healthy episodic memory ageing. Cognitive Neuroscience superageing superager supernormal episodic memory ageing memory maintenance successful ageing Figures Figure 1 Figure 2 Figure 3 1. Introduction Episodic memory, which enables individuals to remember past experiences (Tulving 1972 ), is among the cognitive functions most vulnerable to ageing (Duarte and Kensinger 2019; Glisky 2007 ), with severe deterioration being a primary clinical hallmark of Alzheimer’s disease. However, over the past decade, growing evidence demonstrates that some individuals maintain optimal episodic memory functioning as they age, sometimes at levels comparable to much younger age. This group of older adults with superior episodic memory offers valuable insights into mechanisms for preventing or mitigating memory decline in healthy ageing and dementia. There is considerable heterogeneity in how successful episodic memory ageing is defined across studies, challenging interpretation and comparison. This review investigates the sources of this definitional heterogeneity and evaluates their implications for interpreting and comparing study findings. We aim to outline a framework for understanding the cognitive and neural underpinnings of successful episodic memory ageing. Various terms describe older adults with superior episodic memory, including ‘superagers’ (Dang et al. 2019a ; Garo-Pascual et al. 2023 ; Gefen et al. 2015 ; Harrison et al. 2012 ), ‘supernormals’ (Lin et al. 2017a ; Wang et al. 2019 ), ‘successful agers’ (Pudas et al. 2013 ), ‘optimal memory performers’ (Dekhtyar et al. 2017 ), among others. Despite this terminological diversity, most definitions converge on three core domains: chronological age, episodic memory performance, and other (non-memory) cognitive abilities. However, these criteria are operationalised in markedly different ways, resulting in substantial heterogeneity across studies. Groups designated by the same category label may vary considerably across different studies. The term superager therefore does not reflect a uniform definition. For clarity and consistency, this review uses the terms superager and superageing as convenient references for the broader concept of successful episodic memory ageing. The impact of definitional variations has been addressed by examining sample age (Rogalski 2019 ), episodic memory test type (Touroutoglou et al. 2023 ), and longitudinal trajectories of memory (Nyberg 2024 ). A recent systematic review evaluated superageing assessment, providing quantitative analysis of commonly used classifications (Andrade et al. 2023 ), while another highlighted definitional variability and its interpretive implications (de Godoy et al. 2021b ). The present scoping review builds on this work by investigating multiple sources of variation in selection criteria, providing an integrated perspective on how such variability shapes the interpretation of findings. Although framed as a scoping review, we conducted a systematic literature search to ensure comprehensive field coverage and accurate assessment of its heterogeneity. First, we address age criteria for sample selection. Second, we investigate the episodic memory domain, where greatest variation occurs, including benchmarks distinguishing successful from typical ageing, memory tests used, and longitudinal trajectories incorporated into definitions. Third, we assess whether different non-memory cognitive abilities and related test variability introduce distinct selection biases. Finally, we denote the extent to which current literature reflects diversity in cognitive abilities. Rather than imposing a hierarchy of definitions, we propose a conceptual framework enabling clearer field navigation, facilitating detection of cross-study comparability or complementarity, and revealing gaps in the literature. 2. Search strategy and selection criteria Searches were conducted in MEDLINE (via Ovid) and Scopus from inception to March 2025. Search terms encompassed three main concepts: i. superior or successful memory/cognitive ageing; ii. memory maintenance, and iii. the “superager” construct and related terms including “supernormal”. The full search strategy is provided in Supplementary Table 1 . Reference lists of relevant studies and review articles were screened, yielding four additional records not captured by database searches. All records were exported into Rayyan software (Ouzzani et al. 2016 ) for deduplication and screening. Two authors (CR and MGP) independently screened titles and abstracts, followed by independent full-text assessment (DF and MGP). Discrepancies were resolved through discussion, with third author consultation when necessary. Studies were eligible if they met the following criteria: i. original research published in English; ii. human ageing population; and iii. identification of a group with episodic memory performance beyond typical ageing as part of inclusion criteria. In total, 78 studies met the inclusion criteria. Reasons for exclusion are detailed in Fig. 1 . 3. Age domain While chronological age is inherently tied to ageing-associated episodic memory maintenance and decline, the most appropriate age range for studying superageing remains debated. Although ageing is a continuous process without universal consensus on when old age begins (Shenkin et al. 2017 ), studies of superageing often require establishing a temporal boundary. The United Nations defines an old person as someone aged 60 years and above (UNHCR 2025 ), and the World Health Organisation similarly refers to populations over 60 in its ageing reports (WHO 2025 ). In contrast, the National Institute on Aging generally uses “older adults” for individuals aged 65 years and above (NIH 2025 ). These cut-offs are shaped more by historical and social factors, notably pension eligibility (Costa 1998 ), than by biology. Nonetheless, they align with evidence indicating episodic memory stability until the sixth decade, with decline typically beginning between 60 and 65 years (Nyberg 2017 ; Rönnlund et al. 2005 ; Schaie 2005 ). Across the 78 studies reviewed, 34 (44%) examined individuals aged 80+, 25 (32%) of those aged 60+, and 19 (24%) included cohorts younger than 60. Most of the latter (14 out of 19) were longitudinal assessments, enabling causal inferences (Table 1 ; Fig. 2 ). The focus on those aged 60 + reflects both onset of memory decline and proximity to retirement age (Sun et al. 2016 ). In contrast, studies of the 80 + population better capture the cumulative age-related changes (e.g., brain atrophy) that emerge after longer exposure to biological and social risk factors (Rogalski 2019 ). Episodic memory decline follows a non-linear trajectory with age (Nyberg et al. 2012 ; Rönnlund et al. 2005 ), with distinct patterns observed in individuals aged 60–80 versus those beyond 80 (Rogalski 2019 ). Brain structure shows slower grey matter atrophy rate in superagers over 80 compared with typical peers (Cook et al. 2017 ; Garo-Pascual et al. 2023 ), whereas no significant group differences emerge in the 60–80 range (Dang et al. 2019b ). These findings should therefore be viewed as complementary, reflecting distinct windows of superageing rather than directly comparable trajectories. Comparing studies by chronological age alone is sometimes sufficient to assess whether they examine comparable time windows. However, when populations differ substantially in life expectancy, retirement age, or sociodemographic background, groups of the same chronological age may not correspond in their stage of ageing (Balachandran et al. 2024 ; Sudharsanan and Bloom 2018 ). To account for such variability, some authors propose adjusting age cut-offs – e.g., a five-year shift when comparing individuals over 80 in developed countries with those over 75 in developing countries (Borelli et al. 2018 ). These considerations highlight the importance of accounting for population characteristics when interpreting ageing trajectories. 4. Episodic Memory Benchmark 4.1. Boundary between successful and typical episodic memory ageing Episodic memory ageing phenotypes likely exist on a continuum rather than as discrete categories. Therefore, classifying individuals as superagers or typical agers has limitations: i. benchmarks are often arbitrary and vary across studies, obscuring straightforward interpretation; ii. categorical approaches are statistically less sensitive; and iii. they oversimplify within-group variability, excluding borderline or atypical cases. Despite these drawbacks, delineating a superager category offers advantages. The phenotype is multidimensional by definition, integrating episodic memory performance with other non-memory measures ( see Section 7 ) and longitudinal trajectories ( see Section 6 ). Incorporating these multiple dimensions into a continuous model is not straightforward; although dimensionality-reduction methods could be applied, their outputs may be less interpretable than categorical definitions. The superager construct by design simplifies a complex, dynamic and multifactorial phenomenon. In this emerging research field, such simplification provides an interpretable framework for understanding the mechanisms underlying well-preserved episodic memory in later life. 4.2. Approaches to defining the boundary between successful and typical episodic memory ageing: benefits, limitations and implications This section reviews approaches used in the superageing literature to define superager groups compared with typical older adults. Methods vary considerably and fall into three main categories: i. approaches using a younger reference group; ii. those relying on an age-peer reference, and iii. those using automated classification tools. The younger reference approach defines superagers by comparing their performance to normative values from young or middle-aged adults. This method underpins the original superager concept – episodic memory in older adults equal to that of healthy adults 20–30 years younger (Cook et al. 2017 ; Garo-Pascual et al. 2023 ; Gefen et al. 2015 ; Harrison et al. 2012 ; 2018 ; Sun et al. 2016 ; Wang et al. 2019 ; Zhang et al. 2020 ). It is the most common strategy, adopted in 68% (53 out of 78) of the reviewed studies (Table 1 ; Fig. 2 ), although the younger reference group’s age varies considerably. For studies setting the superager cut-off at 80+, the most common benchmark is adults aged 50–60. For those defining superagers at 60+, the benchmark ranges from young adults (16–29 years) to those aged 45. There is some correspondence between studies using middle-aged references group for individuals over 80 and those using younger adult references for cohorts aged 60–80. Authors using both approaches have reported greater cingulate gyrus cortical thickness in superagers compared with typically ageing adults (Gefen et al. 2015 ; Sun et al. 2016 ). However, this was not replicated in a larger cohort of superagers over 80 using the same middle-aged reference group (Garo-Pascual et al. 2023 ). Other studies adjust norms for education, sex, or ethnicity, although such adjustments are inconsistently applied. Normative values are typically derived from a distinct cohort, as study cohorts usually include only older adults. However, there are studies drawing younger references from the same cohort (Doyle et al. 2024 ; Maccora et al. 2021 ; Trammell et al. 2024 ), ensuring closer comparability. In other cases, reference groups differ markedly in sociodemographic background – e.g., when US standards were applied to Brazilian (de Godoy et al. 2023 ; 2021b ) or Indian (Batra et al. 2024 ), or Chinese populations (Jia et al. 2022 ). Despite this variability, using a younger reference group strengthens validity by avoiding potential biases within the study cohort. The caveat is the lack of directly comparable normative data matched for sociodemographic factors or testing protocols. Even when comparability is achieved, reference value reliability depends on sufficient sample size. Unlike the younger reference method, the age-peer approach benchmarks performance against an elderly population of equivalent age, derived either from the same study cohort of the targeted sample or an external reference dataset (Table 1 ; Fig. 2 ). Superagers are identified by exceeding the cohort mean on cognitive tests, typically by 1-1.5 standard deviations (de Souza et al. 2022 ; Josefsson et al. 2012 ; Lin et al. 2017a ), although some studies use intermediate thresholds (e.g., 1.25 or 1.35 standard deviations) (Hoenig et al. 2020 ; Mapstone et al. 2017 ) or select top performers from the 50th or 20th percentile (Dekhtyar et al. 2017 ; Dominguez et al. 2021 ). This method is advantageous when external normative data are unavailable or poorly matched to the study population. However, reliance on age-peer benchmarks introduces susceptibility to cohort-specific biases, limiting external validity and making cross-study replication more challenging. Automatic classification uses various strategies to define superagers, including graph theory approaches, principal component analysis, and finite mixture models such as latent class analysis, latent transition analysis, or latent trajectory modelling. Despite methodological diversity, these approaches share two features. First, reliance on multiple cognitive tests beyond episodic memory assessment identifying superagers who excel in other cognitive domains (Baran and Lin 2018; Saliasi et al. 2015 ). Second, most approaches input cognitive trajectories over time, incorporating a longitudinal perspective into superager definitions (Lin et al. 2017b ; Mohammadiarvejeh et al. 2024 ; Zammit et al. 2020 ) (Table 1 ; Fig. 2 ). The main advantage is independence from arbitrary thresholds for distinguishing superagers from typical agers. Drawbacks include limited control over the group composition and greater interpretive complexity, as episodic memory scores may be combined with non-memory measures. When multiple domains contribute to classification, it is unclear whether all identified individuals exhibit superior episodic memory. Thus, automatic classification may be better suited to studying general cognitive functioning than episodic memory specifically. Finally, because these methods lack external references, group composition is inherently shaped by study sample biases. Limited overlap exists between studies with similar aims that define superagers with different benchmark approaches, although some studies with comparable objectives have reported consistent findings. For example, whole-brain amyloid burden measured with positron emission tomography shows no differences between superagers and typical older adults across studies using a younger reference group (Dang et al. 2019b ; Harrison et al. 2018 ) an age-peer reference (Dekhtyar et al. 2017 ), or an automatic approach (Baran and Lin 2018), suggesting that different approaches may capture similar effects. 4.3. Considerations for episodic memory benchmarks Defining the episodic memory benchmark to distinguish successful from typical ageing is a stepwise process that should be integrated into the study design. This may begin with determining whether to use non-arbitrary cut-offs derived from automatic methods, or to apply an informed, albeit arbitrary, threshold. When opting for an arbitrary threshold, a common strategy is to reference the mean performance of a younger population on the same test. The suitability of such external references must be evaluated relative to the study cohort, particularly regarding test administration protocols and sociodemographic characteristics. Sample size differences also warrant consideration, especially when the normative cohort is substantially smaller than the study sample. Where external references are unavailable or unsuitable, thresholds based on the age-peer distribution of the study cohort represent a valid alternative, although they will inevitably reflect cohort-specific biases. Consequently, whenever possible, validating the distribution of episodic memory scores in the target cohort against comparable samples is essential. Such validation ensures the chosen threshold retains relevance beyond the immediate study population, enhancing the generalisability of findings. 5. Episodic Memory Assessment Superageing is predominantly defined by episodic memory performance, yet the specific tests used vary across studies and cohorts (de Godoy et al. 2021b ; Nyberg and Pudas 2019) ( Table 1 ; Fig. 2 ) . Episodic memory is a complex, multi-faceted process, relying on coordinated function of multiple neural systems, from initial encoding through consolidation, to retrieval. Therefore, test selection can influence superageing inclusion criteria. Performance may be driven by differential engagement of specific sub-components (e.g. encoding or retrieval demands), or task characteristics (e.g. perceptual or semantic processing). This methodological heterogeneity has critical implications, as it may explain conflicting findings regarding prevalence and neural underpinnings. More fundamentally, it raises the question of whether superageing is a unitary phenomenon or multiple distinct patterns of preserved cognitive function. This section reviews how episodic memory has been assessed across different studies, focusing on how test selection contributes to heterogeneity in the literature, and proposes considerations for advancing mechanistic understanding. 5.1. Verbal and visuospatial tests Verbal learning tests are the most common neuropsychological assessment in the superageing literature, demonstrating an imbalance with visuospatial tests. This bias likely reflects practical considerations – verbal tests are often easier to standardise, administer, and score, but has important theoretical implications. Performance on verbal and visuospatial memory tests shows different ageing trajectories. Some studies show that visuospatial memory declines more steeply with age than verbal memory (Murre et al. 2013 ; Park et al. 2002 ), while others find the opposite (Liampas et al. 2023 ). These differences may partly reflect gender-related patterns, as previous work suggests women show relative advantages on verbal episodic memory tasks (Gale et al. 2007 , 20; Herlitz et al. 1999 ; Murre et al. 2013 ). While effect sizes are typically small to moderate, the superageing literature provides some support for this gender-difference, with proportionally more women categorised as superagers using verbal memory tests (Maccora et al. 2021 ; McPhee et al. 2025 ). Additionally, verbal dominance in assessment may overlook individuals who maintain superior non-verbal memory despite age-typical verbal decline – a pattern that could represent a distinct form of resilience. This bias may disadvantage individuals from different linguistic or educational backgrounds, as verbal tests often draw on accumulated semantic knowledge and language-specific strategies (Lim et al. 2009 ). While visuospatial memory tests are not culture-free (Rosselli and Ardila 2003), they may reduce some language and educational confounds. As most studies also include non-memory measures ( see Section 7 ), these different functions can be directly compared. Several studies using verbal tasks to categorise superagers and typical older adults found no significant differences in language and visuospatial function (excluding episodic memory) between groups (Cook Maher et al. 2022 ; Karpouzian-Rogers et al. 2023 ). In one cohort (Katsumi et al. 2021 ; Zhang et al. 2020 ) researchers examined visual-verbal association in superagers and typical older adults categorised based on California Verbal Learning Test (CVLT) performance. Superagers performed better on the visual-verbal item recognition task and marginally better on the association task. This suggests superior episodic memory performance of superagers extends beyond verbal memory tests (Pudas et al. 2013 ). To further delineate this finding, directly comparing performance on verbal and visuospatial memory tests when categorising superagers would be valuable. While one cohort incorporated visuospatial memory tests in the categorisation stage (Kim et al. 2020 ; 2024 ), superagers in this cohort also showed higher non-memory visuospatial function, contrasting with previous findings (Cook Maher et al. 2022 ; Karpouzian-Rogers et al. 2023 ). These contradictory findings demonstrate the importance of episodic memory test selection for categorising superagers, and its implications for phenotyping this population. It is also worth considering the neural underpinnings supporting performance on verbal and visuospatial memory tests. While verbal memory functions are generally associated with left hemispheric dominance, no such laterality has been observed in relation to brain anatomy (e.g., hippocampal volume and cingulate cortical thickness) (Garo-Pascual et al. 2023 ; Pezzoli et al. 2024 ; Sun et al. 2016 ). It is possible that lateralisation was not captured by structural measures but exists functionally. Although several studies examined functional connectivity using resting-state magnetic resonance imaging (fMRI) (de Godoy et al. 2023 ; Diamond et al. 2024 ; Zhang et al. 2020 ), this approach may not be best suited to examine laterality with the exception of one study reporting left-dominant functional connectivity within the cholinergic system (Jia et al. 2022 ). To fully address this possibility, task-based fMRI using both types of memory tests would be needed. Alternatively, the lack of laterality effects could reflect a unitary superior episodic memory capacity that transcends domain-specific processes, whereby superageing captures a global advantage even when assessments primarily index the verbal domain. 5.2. Measurements: retrieval tests and single vs composite scores Another source of heterogeneity, even within verbal-memory tests, is the retrieval task employed: immediate free or cued recall, delayed free or cued recall, and recognition. Additionally, some studies combine these tests to create composite scores. Like the verbal-visuospatial memory distinction, the retrieval format profoundly shapes which cognitive and neural processes are assessed, potentially leading to distinct superager phenotypes. In our review sample, over half the studies (41 out of 78) rely exclusively on delayed free recall scores, which place high demands on encoding and self-initiated retrieval mediated by hippocampal-frontal-parietal engagement (Baldo and Shimamura 2002 ). These neural systems align with the structural preservation reported in superager cohorts identified using free recall tests (Keenan et al. 2024 ; Sun et al. 2016 ). Nevertheless, it is important to note performance on free recall tests can also be influenced by other factors, that might not affect recognition to the same extent (e.g. mood disorders, vascular changes, attention and executive function impairments). Recognition tests minimise retrieval demands and can be supported by either hippocampal-dependent recollection or medial temporal lobe-dependent familiarity processes (Montaldi and Mayes 2010 ; Yonelinas 2002 ). Given that recall is more sensitive to ageing than recognition (Rhodes et al. 2019 ), as well as cued recall and associate-recognition tasks (Lowndes et al. 2008 ), these formats likely capture partially independent memory abilities and/or processes (Healey and Kahana 2016). As such, recognition is often used as part of a composite score to categorise superagers (Dominguez et al. 2024 ; Mapstone et al. 2017 ; Pezzoli et al. 2025 ; Zammit et al. 2021 ). In most cases, performance is aggregated across different tests – recognition and recall, hindering our ability to examine different performance patterns across the two measurements. Despite this issue, composite scores are quite common, used by 31 of the 78 articles identified in this review. Most often they combine multiple memory measures, e.g. immediate and delayed recall or free and cued recall (Dekhtyar et al. 2017 ; Josefsson et al. 2012 ), but have also been used to combine measures across cognitive domains (Hermansen et al. 2024 ; Yu et al. 2020 , 20; Uribe-Kirby et al. 2025 ). Composite measures can provide greater statistical reliability by reducing measurement error inherent in single tests (Kane and Case 2004), and may capture a general episodic memory factor that surpasses specific task demands (Jonaitis et al. 2019 ), providing a more holistic picture of superageing. An individual maintaining superior performance across word lists, paragraph recall, and visual memory tests likely represents a more robust superageing phenotype than a person excelling at only one format. Composite scores also address the ceiling effects that can occur in high-functioning older adults on individual tests, providing greater discriminative power in the superior performance range. Moreover, composite scores better reflect real-world memory demands, which rarely involve pure word-list learning but rather integrate verbal, visual, and contextual information (Tulving 2002 ). However, these benefits need to be weighed against the loss of mechanistic specificity; composite scores can obscure whether superior performance reflects globally preserved memory systems or compensatory strengths in specific domains masking deficits in others. Relatedly, they could be skewed by different age-related trajectories across cognitive functions, such as the steeper decline in episodic memory compared to other domains and between individuals (Lin et al. 2017b ; Pudas et al. 2013 ; Wu et al. 2020 ). Composite score use could also reflect convenience or availability rather than active choice, such as in large-scale cohorts like UK Biobank or Alzheimer’s Disease Neuroimaging Initiative (ADNI), which are not specifically designed to study superageing. The ADNI episodic memory composite score (Crane et al. 2012 ) combines RAVLT (trials 1–5, 30-minute delayed recall, and recognition), Logical Memory (immediate and delayed), and select items from the ADAS-Cog. This exemplifies a composite score mixing retrieval formats and cognitive domains, which could potentially illuminate differential performance if sub-scales are provided. Therefore, where possible, studies should report both composite scores and individual components, allowing the field to determine whether superageing represents uniform preservation across all episodic memory processes or selective resilience in specific domains. 5.3. Organisation of information used The internal structure of to-be-remembered material – whether semantically organised or unrelated –, places different demands on encoding strategies, executive control, and semantic processing abilities, thereby altering which cognitive processes drive superior performance. The most commonly used test, the RAVLT (almost a third of studies in our review; 21 out of 78 used it as the sole criterion), consists of 15 unrelated words that do not permit traditional semantic clustering strategies, although participants may employ alternative organisational approaches such as serial clustering or individually devised strategies (Baldo and Shimamura 2002 ). In contrast, tests like the CVLT and HVLT present words from distinct semantic categories (e.g., fruits, tools, animals), explicitly enabling semantic clustering strategies during encoding and retrieval (Sun et al. 2016 ). The Free and Cued Selective Reminding Test (FCSRT) takes this approach further by providing semantic category cues during the learning phase, promoting reliance on semantic processing and cue-target associations during encoding (Baldo and Shimamura 2002 ). Tasks like the FCSRT therefore leverage controlled encoding and semantic cues to distinguish genuine storage deficits from retrieval failures, probing hippocampal-dependent associative binding. These differences modulate task cognitive demands, potentially identifying superagers who excel through different underlying mechanisms. When tested on semantically organised material (e.g., CVLT), superagers showed significantly higher semantic clustering scores than typical older adults, and matched young adults, indicating greater use of executive control strategies leveraging semantic relationships among words (Sun et al. 2016 ; Tremont et al. 2000 ). This contrasts with findings from RAVLT-based studies, where executive function and attention composite scores predict approximately 20% of variance in superager episodic memory performance (Cook Maher et al. 2022 ). The reliance on executive control for successful performance might be driven by the RAVLT's unrelated word structure, which precludes semantic strategies, and forces superior performers to develop alternative organisational approaches (Baldo and Shimamura 2002 ). This leads to the final organisational difference: standalone words versus short stories. While most studies employing verbal learning tests use word lists, others use paragraph recall tasks such as Logical Memory from the Wechsler Memory Scales (Baran and Lin 2018; Harrison et al. 2024 ; Saloner et al. 2019 ). Story-based tests provide narrative structure, semantic coherence, and contextual relationships that fundamentally differ from word list learning (Tremont et al. 2000 ). Stories enable participants to leverage discourse comprehension, existing schemas, and meaningful connections between ideas – cognitive processes that may be less vulnerable to ageing or represent different aspects of preserved function. Consequently, individuals meeting superager criteria through superior story recall might rely on intact language comprehension and narrative processing abilities, while those excelling at word lists might demonstrate superior associative binding or strategic organisational skills. Combined, these findings suggest that semantically organised tests may identify a phenotypic feature of superageing reflecting excellence in strategic encoding processes, while unrelated word lists may capture individuals with superior raw binding capacity or ability to generate organisational strategies. An individual meeting superager criterion through superior semantic clustering on the CVLT might show average performance level on the RAVLT if their advantage is derived from strategic semantic processing. Conversely, a superager identified based on RAVLT performance might not leverage semantic structure effectively on category-based tests, potentially reflecting qualitatively different cognitive mechanisms underlying superageing. 5.4. Considerations for episodic memory assessment A significant step forward for superageing research would be establishing a consistent operational definition that enables meaningful comparison while preserving the ability to capture qualitatively different forms of successful ageing. One potential approach is establishing a multi-format assessment battery that includes both verbal and visuospatial memory tests and different retrieval formats. Scoring should involve individual components (for normative comparisons) and composite scores, allowing researchers to determine whether superior performance reflects globally preserved memory systems or selective strengths masking specific deficits. Additionally, future research could incorporate process-level analyses to understand how superagers maintain superior memory function. While standardised neuropsychological tests are needed for normative data, they could be complemented with experimental paradigms that directly probe underlying cognitive processes. For example, process dissociation or remember/know procedures could distinguish recollection-based from familiarity-based recognition advantages, while source memory (Josefsson et al. 2012 ; Dennis et al. 2008 ), mnemonic similarity discrimination (Stark et al. 2019 ; Frank et al. 2020), and associative binding (Yonelinas 2013 ) tasks could isolate hippocampal-dependent functions from broader strategic abilities. Importantly, experimental paradigms could also extend brain imaging studies beyond brain-behaviour correlations by introducing cognitive manipulations that enable investigation of neural mechanisms. For example, task-based neuroimaging using different memory processes could reveal whether superagers show distinct patterns of neural recruitment (Katsumi et al. 2021 ). Nevertheless, multi-format assessment batteries incorporating experimental paradigms beyond standard neuropsychological tasks are rarely available for existing cohorts studying superageing. Consequently, the episodic memory measures employed in the current literature remain informative for understanding cognitive mechanisms. However, as highlighted in this section, careful consideration is required when interpreting and comparing findings, as each approach carries distinct strengths and limitations. 6. Longitudinal Trajectory of Episodic Memory Research on successful episodic memory ageing can be divided into studies that incorporate longitudinal trajectory of episodic memory into their definition and those that do not. Of the 78 studies reviewed, approximately 70% (54 studies) employed a cross-sectional design, while the remaining 30% (24 studies) adopted a longitudinal approach (Table 1 ; Fig. 2 ). Among cross-sectional studies, about half set an age cut-off at 80 years or older (Borelli et al. 2021 ; Calandri et al. 2020 ; Harrison et al. 2012 ), while the remainder included participants below this threshold (Park et al. 2025 ; Sun et al. 2016 ; Yu et al. 2020 ). In contrast, only a minority of longitudinal studies (7 out of 24) focused on participants over 80 years (Cook et al. 2017 ; Garo-Pascual et al. 2023 ; Hoenig et al. 2020 ), with the majority examining younger samples (Baran and Lin 2018; Gardener et al. 2021 ; Pudas et al. 2013 ). A key consideration in longitudinal designs is the duration of follow-up, which ranges from 18 months (Cook Maher et al. 2022 ) to two decades (Josefsson et al. 2012 ). The choice between cross-sectional and longitudinal designs in selection criteria is critical for conceptualising and interpreting mechanisms of successful episodic memory ageing. As Nyberg ( 2024 ) notes, a high episodic memory score at a single time point does not necessarily reflect preserved function over time. Older adults with strong baseline performance may follow divergent trajectories, either sustained high performance into advanced age or subsequent decline. This variability is evident in studies where cross-sectionally defined superagers did not always maintain their status at follow-up. Reported proportions of stable superagers range widely, from 10.9% over three years in a cohort defined at ≥ 80 years (Cervenkova et al. 2020) to 60% over three years in a cohort defined at ≥ 75 years (Dekhtyar et al. 2017 ). Such discrepancies underscore limited understanding of factors supporting the maintenance of episodic memory in ageing. Cross-sectional definitions assume that high episodic memory performance at a single time point suffices to identify superagers. This approach is sensitive to age cut-offs and may include individuals with high cognitive reserve who are already undergoing preclinical dementia, characterised by delayed yet rapid cognitive decline (Stern 2012 ; Stern et al. 1999 ). When criteria are applied close to the age at which episodic memory decline typically begins, cross-sectional studies are more likely to capture individuals who had inherently high episodic memory capacity. By contrast, longitudinal designs that track performance across the ageing process are better suited to identify those resistant to age-related decline. However, cross-sectional studies remain valuable for efficiently characterising neural signatures, genetic profiles, and cognitive mechanisms associated with different superager phenotypes. They also enable systematic comparison of assessment approaches to identify overlapping versus distinct cognitive profiles, providing essential foundational knowledge for designing targeted longitudinal investigations. Adopting a longitudinal perspective in defining successful episodic memory ageing raises important considerations about the heterogeneity of identified populations. Cross-sectional definitions – especially when applied to younger cohorts of older adults – are more likely to capture heterogeneous groups, encompassing individuals who may or may not maintain high performance over time. While no consensus exists on optimal longitudinal follow-up duration, longer observation periods provide stronger evidence of stability and reduce interpretative uncertainty. This does not negate the value of cross-sectional approaches, which can shed light on why some high performers subsequently decline while others maintain optimal episodic memory function with age. 7. Non-memory Assessment Although episodic memory is the core neuropsychological criterion in defining superagers, and the main source of heterogeneity ( see Sections 4–6 ), an important question arises regarding other cognitive abilities: should these be expected to fall within the normal range or also be supernormal? Some studies do not consider performance in other cognitive domains beyond demonstrating the lack of pathology by reaching thresholds on standard cognitive tests used as a screening tool for cognitive decline, e.g. MMSE (Maccora et al. 2021 ) (Table 1 ; Fig. 2 ). Others require superagers to perform within the normal range for their age in cognitive tasks assessing other cognitive domains, such as attention, processing speed, or executive function (Harrison et al. 2012 ), while some include participants who perform within or above the normal range for their age (Gefen et al. 2015 ). Another approach includes participants that happen to be higher performers in other cognitive domains (Baran and Lin 2018). To complicate matters further, some studies (Maccora et al. 2021 ) include maintenance of superior cognition across time as part of the definition, adding a longitudinal dimension to the criteria. This section addresses the variability in including additional cognitive domains in the superager definition and its implications. 7.1. Cognitive architecture in superageing The choice of non-memory cognitive criteria to define superageing is far from trivial, as it reflects an underlying theoretical stance on cognitive architecture – particularly the role of general cognitive ability, or the g factor. Performance on a broad range of cognitive tests tends to correlate, and this shared variance is commonly attributed to the g factor (Spearman 1904 ). The g factor, thought to capture an individual’s general capacity to reason, learn, and adapt across diverse tasks, accounts for a substantial proportion of variance in cognitive performance. Its relevance for studying superageing lies in the fact that episodic memory is itself moderately to strongly correlated with g , and age-related declines in episodic memory are frequently paralleled by declines in other g -related domains such as processing speed and executive function (Glisky et al. 2022 ; Harada et al. 2013 ; Hertzog et al. 2003 ; Schwarz et al. 2024 ; Tucker-Drob et al. 2019 ; Crawford 2000 ; Zaninotto et al. 2018; Ghisletta et al. 2012 ). Thus, whether superior memory performance in older adults reflects domain-specific preservation of episodic memory systems, or broader resilience of general cognitive ability (Cook Maher et al. 2022 ), remains a central question for understanding the cognitive architecture of superageing. Underlying the g factor framework is the assumption that cognitive abilities are fundamentally interrelated and draw on shared resources, reflecting a unitary component of intelligence. As outlined in Section 5.3 , basic processes such as attention are core components of memory. The relationship between attention and memory is complex, as attentional control not only affects encoding efficiency but also interacts with other cognitive processes, such as working memory and inhibitory control, to influence memory outcomes (Cowan et al. 2024 ; Long et al. 2018; Sherman et al. 2019 ). Another fundamental component is processing speed, often measured using tasks such as the Trail Making Test or digit-symbol substitution. Processing speed constrains the efficiency of both encoding and retrieval (Luo and Craik 2008; Salthouse 1996 ) and age-related slowing can disproportionately affect memory performance (Lee et al. 2012 ; Levitt et al. 2006). Alongside processing speed, working memory, which provides a workspace for temporarily maintaining and manipulating information, is strongly associated with individual differences in episodic memory in ageing (Korkki et al. 2023 ), including with the preserved performance seen in superagers (Cook Maher et al. 2022 ; Nyberg et al. 2012 ). In contrast to this assumption of interdependence, modular accounts of cognition (Fodor 1983 ) propose that the cognitive architecture consists of specialised modules that operate relatively independently. Each module is dedicated to processing a particular type of information, thus supporting cognitive flexibility and allowing the preservation of function in the event of localised brain injury. This has implications for how we conceptualise superageing and interpret its underlying neural structures. If we consider functions as particularly modular, then each function, and the brain regions supporting it may follow different developmental and decline trajectories from other functions. Therefore, superagers may be better defined as excelling in one function, like episodic memory, as the other functions may show differential decline. If episodic memory is treated as an isolated function, the focus tends to fall narrowly on medial temporal lobe regions, particularly the hippocampus. By contrast, when episodic memory is viewed in relation to its component processes such as attentional control, working memory, or processing speed, a wider set of brain regions becomes relevant (Moscovitch et al. 2016 ), highlighting broader, network-level contributions. These definitional choices therefore shape the neural ‘signatures’ that emerge. How superageing is defined therefore implicitly aligns with one or the other of these assumptions, privileging either a domain-general account of preserved cognition or a domain-specific, modular interpretation, with the unintended consequence of shaping and constraining the interpretation of study findings. 7.2. Considerations for non-memory assessments As suggested in Section 5.4 , to understand the basis of superior memory in superagers, studies would benefit from incorporating process-level analyses. These analyses are designed to capture the strategies and intermediate mechanisms underlying episodic memory performance, such as monitoring learning curves across repeated trials, identifying error patterns (e.g., intrusions or false alarms), or measuring reliance on recollection versus familiarity. However, in the frequent case where episodic memory cannot be studied with specialised experimental paradigms (as in big cohort studies), information from performance on other standardised cognitive tasks becomes invaluable. Standardised neuropsychological measures of core functions such as attention, processing speed, and working memory enable the identification of specific cognitive, and possibly neural, mechanisms that support preserved memory in ageing. Ultimately, this strategy may help explain why some older adults maintain exceptional memory performance. An additional consideration concerns how cognitive abilities beyond episodic memory are characterised over time. Do they follow a similar course to episodic memory, or are there distinct trajectories for each domain (Hartshorne and Germine 2015 ; Whitley et al. 2016 )? Tracking changes across multiple domains allows researchers to determine whether episodic memory preservation occurs in isolation or as part of a more general pattern of cognitive resilience. Integrating these considerations – episodic memory, other cognitive skills, longitudinal trajectories, and reference group selection, is critical for defining superagers in a way that is both conceptually meaningful and methodologically rigorous. This methodological variability complicates cross-study comparisons and may underlie apparent inconsistencies in the literature. There are also practical reasons to foreground other cognitive functions in studying superageing. The field is largely motivated by the aim of identifying mechanisms of resistance to Alzheimer’s disease, which has positioned episodic memory as a central focus. However, this emphasis may be too narrow, as decline is observed in other cognitive functions like executive function (Dubbelman et al. 2024 ). Restricting assessment to episodic memory may overlook broader patterns of cognitive preservation or vulnerability that are relevant for understanding resistance to neurodegenerative pathology. Tasks assessing additional cognitive domains can provide valuable complementary information. By broadening the scope of cognitive assessment in superageing research, studies can more accurately characterise the cognitive profile associated with resistance to Alzheimer disease and offer insights into how cognitive health could be maintained. 8. Diversity in Ageing Populations This section examines the extent to which the superageing literature reflects the global ageing landscape and its diversity. Geographically, according to the United Nations World Population Prospects, the global population aged over 65 in 2023 was estimated at 808.37 million, with Asia representing the largest proportion (469.40 million, over 58% of the total; Fig. 3 A) (Ritchie and Roser 2019). In contrast, 62% (48 out of 78) of cohorts studied in this field were from the United States (Table 1 , Fig. 3 B), highlighting a clear geographical bias. Population diversity, however, extends beyond geographical bias. Even within highly represented countries, study samples tend to be skewed toward highly educated, urban, and generally healthy individuals, frequently excluding social or ethnic minorities. For instance, according to the United Nations, the average number of years of schooling among adults aged over 25 years in the United States in 2023 was 13.9 (UNDP (United Nations Development Programme) 2025 ). In contrast, in the majority of studies reviewed based on United States cohorts, average education levels were higher than the general population. Notably, at least 25% of these studies involved highly educated participants, with mean years of education exceeding 17 (Harrison et al. 2018 ; Sun et al. 2016 ; Hoenig et al. 2020 ). Only three studies reported lower average educational attainment than the national mean, all of which applied the superager framework to populations living with human immunodeficiency virus (HIV) (Saloner et al. 2019 ; 2022a ; 2022b ). This observation highlights another limitation in the field, as most superageing studies overlook the fact that morbidity in old age is common (Salive 2013 ) and tend to over-represent exceptionally healthy participants. Nevertheless, some exceptions exist. For instance, superager studies have been conducted among adults living with HIV (Saloner et al. 2019 ; 2022a ; 2022b ) and among newly diagnosed patients with Parkinson’s disease (Uribe-Kirby et al. 2025 ). Although these studies included participants aged 50 years and above, slightly younger than most cohorts in the field, their cognitive performance was compared with that of healthy individuals aged 25 years, and they still identified superagers. These findings reinforce the notion that successful episodic memory ageing can coexist with systemic infection or even early stages of neurodegenerative disease. A further example concerns the urban-rural distribution of the populations screened using the superager paradigm. In the 27 European Union member states, most adults aged 65 years or over live in rural areas (Eurostat 2020 ), yet participants in these cohorts are typically recruited from major cities. Overall, not only does the diversity of ageing cohorts studied within the superageing paradigm remain limited, but these groups may not closely reflect the characteristics of the average ageing population. Extending this research field to underrepresented populations is crucial for fully understanding the phenotype of superagers. Investigating diverse populations allows comprehensive evaluation of the multifactorial determinants of brain health, including genetic and exposome factors, which vary geographically and across socio-economic status (Baez et al. 2023; Greene et al. 2022 ; Resende et al. 2019 ). Only through such an inclusive approach can we identify the whole range of protective factors that superagers can reveal against age-related or pathological decline of episodic memory. Factors contributing to human diversity in ageing populations modulate cognitive and brain ageing. For instance, older brain ages have been reported in Latin American and Caribbean countries compared with the Global North, and in societies with greater socioeconomic inequality (Moguilner et al. 2024 ). Faster ageing has likewise been observed in sociodemographic groups with shorter lifespans (Balachandran et al. 2024 ). Even in Switzerland, a country with one of the highest Human Development Index values in the world (United Nations 2025 ), individuals exposed to socioeconomic disadvantage from childhood to adulthood aged 10% faster than those with consistently advantageous backgrounds (Schrempft et al. 2022 ). These differences concern population-level effects rather than individual variability, as superagers often exhibit slower brain ageing, evidenced by younger estimated brain ages relative to typical peers (Gaser et al. 2025 ; Park et al. 2025 ). Additionally, although recent studies suggest that higher educational attainment does not influence the rate of memory decline over time (Fjell et al. 2025 ; Lövdén et al. 2020 ; Seblova et al. 2020 ) nor affects age-sensitive cortical regions or hippocampal atrophy (Nyberg et al. 2021 ), the relationship between education level and episodic memory age-related decline or brain ageing trajectories remains controversial (Li et al. 2021 ; Steffener 2021 ). Nevertheless, the influence of education on baseline memory performance is well established, with higher educational attainment consistently associated with superior episodic memory performance at baseline (Fjell et al. 2025 ; Glymour et al. 2008 ; Schneeweis et al. 2014 ) reflected in the common practice of adjusting normative cognitive test values according to educational level (Delis et al. 2022 ; Magalhães and Hamdan 2010; Peña-Casanova et al. 2009 ). Applying the superager selection criteria to more diverse populations may require adaptations (Rajah et al. 2026 ). Variations in life expectancy, educational attainment, and socio-economic context should be considered when adapting elements of the superager criteria, such as age thresholds and neuropsychological assessment tests. Regarding age cut-offs, the same chronological age to define the target population in countries with markedly different disability level results in groups with distinct characteristics (Sudharsanan and Bloom 2018 ). For this reason, determining age thresholds based on remaining life expectancy has been proposed as a more accurate proxy for a population’s functional status than chronological age alone (Riffe et al. 2015 ). As highlighted in Section 3, this threshold may need adjustment according to population characteristics, with suggestions to lower it by around five years in developing countries to improve comparability (Borelli et al. 2018 ) and better align the target population. Most neuropsychological assessments were developed for highly educated, English-speaking Western populations, limiting their applicability elsewhere (Alladi and Hachinski 2018; Parra et al. 2018 ). As raised in Section 5.1 , most tests used to evaluate episodic memory in the superageing literature are verbal learning tasks that depend on language ability and are influenced by educational level (Fjell et al. 2025 ; Glymour et al. 2008 ; Schneeweis et al. 2014 ). This reliance may pose a barrier when assessing individuals with limited formal education (Borelli et al. 2018 ; Pellicer-Espinosa and Díaz-Orueta 2022). Non-verbal memory tests could be an alternative for populations with lower educational backgrounds, although such tests are not entirely independent of educational level (Rosselli and Ardila 2003). Consequently, although heterogeneous selection criteria complicate integrating findings across studies, a one-size-fits-all approach is inappropriate when seeking to enhance our understanding of superageing in an increasingly diverse ageing population. 9. Conclusions This scoping review examines the divergent criteria used to define superagers within the episodic memory ageing literature, based on the heterogeneity identified through a systematic search of MEDLINE and Scopus, which yielded 78 articles. The review identified as the main sources of heterogeneity in superager selection criteria: i. the age range of the target sample; ii. the specific tests used to assess episodic memory; iii. the benchmarks applied to episodic memory performance to distinguish successful from typical ageing; iv. whether longitudinal trajectories of episodic memory ability are incorporated into the definition; and v. whether non-memory cognitive abilities are included as additional criteria. The findings indicate that the combinations of these factors are nearly unique to each cohort or study, thereby maximising the number of possible definitions and increasing overall heterogeneity in the field. This variability makes the cross-study comparison of results challenging. The review discusses the implications of these definitional variations to facilitate the interpretation of findings, as studies may be reporting outcomes that are only loosely comparable. Heterogeneity in the superager selection criteria should not be viewed as a flaw in the field. Focusing on this variability is not intended to impose a preferred criterion or establish a hierarchy among definitions. Rather, this diversity enriches our understanding of successful episodic memory ageing by providing multiple perspectives and represents a necessary avenue for advancing the field. Such heterogeneity is not solely determined by study design. In some cases, it arises from the need to extend these criteria to diverse ageing populations. For instance, to determine whether the superager phenomenon occurs across different sociodemographic conditions, it may be necessary to adapt assessment tools or criteria, for example, to ensure their relevance for populations with lower levels of formal education. Finally, this review offers a conceptual framework to guide future research. It can facilitate cross-study comparisons, highlight gaps in the literature, and support selection of appropriate criteria for specific cohort designs by clarifying which questions can and cannot be addressed. The framework also aids readers in interpreting existing findings, promoting a more coherent understanding of superageing across studies. Table 1 Summary of criteria defining the superager group across the reviewed studies. Study Country cohort Age range Memory assessment Benchmark memory Longitudinal memory criterion Non-memory asessement Sample size superager group Baran et al., 2018(Baran and Lin 2018) United States 55–90 Memory composite: RAVLT, ADAS-Cog and Logical Memory Automatic: finite mixture modeling Yes Digit Span backwards score, semantic fluency, TMT-A, TMT-B and the Clock Drawing Test 122 Batra et al., 2024 (Batra et al. 2024 ) India ≥ 75 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old No Full Scale IQ 24 Borelli et al., 2021 (Borelli et al. 2021 ) Brazil ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50-65-year-olds No BNT, TMT-B and semantic fluency 10 Calandri et al., 2020 (Calandri et al. 2020 ) Argentina ≥ 80 RAVLT: delayed recall score Younger: memory score > -1 SD of 50-60-year-olds No BNT, TMT-B and semantic fluency 20 Cervenkova et al., 2020(Cervenkova et al. 2020) Czech Republic ≥ 80 PVLT: delayed recall score Younger: memory score ≥ mean of 60-year-olds No BNT, TMT-B and semantic fluency 20 Chen et al., 2020 (Chen et al. 2020 ) United States 55–90 Memory composite: RAVLT, ADAS-Cog and Logical Memory Automatic: finite mixture modeling Yes Digit Span backwards score, semantic fluency, TMT-A, TMT-B and the Clock Drawing Test 24 Cook et al., 2017 (Cook et al. 2017 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old Yes BNT, TMT-B and semantic fluency 24 Cook Maher et al., 2017 (Cook Maher et al. 2017 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50-65-year-old No BNT, TMT-B and semantic fluency 31 Cook Maher et al., 2022 (Cook Maher et al. 2022 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 56–64 year-olds Yes BNT, TMT-B and semantic fluency 56 Dang et al., 2019a (Dang et al. 2019a ) Australia ≥ 60 CVLT: long delayed free recall score (second edition) Younger: memory score ≥ mean of 30-44-year-olds No Digit Symbol Substitution Test, Victoria Stroop Test words score, Digit Span, lexical fluency and semantic fluency 172 Dang et al., 2019b (Dang et al. 2019b ) Australia ≥ 60 CVLT: long delayed free recall score (second edition) Younger: memory score ≥ mean of 30-44-year-olds No Digit Symbol Substitution Test, Victoria Stroop Test words score, Digit Span, lexical fluency and semantic fluency 179 de Godoy et al., 2021(de Godoy et al. 2021a ) Brazil ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50-60-year-olds No Digit Span forward and backward score, BNT, TMT-A, TMT-B, RCFT, semantic and lexical fluency 12 de Godoy et al., 2023 (de Godoy et al. 2023 ) Brazil ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50-60-year-olds No Digit Span forward and backward score, BNT, TMT-A, TMT-B, RCFT, semantic and lexical fluency 14 de Souza et al., 2022 (de Souza et al. 2022 ) Brazil ≥ 80 RAVLT: delayed recall score Age-Peer: memory score > 1.5 SD for age No Not applied 10 Dekhtyar et al., 2017 (Dekhtyar et al. 2017 , 20217) United States ≥ 75 Memory composite: MCT delayed free and cued recall score, FNAME delayed recall and SRT delayed recall and delayed multiple choice Age-Peer: memory composite ≥ 0.5 SD No Not applied 25 Diamond et al., 2024 (Diamond et al. 2024 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old Yes BNT, TMT-B and semantic fluency 24 Dominguez et al., 2021 (Dominguez et al. 2021 ) United States ≥ 70 NACC cohort: WMS Logical Memory IIA delayed recall (revised); 90 + cohort: CVLT long delayed free recall score (short form) Age-Peer: NACC cohort, top 50th percentile; 90 + cohort, at or above the top 50th percentile for their age group No NACC cohort: TMT-B; 90 + cohort: TMT-B NACC cohort = 105; 90 + cohort = 35 Dominguez et al., 2024 (Dominguez et al. 2024 ) United States ≥ 60 ADNI cohort: WMS Logical Memory IIA delayed recall (revised); 90 + cohort: CVLT long delayed free recall score (short form) Age-Peer: ADNI cohort, top 50th percentile; 90 + cohort, at or above the top 50th percentile for their age group No ADNI cohort: TMT-B; 90 + cohort: TMT-B ADNI cohort = 58; 90 + cohort = 41 Doyle et al., 2024 (Doyle et al. 2024 ) Puerto Rico and United States ≥ 80 Word List: immediate and delayed recall score Younger: memory score ≥ median of 55-64-year-olds No Not applied PREHCO cohort = 45; HRS cohort = 31 Engelmeyer et al., 2023 (Engelmeyer et al. 2023 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old No BNT, TMT-B and semantic fluency 96 Fili et al., 2024 (Fili et al. 2024 ) United Kingdom 55–70 Cognitive composite: prospective memory, pairs matching memory, fluid intelligence and reaction time Automatic: Optimal Labeling with Bayesian Optimization (OLBO) in-house algorithm Yes Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 689 Fili et al., 2025 (Fili et al. 2025 ) United Kingdom 55–70 Cognitive composite: prospective memory, pairs matching memory, fluid intelligence and reaction time Automatic: Optimal Cognitive Scoring (OptiCS) (in-house algorithm) Yes Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 864 Gardener et al., 2021 (Gardener et al. 2021 ) Australia ≥ 70 CVLT: long delayed free recall score (second edition) Younger: memory score ≥ mean of 30-44-year-olds Yes Logical Memory, RCFT delayed score, Stroop speed of colors/speed of dots ratio, Digit Span, Digit Symbol Coding, Controlled Oral Word Association Task, semantic fluency, and BNT 76 Garo-Pascual et al., 2023 (Garo-Pascual et al. 2023 ) Spain ≥ 79.5 FCSRT: delayed free recall score Younger: memory score ≥ mean of 50-56-year-olds Yes BNT, digit symbol substitution test and semantic fluency test 64 Garo-Pascual et al., 2024 (Garo-Pascual et al. 2024 ) Spain ≥ 79.5 FCSRT: delayed free recall score Younger: memory score ≥ mean of 50-56-year-olds Yes BNT, digit symbol substitution test and semantic fluency test 64 Gefen et al., 2014 (Gefen et al. 2014 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old No BNT, TMT-B and semantic fluency 18 Gefen et al., 2015 (Gefen et al. 2015 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old No BNT, TMT-B and semantic fluency 31 Gefen et al., 2018 (Gefen et al. 2018 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old No BNT, TMT-B and semantic fluency 5 Gefen et al., 2019 (Gefen et al. 2019 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50-60-year-olds No BNT, TMT-B and semantic fluency 5 Gefen et al., 2021 (Gefen et al. 2021 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old No BNT, TMT-B and semantic fluency 7 Harrison et al., 2012 (Harrison et al. 2012 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old No BNT, TMT-B and semantic fluency 12 Harrison et al., 2018 (Harrison et al. 2018 ) United States ≥ 70 CVLT: long delayed free recall score Younger: memory score ≥ mean of 18-32-year-olds No TMT-B 26 Harrison et al., 2024 (Harrison et al. 2024 ) United States 55–90 Memory composite: RAVLT, ADAS-Cog and Logical Memory Age-Peer: memory composite slope ≥ 0 Yes Not applied 221 Hermansen et al., 2024 (Hermansen et al. 2024 ) Denmark 92–95 Cognitive composite: fluency test, digits forward test, digits backward test, immediate recall test and delayed recall test Younger: cognitive composite > mean of 50-60-year-olds No Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 1905 birth cohort = 27; 1915 birth cohort = 33 Hoenig et al., 2020 (Hoenig et al. 2020 ) United States ≥ 80 Memory composite: RAVLT, ADAS-Cog and Logical Memory Age-Peer: memory composite z-score > 1.25 Yes Not applied 25 Huentelman et al., 2018 (Huentelman et al. 2018 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50-65-year-old No BNT, TMT-B and semantic fluency 56 Janeczek et al., 2018 (Janeczek et al. 2018 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old No BNT, TMT-B and semantic fluency 5 Jia et al., 2022 (Jia et al. 2022 ) China ≥ 60 RAVLT: delayed recall score Age-Peer: memory score > 1 SD for age No TMT-A, TMT-B, BNT and WAIS-III 34 Josefsson et al., 2012 (Josefsson et al. 2012 ) Sweden 35–85 Memory composite: immediate free recall of 16 imperative verb–noun sentences enacted by participant, delayed cued recall of nouns from enacted sentences, immediate free recall of 16 verb–noun sentences verbally and visually presented, delayed cued recall of nouns from the previously presented sentences and immediate free recall of 12 verbally presented nouns Age-Peer: memory composite > 1 SD from the estimated average score for age Yes Not applied 285 Josefsson et al., 2023 (Josefsson et al. 2023 ) Sweden 35–85 Memory composite: immediate free recall of 16 imperative verb–noun sentences enacted by participant, delayed cued recall of nouns from enacted sentences, immediate free recall of 16 verb–noun sentences verbally and visually presented, delayed cued recall of nouns from the previously presented sentences and immediate free recall of 12 verbally presented nouns Age-Peer: memory composite > 1 SD from the estimated average score for age Yes Not applied 256 Karpouzian-Rogers et al., 2023 (Karpouzian-Rogers et al. 2023 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50-65-year-old Yes BNT, TMT-B and semantic fluency 46 Katsumi et al., 2021 (Katsumi et al. 2021 ) United States 60–80 CVLT: long delayed free recall score Younger: memory score ≥ mean of 18-32-year-olds No TMT-B 17 Katsumi et al., 2022 (Katsumi et al. 2022 ) United States ≥ 70 HVLT: long delayed free recall score (revised) Younger: memory score ≥ mean of 16-29-year-olds No TMT-B 19 Keenan et al., 2024 (Keenan et al. 2024 ) United States ≥ 60 RAVLT: immediate and forgetting score Younger: memory score ≥ mean of 20-29-year-olds No TMT-B 20 Kim et al., 2020 (Kim et al. 2020 ) South Korea ≥ 60 Memory composite: SVLT delayed recall score and RCFT delayed recall score Younger: memory composite ≥ mean of 45-year-olds No Not applied 35 Kim et al., 2024 (Kim et al. 2024 ) South Korea ≥ 60 Memory composite: SVLT delayed recall score and RCFT delayed recall score Younger: memory composite ≥ mean of 45-year-olds No Seoul Neuropsychological Screening Battery-II 57 Kopeček et al., 2023 (Kopeček et al. 2023 ) Czech Republic ≥ 80 PVLT: delayed recall score Younger: memory score ≥ mean 60-64-year-olds No BNT, TMT-B and semantic fluency 20 Lin et al., 2017a (Lin et al. 2017a ) United States 55–90 Memory composite: RAVLT, ADAS-Cog and Logical Memory Automatic: finite mixture modeling Yes Digit Span backwards score, semantic fluency, TMT-A, TMT-B and the Clock Drawing Test 144 Lin et al., 2017b (Lin et al. 2017b ) United States 55–90 Memory composite: RAVLT, ADAS-Cog and Logical Memory Age-Peer: memory composite Z-score > 1.5 Yes Not applied 9 Lin et al., 2024 (Lin et al. 2024 ) United States ≥ 80 CERAD W-L: three immediate and delayed score Younger: memory score > mean of 60-64-year-olds No Semantic fluency test and digit symbol substitution test 33 Maccora et al., 2021 (Maccora et al. 2021 ) Australia 68–74 CVLT: immediate and delayed recall score Younger: memory score ≥ median of participants in the study’s 20s cohort Yes Not applied 116 Mapstone et al., 2017 (Mapstone et al. 2017 ) United States ≥ 70 RAVLT: learning, retrieval and recognition score Age-Peer: memory Z-score > 1.35 No Digit Span forward and backward score, TMT-A and TMT-B, BNT and Hooper Visual Organization Test 41 McPhee et al., 2025 (McPhee et al. 2025 ) United States and Canada 80–89 FNA: associative recognition score (hit rate minus false alarm rate) Younger: memory score ≥ mean of 50-69-year-olds No Spatial Working Memory, Stroop interference task and Letter-Number Alternations task 162 Mohammadiarvejeh et al., 2024 (Mohammadiarvejeh et al. 2024 ) United Kingdom 55–70 Cognitive composite: prospective memory, pairs matching memory, fluid intelligence and reaction time Automatic: principal component analysis Yes Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 1684 Nassif et al., 2022 (Nassif et al. 2022 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old No BNT, TMT-B and semantic fluency 6 Park et al., 2022 (Park et al. 2022 ) South Korea ≥ 60 Memory composite: SVLT delayed recall score and RCFT delayed recall score Younger: memory composite ≥ mean of 45-year-olds No Digit Span, BNT, Controlled Oral Word Association Test, Color Word Stroop Test, Digit Symbol Coding and TMT 32 Park et al., 2025 (Park et al. 2025 ) South Korea ≥ 60 Memory composite: SVLT delayed recall score and RCFT delayed recall score Younger: memory composite ≥ mean of 45-year-olds No Digit Span, BNT, Controlled Oral Word Association Test, Color Word Stroop Test, Digit Symbol Coding and TMT 63 Petkus et al., 2021 (Petkus et al. 2021 ) United States 66–84 Cognitive composite: CVLT immediate recall scores and long delayed recall score (modified), BVRT number of errors, Digit Span forward and backward scores, Card Rotations Test, phonemic test and semantic verbal fluency Automatic: latent class analysis No Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 381 Pezzoli et al., 2024 (Pezzoli et al. 2024 ) United States ≥ 70 Memory composite: CVLT short delayed free recall score, CVLT long delayed free recall score, Visual reproduction I and II, Logical Memory total Score, and Verbal Paired Associates Younger: definition (1): cognitive age gap (cognitive predicted age - chronological age) derived from cognitive composite at lowest 20th percentile; definition (2): memory composite at top 20% for their age; definition (3): non-memory cognition composite at 80th percentile for their age; definition (4): CVLT long delayed free recall ≥ mean of 18-32-year-olds No Stroop in 60 seconds, Digit Symbol, TMT-A, TMT-A subtracted from TMT-B (Trails B–A), Digit Span Backward, Animal Naming, and Vegetable Naming 74 Pezzoli et al., 2025 (Pezzoli et al. 2025 ) United States ≥ 70 Cognitive composite: CVLT free total (trials 1–5) recall score, short delayed cued recall score, long delayed cued recall score, TMT-A and TMT-B, Stroop test, verbal fluency (FAS test), Animal Naming, Vegetable Naming, Digit Symbol, Logical Memory total recall, Visual Reproduction I, II and recognition total, Digit Span forward and backward scores, and BNT Younger: cognitive age gap (cognitive predicted age - chronological age) derived from cognitive composite < 0 No Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column not specified Pudas et al., 2013 (Pudas et al. 2013 ) Sweden 35–85 Memory composite: immediate free recall of 16 imperative verb–noun sentences enacted by participant, delayed cued recall of nouns from enacted sentences, immediate free recall of 16 verb–noun sentences verbally and visually presented, delayed cued recall of nouns from the previously presented sentences and immediate free recall of 12 verbally presented nouns Age-Peer: memory composite > 1 SD from the estimated average score for age Yes Not applied 51 Rogalski et al., 2013 (Rogalski et al. 2013 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old No BNT, TMT-B and semantic fluency 5 Rogalski et al., 2019 (Rogalski et al. 2019 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50–60 years old No BNT, TMT-B and semantic fluency 10 Saliasi et al., 2015 (Saliasi et al. 2015 ) Netherlands 59–74 Cognitive composite: phonemic fluency tests, semantic fluency tests, Digit Span forward and backward scores, TMT-A, ratio TMT-(B/A), immediate recall score, delayed recall score and response speed score Automatic: graph theory approach No Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 26 Saloner et al., 2019 (Saloner et al. 2019 ) United States ≥ 50 Cognitive composite: semantic fluency, lexical fluency, Paced Auditory Serial Addition Task, WAIS–III Letter-Number Sequencing, WMS-III Spatial Span, WAIS–III Digit Symbol, WAIS–III Symbol Search, TMT-A, Stroop Color and Word Test Color Score, Wisconsin Card Sorting Test-64, Perseverative Errors, TMT-B, Stroop Color & Word Test Interference Score, Halstead Category Test, HVLT total learning score (revised), Brief Visuospatial Memory Test (revised), Total Learning, Story Memory Test Learning, Figure Memory Test Learning, HVLT delayed recall score (revised), Brief Visuospatial Memory Test delayed recall score (revised), Story Memory Test delayed recall score, Figure Memory Test delayed recall score and Grooved Pegboard Test dominant and non-dominant hand Younger: cognitive composite within 1 SD of 25-year-olds No Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 124 Saloner et al., 2022a (Saloner et al. 2022a ) United States ≥ 50 Cognitive composite: semantic fluency, lexical fluency, Paced Auditory Serial Addition Task, WAIS–III Letter-Number Sequencing, WMS-III Spatial Span, WAIS–III Digit Symbol, WAIS–III Symbol Search, TMT-A, Stroop Color and Word Test Color Score, Wisconsin Card Sorting Test-64, Perseverative Errors, TMT-B, Stroop Color & Word Test Interference Score, Halstead Category Test, HVLT total learning score (revised), Brief Visuospatial Memory Test (revised), Total Learning, Story Memory Test Learning, Figure Memory Test Learning, HVLT delayed recall score (revised), Brief Visuospatial Memory Test delayed recall score (revised), Story Memory Test delayed recall score, Figure Memory Test delayed recall score and Grooved Pegboard Test dominant and non-dominant hand Younger: cognitive composite within 1 SD of 25-year-olds No Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 57 Saloner et al., 2022b (Saloner et al. 2022b ) United States ≥ 50 Cognitive composite: semantic fluency, lexical fluency, Paced Auditory Serial Addition Task, WAIS–III Letter-Number Sequencing, WMS-III Spatial Span, WAIS–III Digit Symbol, WAIS–III Symbol Search, TMT-A, Stroop Color and Word Test Color Score, Wisconsin Card Sorting Test-64, Perseverative Errors, TMT-B, Stroop Color & Word Test Interference Score, Halstead Category Test, HVLT total learning score (revised), Brief Visuospatial Memory Test (revised), Total Learning, Story Memory Test Learning, Figure Memory Test Learning, HVLT delayed recall score (revised), Brief Visuospatial Memory Test delayed recall score (revised), Story Memory Test delayed recall score, Figure Memory Test delayed recall score and Grooved Pegboard Test dominant and non-dominant hand Younger: cognitive composite within 1 SD of 25-year-olds Yes Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 31 Spencer et al., 2022 (Spencer et al. 2022 ) United States ≥ 80 RAVLT: delayed recall score Younger: memory score ≥ mean of 50-65-year-olds No BNT, TMT-B and semantic fluency 37 Sun et al., 2016 (Sun et al. 2016 ) United States 60–80 CVLT: long delayed free recall score Younger: memory score ≥ mean of 18-32-year-olds No TMT-B 17 Ticha et al., 2023 (Ticha et al. 2023 ) Czech Republic ≥ 80 PVLT: delayed recall score Younger: memory score ≥ mean 60-64-year-olds No BNT, TMT-B and semantic fluency 19 Trammell et al., 2024 (Trammell et al. 2024 ) United States ≥ 80 Craft Story: delayed recall score (version 21) Younger: memory score within 1 SD of 50-60-year-olds No TMT-B, lexical fluency and the Multilingual Naming Test 61 Uribe-Kirby et al., 2025 (Uribe-Kirby et al. 2025 ) Multiple sites in United States and Europe 44–85 Cognitive composite: Letter-Number Sequence, Symbol Digit Modalities Test, semantic fluency, HVLT immediate and delayed verbal recall score, and Judgment of Line Orientation Younger: cognitive composite ≥ 0.5 SD of 25-year-olds on ≥ 3 tests No Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 233 Wang et al., 2019 (Wang et al. 2019 ) United States 55–90 Memory composite: RAVLT, ADAS-Cog and Logical Memory Automatic: finite mixture modeling Yes Digit Span backwards score, semantic fluency, TMT-A, TMT-B and the Clock Drawing Test 13 Yu et al., 2020 (Yu et al. 2020 ) Singapore ≥ 60 Cognitive composite: RBANS consisting of 12 subtests that assess immediate memory, delayed memory, language, attention and visuospatial construction Age-Peer: cognitive composite in ≥ one domain according to age norms (index scores ≥ 115) No Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 64 Zammit et al., 2018 (Zammit et al. 2018 ) United States ≥ 70 Cognitive composite: FCSRT free recall score, Logical Memory, semantic fluency, BNT, Digit Span, TMT-A, TMT-B, Digit Symbol Coding, Block Design and Controlled Oral Word Fluency Test Automatic: latent class analysis No Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 124 Zammit et al., 2020 (Zammit et al. 2020 ) United States 53.3–100 Cognitive composite: Logical Memory total score, Word List recall, BNT, semantic fluency, Digit Span forward and backward scores, Digit Ordering, Matrices and Line Orientation, Symbol Digits Modalities Test and Number Composition Automatic: latent transition analysis Yes Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 420 Zammit et al., 2021 (Zammit et al. 2021 ) United States 53.3–100 Cognitive composite: Word List memory, recall and recognition scores, Story Recall immediate and delayed scores, Logical Memory I and II, BNT, Verbal Fluency, Reading Test, Digit Span forward and backward score, Digit Ordering, Symbol Digits Modalities Test, Number Comparison, Stroop color naming and word reading, Judgment Line Orientation, Standard Progressive Matrices Automatic: latent class analysis and time-varying effects models Yes Memory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column 328 Zhang et al., 2020 (Zhang et al. 2020 ) United States 60–80 CVLT: long delayed free recall score Younger: memory score ≥ mean of 18-32-year-olds No TMT-B 17 Benchmark memory refer to age-specific norms when applicable and details regarding gender or education adjustments are available in the original articles. The Mini-Mental State Examination (MMSE) was excluded from assessment counts as it is considered a cognitive screening tool. Memory assessment and non-memory assessment classification was based solely on specific memory and non-memory cognitive tests. ADAS-Cog, Alzheimer's Disease Assessment Scale Cognitive Subscale; BNT, Boston Naming Test; BVRT, Benton Visual Retention Test; CERAD W-L, Consortium to Establish a Registry for Alzheimer's Disease Word List Memory Task; CVLT, California Verbal Learning Test; FCSRT, Free and Cued Selective Reminding Test; FNA, Face Name Association; FNAME, Face Name Associative Memory Exam; HVLT, Hopkins Verbal Learning Test; MCT, Memory Capacity Test; PVLT, Philadelphia Verbal Learning Test; RAVLT, Rey Auditory Verbal Learning Test; RBANS, Repeatable Battery for the Assessment of Neuropsychological Status; RCFT, Rey-Osterrieth Complex Figure Test; SD, standard deviation; SRT, Selective Reminding Test; SVLT, Seoul Verbal Learning Test; TMT-A, Trail Making Test part A; TMT-B, Trail Making Test part B; WAIS–III, Wechsler Adult Intelligence Scale (third edition) and WMS, Wechsler Memory Scale. Declarations Competing interest: The authors declare no competing interests. Author contributions: MGP and BD contributed to the conceptualisation of the study, MGP, DF and CR contributed to the investigation performing the literature search and review, MGP, DF and CR drafted the original manuscript, MGP, DF, CR and BD review and edited the manuscript. Acknowledgments: B.D. is supported by the Swiss National Science Foundation (project grant no. 32003B_212466, 32NE30_221732, 33IC30_213595 and CRSII5_209510), InnoSuisse Flagship Swiss brAInHealth project, ERA_NET NEURON JTC2023-ELSA: BrainTree projects. D.F. is supported by a Royal Society University Research Fellowship (URF/R1/241499) We thank Alexia Candal-Zürcher for providing valuable clinical perspective for this study, and the inAGE laboratory members for their helpful feedback on the study visuals. 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J Alzheimer’s Disease: JAD 73(3):1063–1073 rayyan-188462662. https://doi.org/10.3233/JAD-190778 Zammit AR, Hall CB, Katz MJ et al (2018) Class-Specific Incidence of All-Cause Dementia and Alzheimer’s Disease: A Latent Class Approach. J Alzheimer’s Disease: JAD 66(1):347–357. https://doi.org/10.3233/JAD-180604 . rayyan-188462664 Zammit AR, Yang J, Buchman AS et al (2021) Latent Cognitive Class at Enrollment Predicts Future Cognitive Trajectories of Decline in a Community Sample of Older Adults. J Alzheimer’s Disease: JAD 83(2):641–652. https://doi.org/10.3233/JAD-210484 . rayyan-188462666 Zaninotto, Paola GD, Batty M, Allerhand, Deary IJ (2018) Cognitive Function Trajectories and Their Determinants in Older People: 8 Years of Follow-up in the English Longitudinal Study of Ageing’. Ageing and Health. J Epidemiol Community Health 72(8):685–694. https://doi.org/10.1136/jech-2017-210116 Zhang J, Andreano JM, Dickerson BC, Touroutoglou A, Lisa Feldman B (2020) ‘Stronger Functional Connectivity in the Default Mode and Salience Networks Is Associated With Youthful Memory in Superaging’. Cerebral Cortex (New York, N.Y.: 1991) 30 (1): 72–84. rayyan-188462687. https://doi.org/10.1093/cercor/bhz071 Additional Declarations The authors declare no competing interests. Supplementary Files GaroPascualetalsupplementarymaterial.docx Supplementary Material Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2026 Read the published version in Neuroscience & Biobehavioral Reviews → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8916228","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":593834219,"identity":"6e717348-76c6-44e6-9d2e-f48f8f169d1c","order_by":0,"name":"Marta Garo-Pascual","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIiWNgGAWjYCgAGX4QmVCAVxFjAzKPRxLETTAgRYvBARCFR4tu+9nnD378ssvnl25+/OFnmx2P8fnViR8eGDDI84sdwKrF7Ey6YWNvX7LlzDnHzCR725J5zG683SwBdJjhzNkJ2LUcSGNs4O1hNjC4kWDGzNjGDNRydgNIS4LBbRxazj9jbPzbUw/Ukv75M2NbPY/xjLObf+DVciONsZnnx2GglhwDaca2wzwG/L3b8Nty4xnjbNmG4waSc86USfacO84jcYN3m0WCgQRuv5xPY/j45k+1Ab90++YPP8qq5fj7z26++aPCRp5fGrsWMGBsAxISMJ4EWKUELsVQ8AdZDf8BAqpHwSgYBaNgpAEAUF9iPJouqc8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9502-7016","institution":"Inselspital - University Hospital Bern","correspondingAuthor":true,"prefix":"","firstName":"Marta","middleName":"","lastName":"Garo-Pascual","suffix":""},{"id":593834936,"identity":"743faabc-e1d9-40f7-b2a5-e274e5906db4","order_by":1,"name":"Darya Frank","email":"","orcid":"https://orcid.org/0000-0001-6081-6755","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Darya","middleName":"","lastName":"Frank","suffix":""},{"id":593834937,"identity":"54ae2f3b-4232-48af-98be-c6cfebc0da18","order_by":2,"name":"Cristina Ramponi","email":"","orcid":"https://orcid.org/0000-0003-3762-6931","institution":"Inselspital - University Hospital Bern","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Ramponi","suffix":""},{"id":593834938,"identity":"01609c35-194e-4156-88af-9938ca780976","order_by":3,"name":"Bogdan Draganski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYBACAyCWAGLGBgg/gYefGUQXQKSI0yLZDBUnWguDwQECWszZ2y/e+LmDQXZ7+/GLnwv+pMkYH+c9/OGDAYO8OQ4tlj1nii17zzAYzzmTUyw9sy2Hx+wwX5rkDAMGw50NOBx2IydNgreNIXEGQ06CNG9DBVALjxkzjwFDAsSF2LVI/gVp4X+T/JvnTwWPcTOP8ec/eLWkH5MG2yIBZPCw5fAYAK2QZsCjBegXZmvZNgnjGRJv2Kx529J4JIAOk+wxkDDcgEMLMMQe3nzbZiM7gz/98W2eP8n2/P1njD/8qLCRx2ULAwMPLGp4UCJCApd6IGB/gM4YBaNgFIyCUYAKANi5VsCMzekLAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5159-5919","institution":"Inselspital - University Hospital Bern","correspondingAuthor":true,"prefix":"","firstName":"Bogdan","middleName":"","lastName":"Draganski","suffix":""}],"badges":[],"createdAt":"2026-02-19 09:55:07","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8916228/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8916228/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.neubiorev.2026.106707","type":"published","date":"2026-04-28T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103047580,"identity":"80e62392-976f-4728-aac5-919eca53a818","added_by":"auto","created_at":"2026-02-20 06:55:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":258392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMCI, mild cognitive impairment.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8916228/v1/c7e3f572de3b796f63858d23.png"},{"id":103047531,"identity":"3c8574cf-204d-4ea1-a405-277afdd95141","added_by":"auto","created_at":"2026-02-20 06:55:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1251688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework for characterising the methodological heterogeneity in superageing research. \u003c/strong\u003eFollowing screening of the literature, 78 articles were included in the analysis, revealing substantial heterogeneity in the selection criteria of successful episodic memory agers, hereafter termed superagers. The figure summarises variability in selection criteria across five domains: chronological age thresholds, memory assessments, non-memory cognitive assessments, episodic memory benchmarks for delineating superagers from typical agers, and longitudinal memory requirements in the selection criteria. This conceptual framework highlights challenges for cross-study comparability and identifies gaps in the literature related to selection criteria. Studies are categorised according to their approach for defining the boundary between successful and typical episodic memory ageing (vertical facets), specifically whether this is based on comparison with a younger population, with top-performing age-matched peers, or using an automated method. In the left panel, horizontal bars represent the inclusion age range and truncated bars denote open-ended criteria with no specified maximum age limit. The inclusion of longitudinal memory assessments within the selection criteria is represented by different shades of grey in the bars. In the right panel, horizontal bars represent the sample size of the superager group, where n.s.indicates the value was not specified. Bar colours correspond to the memory assessment tasks used to select superagers. Symbols at the beginning of each bar (circle or triangle) indicate the presence or absence of non-memory cognitive assessments within the selection criteria. Details of data plotting are provided in the Supplementary Methods and full details of the selection criteria for each article are provided in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCERAD W-L, Consortium to Establish a Registry for Alzheimer's Disease Word List Memory Task; CVLT, California Verbal Learning Test; FCSRT, Free and Cued Selective Reminding Test; FNA, Face Name Association; HVLT, Hopkins Verbal Learning Test; PVLT, Philadelphia Verbal Learning Test; RAVLT, Rey Auditory Verbal Learning Test and WMS, Wechsler Memory Scale\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8916228/v1/2667e007692dcf3159f52ced.png"},{"id":103047583,"identity":"b2c43024-4d68-450b-99c6-1182857e8714","added_by":"auto","created_at":"2026-02-20 06:55:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1956917,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographical distribution of the global population over 65 years of age compared with the origins of cohorts studied under superageing paradigm. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) In 2023, Asia had the largest population of adults aged over 65 years (469.4 million), according to the United Nations World Population Prospects (2024) as processed by Our World in Data (Ritchie and Roser 2019). (\u003cstrong\u003eB\u003c/strong\u003e) In contrast, a 62% of studies included in this review (48 out of 78) were conducted in a population from the United States, highlighting a geographical bias in research on successful episodic memory ageing.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8916228/v1/5ed881074f5be4953a9d664c.png"},{"id":108707876,"identity":"6da6edd7-d628-4775-a8d6-382abbaced04","added_by":"auto","created_at":"2026-05-07 13:44:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4471970,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8916228/v1/f2f223d1-2c3d-471c-b6bb-a07955af55e4.pdf"},{"id":103047582,"identity":"7da0b261-c9fb-4a74-8dc4-ea78cdf511fb","added_by":"auto","created_at":"2026-02-20 06:55:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16529,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material\u003c/p\u003e","description":"","filename":"GaroPascualetalsupplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8916228/v1/36ff9bc6b163d9177a578199.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDefining ‘Successful’ Episodic Memory Ageing: Implications of Methodological Heterogeneity\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEpisodic memory, which enables individuals to remember past experiences (Tulving \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e1972\u003c/span\u003e), is among the cognitive functions most vulnerable to ageing (Duarte and Kensinger 2019; Glisky \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), with severe deterioration being a primary clinical hallmark of Alzheimer\u0026rsquo;s disease. However, over the past decade, growing evidence demonstrates that some individuals maintain optimal episodic memory functioning as they age, sometimes at levels comparable to much younger age. This group of older adults with superior episodic memory offers valuable insights into mechanisms for preventing or mitigating memory decline in healthy ageing and dementia.\u003c/p\u003e \u003cp\u003eThere is considerable heterogeneity in how successful episodic memory ageing is defined across studies, challenging interpretation and comparison. This review investigates the sources of this definitional heterogeneity and evaluates their implications for interpreting and comparing study findings. We aim to outline a framework for understanding the cognitive and neural underpinnings of successful episodic memory ageing.\u003c/p\u003e \u003cp\u003eVarious terms describe older adults with superior episodic memory, including \u0026lsquo;superagers\u0026rsquo; (Dang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Garo-Pascual et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gefen et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Harrison et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), \u0026lsquo;supernormals\u0026rsquo; (Lin et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u0026lsquo;successful agers\u0026rsquo; (Pudas et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), \u0026lsquo;optimal memory performers\u0026rsquo; (Dekhtyar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), among others. Despite this terminological diversity, most definitions converge on three core domains: chronological age, episodic memory performance, and other (non-memory) cognitive abilities. However, these criteria are operationalised in markedly different ways, resulting in substantial heterogeneity across studies. Groups designated by the same category label may vary considerably across different studies. The term superager therefore does not reflect a uniform definition. For clarity and consistency, this review uses the terms superager and superageing as convenient references for the broader concept of successful episodic memory ageing.\u003c/p\u003e \u003cp\u003eThe impact of definitional variations has been addressed by examining sample age (Rogalski \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), episodic memory test type (Touroutoglou et al. \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and longitudinal trajectories of memory (Nyberg \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A recent systematic review evaluated superageing assessment, providing quantitative analysis of commonly used classifications (Andrade et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while another highlighted definitional variability and its interpretive implications (de Godoy et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). The present scoping review builds on this work by investigating multiple sources of variation in selection criteria, providing an integrated perspective on how such variability shapes the interpretation of findings. Although framed as a scoping review, we conducted a systematic literature search to ensure comprehensive field coverage and accurate assessment of its heterogeneity.\u003c/p\u003e \u003cp\u003eFirst, we address age criteria for sample selection. Second, we investigate the episodic memory domain, where greatest variation occurs, including benchmarks distinguishing successful from typical ageing, memory tests used, and longitudinal trajectories incorporated into definitions. Third, we assess whether different non-memory cognitive abilities and related test variability introduce distinct selection biases. Finally, we denote the extent to which current literature reflects diversity in cognitive abilities. Rather than imposing a hierarchy of definitions, we propose a conceptual framework enabling clearer field navigation, facilitating detection of cross-study comparability or complementarity, and revealing gaps in the literature.\u003c/p\u003e"},{"header":"2. Search strategy and selection criteria","content":"\u003cp\u003eSearches were conducted in MEDLINE (via Ovid) and Scopus from inception to March 2025. Search terms encompassed three main concepts: i. superior or successful memory/cognitive ageing; ii. memory maintenance, and iii. the \u0026ldquo;superager\u0026rdquo; construct and related terms including \u0026ldquo;supernormal\u0026rdquo;. The full search strategy is provided in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. Reference lists of relevant studies and review articles were screened, yielding four additional records not captured by database searches.\u003c/p\u003e \u003cp\u003eAll records were exported into Rayyan software (Ouzzani et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for deduplication and screening. Two authors (CR and MGP) independently screened titles and abstracts, followed by independent full-text assessment (DF and MGP). Discrepancies were resolved through discussion, with third author consultation when necessary. Studies were eligible if they met the following criteria: i. original research published in English; ii. human ageing population; and iii. identification of a group with episodic memory performance beyond typical ageing as part of inclusion criteria. In total, 78 studies met the inclusion criteria. Reasons for exclusion are detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Age domain","content":"\u003cp\u003eWhile chronological age is inherently tied to ageing-associated episodic memory maintenance and decline, the most appropriate age range for studying superageing remains debated. Although ageing is a continuous process without universal consensus on when old age begins (Shenkin et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), studies of superageing often require establishing a temporal boundary. The United Nations defines an old person as someone aged 60 years and above (UNHCR \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and the World Health Organisation similarly refers to populations over 60 in its ageing reports (WHO \u003cspan citationid=\"CR165\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In contrast, the National Institute on Aging generally uses \u0026ldquo;older adults\u0026rdquo; for individuals aged 65 years and above (NIH \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These cut-offs are shaped more by historical and social factors, notably pension eligibility (Costa \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), than by biology. Nonetheless, they align with evidence indicating episodic memory stability until the sixth decade, with decline typically beginning between 60 and 65 years (Nyberg \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; R\u0026ouml;nnlund et al. \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Schaie \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross the 78 studies reviewed, 34 (44%) examined individuals aged 80+, 25 (32%) of those aged 60+, and 19 (24%) included cohorts younger than 60. Most of the latter (14 out of 19) were longitudinal assessments, enabling causal inferences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The focus on those aged 60\u0026thinsp;+\u0026thinsp;reflects both onset of memory decline and proximity to retirement age (Sun et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contrast, studies of the 80\u0026thinsp;+\u0026thinsp;population better capture the cumulative age-related changes (e.g., brain atrophy) that emerge after longer exposure to biological and social risk factors (Rogalski \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Episodic memory decline follows a non-linear trajectory with age (Nyberg et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; R\u0026ouml;nnlund et al. \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), with distinct patterns observed in individuals aged 60\u0026ndash;80 versus those beyond 80 (Rogalski \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Brain structure shows slower grey matter atrophy rate in superagers over 80 compared with typical peers (Cook et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Garo-Pascual et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), whereas no significant group differences emerge in the 60\u0026ndash;80 range (Dang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). These findings should therefore be viewed as complementary, reflecting distinct windows of superageing rather than directly comparable trajectories.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparing studies by chronological age alone is sometimes sufficient to assess whether they examine comparable time windows. However, when populations differ substantially in life expectancy, retirement age, or sociodemographic background, groups of the same chronological age may not correspond in their stage of ageing (Balachandran et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sudharsanan and Bloom \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To account for such variability, some authors propose adjusting age cut-offs \u0026ndash; e.g., a five-year shift when comparing individuals over 80 in developed countries with those over 75 in developing countries (Borelli et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These considerations highlight the importance of accounting for population characteristics when interpreting ageing trajectories.\u003c/p\u003e"},{"header":"4. Episodic Memory Benchmark","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Boundary between successful and typical episodic memory ageing\u003c/h2\u003e \u003cp\u003eEpisodic memory ageing phenotypes likely exist on a continuum rather than as discrete categories. Therefore, classifying individuals as superagers or typical agers has limitations: i. benchmarks are often arbitrary and vary across studies, obscuring straightforward interpretation; ii. categorical approaches are statistically less sensitive; and iii. they oversimplify within-group variability, excluding borderline or atypical cases. Despite these drawbacks, delineating a superager category offers advantages. The phenotype is multidimensional by definition, integrating episodic memory performance with other non-memory measures (\u003cem\u003esee Section 7\u003c/em\u003e) and longitudinal trajectories (\u003cem\u003esee Section 6\u003c/em\u003e). Incorporating these multiple dimensions into a continuous model is not straightforward; although dimensionality-reduction methods could be applied, their outputs may be less interpretable than categorical definitions. The superager construct by design simplifies a complex, dynamic and multifactorial phenomenon. In this emerging research field, such simplification provides an interpretable framework for understanding the mechanisms underlying well-preserved episodic memory in later life.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.2. Approaches to defining the boundary between successful and typical episodic memory ageing: benefits, limitations and implications\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis section reviews approaches used in the superageing literature to define superager groups compared with typical older adults. Methods vary considerably and fall into three main categories: i. approaches using a younger reference group; ii. those relying on an age-peer reference, and iii. those using automated classification tools.\u003c/p\u003e \u003cp\u003eThe younger reference approach defines superagers by comparing their performance to normative values from young or middle-aged adults. This method underpins the original superager concept \u0026ndash; episodic memory in older adults equal to that of healthy adults 20\u0026ndash;30 years younger (Cook et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Garo-Pascual et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gefen et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Harrison et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is the most common strategy, adopted in 68% (53 out of 78) of the reviewed studies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), although the younger reference group\u0026rsquo;s age varies considerably. For studies setting the superager cut-off at 80+, the most common benchmark is adults aged 50\u0026ndash;60. For those defining superagers at 60+, the benchmark ranges from young adults (16\u0026ndash;29 years) to those aged 45.\u003c/p\u003e \u003cp\u003eThere is some correspondence between studies using middle-aged references group for individuals over 80 and those using younger adult references for cohorts aged 60\u0026ndash;80. Authors using both approaches have reported greater cingulate gyrus cortical thickness in superagers compared with typically ageing adults (Gefen et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, this was not replicated in a larger cohort of superagers over 80 using the same middle-aged reference group (Garo-Pascual et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Other studies adjust norms for education, sex, or ethnicity, although such adjustments are inconsistently applied. Normative values are typically derived from a distinct cohort, as study cohorts usually include only older adults. However, there are studies drawing younger references from the same cohort (Doyle et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Maccora et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Trammell et al. \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), ensuring closer comparability. In other cases, reference groups differ markedly in sociodemographic background \u0026ndash; e.g., when US standards were applied to Brazilian (de Godoy et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) or Indian (Batra et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), or Chinese populations (Jia et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite this variability, using a younger reference group strengthens validity by avoiding potential biases within the study cohort. The caveat is the lack of directly comparable normative data matched for sociodemographic factors or testing protocols. Even when comparability is achieved, reference value reliability depends on sufficient sample size.\u003c/p\u003e \u003cp\u003eUnlike the younger reference method, the age-peer approach benchmarks performance against an elderly population of equivalent age, derived either from the same study cohort of the targeted sample or an external reference dataset (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Superagers are identified by exceeding the cohort mean on cognitive tests, typically by 1-1.5 standard deviations (de Souza et al. \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Josefsson et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e), although some studies use intermediate thresholds (e.g., 1.25 or 1.35 standard deviations) (Hoenig et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mapstone et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) or select top performers from the 50th or 20th percentile (Dekhtyar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dominguez et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This method is advantageous when external normative data are unavailable or poorly matched to the study population. However, reliance on age-peer benchmarks introduces susceptibility to cohort-specific biases, limiting external validity and making cross-study replication more challenging.\u003c/p\u003e \u003cp\u003eAutomatic classification uses various strategies to define superagers, including graph theory approaches, principal component analysis, and finite mixture models such as latent class analysis, latent transition analysis, or latent trajectory modelling. Despite methodological diversity, these approaches share two features. First, reliance on multiple cognitive tests beyond episodic memory assessment identifying superagers who excel in other cognitive domains (Baran and Lin 2018; Saliasi et al. \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Second, most approaches input cognitive trajectories over time, incorporating a longitudinal perspective into superager definitions (Lin et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Mohammadiarvejeh et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zammit et al. \u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The main advantage is independence from arbitrary thresholds for distinguishing superagers from typical agers. Drawbacks include limited control over the group composition and greater interpretive complexity, as episodic memory scores may be combined with non-memory measures. When multiple domains contribute to classification, it is unclear whether all identified individuals exhibit superior episodic memory. Thus, automatic classification may be better suited to studying general cognitive functioning than episodic memory specifically. Finally, because these methods lack external references, group composition is inherently shaped by study sample biases.\u003c/p\u003e \u003cp\u003eLimited overlap exists between studies with similar aims that define superagers with different benchmark approaches, although some studies with comparable objectives have reported consistent findings. For example, whole-brain amyloid burden measured with positron emission tomography shows no differences between superagers and typical older adults across studies using a younger reference group (Dang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e; Harrison et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) an age-peer reference (Dekhtyar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), or an automatic approach (Baran and Lin 2018), suggesting that different approaches may capture similar effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Considerations for episodic memory benchmarks\u003c/h2\u003e \u003cp\u003eDefining the episodic memory benchmark to distinguish successful from typical ageing is a stepwise process that should be integrated into the study design. This may begin with determining whether to use non-arbitrary cut-offs derived from automatic methods, or to apply an informed, albeit arbitrary, threshold. When opting for an arbitrary threshold, a common strategy is to reference the mean performance of a younger population on the same test. The suitability of such external references must be evaluated relative to the study cohort, particularly regarding test administration protocols and sociodemographic characteristics. Sample size differences also warrant consideration, especially when the normative cohort is substantially smaller than the study sample. Where external references are unavailable or unsuitable, thresholds based on the age-peer distribution of the study cohort represent a valid alternative, although they will inevitably reflect cohort-specific biases. Consequently, whenever possible, validating the distribution of episodic memory scores in the target cohort against comparable samples is essential. Such validation ensures the chosen threshold retains relevance beyond the immediate study population, enhancing the generalisability of findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Episodic Memory Assessment","content":"\u003cp\u003eSuperageing is predominantly defined by episodic memory performance, yet the specific tests used vary across studies and cohorts (de Godoy et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Nyberg and Pudas 2019) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Episodic memory is a complex, multi-faceted process, relying on coordinated function of multiple neural systems, from initial encoding through consolidation, to retrieval. Therefore, test selection can influence superageing inclusion criteria. Performance may be driven by differential engagement of specific sub-components (e.g. encoding or retrieval demands), or task characteristics (e.g. perceptual or semantic processing). This methodological heterogeneity has critical implications, as it may explain conflicting findings regarding prevalence and neural underpinnings. More fundamentally, it raises the question of whether superageing is a unitary phenomenon or multiple distinct patterns of preserved cognitive function. This section reviews how episodic memory has been assessed across different studies, focusing on how test selection contributes to heterogeneity in the literature, and proposes considerations for advancing mechanistic understanding.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Verbal and visuospatial tests\u003c/h2\u003e \u003cp\u003eVerbal learning tests are the most common neuropsychological assessment in the superageing literature, demonstrating an imbalance with visuospatial tests. This bias likely reflects practical considerations \u0026ndash; verbal tests are often easier to standardise, administer, and score, but has important theoretical implications. Performance on verbal and visuospatial memory tests shows different ageing trajectories. Some studies show that visuospatial memory declines more steeply with age than verbal memory (Murre et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Park et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), while others find the opposite (Liampas et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These differences may partly reflect gender-related patterns, as previous work suggests women show relative advantages on verbal episodic memory tasks (Gale et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, 20; Herlitz et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Murre et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While effect sizes are typically small to moderate, the superageing literature provides some support for this gender-difference, with proportionally more women categorised as superagers using verbal memory tests (Maccora et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; McPhee et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, verbal dominance in assessment may overlook individuals who maintain superior non-verbal memory despite age-typical verbal decline \u0026ndash; a pattern that could represent a distinct form of resilience. This bias may disadvantage individuals from different linguistic or educational backgrounds, as verbal tests often draw on accumulated semantic knowledge and language-specific strategies (Lim et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). While visuospatial memory tests are not culture-free (Rosselli and Ardila 2003), they may reduce some language and educational confounds.\u003c/p\u003e \u003cp\u003eAs most studies also include non-memory measures (\u003cem\u003esee Section 7\u003c/em\u003e), these different functions can be directly compared. Several studies using verbal tasks to categorise superagers and typical older adults found no significant differences in language and visuospatial function (excluding episodic memory) between groups (Cook Maher et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Karpouzian-Rogers et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In one cohort (Katsumi et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) researchers examined visual-verbal association in superagers and typical older adults categorised based on California Verbal Learning Test (CVLT) performance. Superagers performed better on the visual-verbal item recognition task and marginally better on the association task. This suggests superior episodic memory performance of superagers extends beyond verbal memory tests (Pudas et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo further delineate this finding, directly comparing performance on verbal and visuospatial memory tests when categorising superagers would be valuable. While one cohort incorporated visuospatial memory tests in the categorisation stage (Kim et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), superagers in this cohort also showed higher non-memory visuospatial function, contrasting with previous findings (Cook Maher et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Karpouzian-Rogers et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These contradictory findings demonstrate the importance of episodic memory test selection for categorising superagers, and its implications for phenotyping this population.\u003c/p\u003e \u003cp\u003eIt is also worth considering the neural underpinnings supporting performance on verbal and visuospatial memory tests. While verbal memory functions are generally associated with left hemispheric dominance, no such laterality has been observed in relation to brain anatomy (e.g., hippocampal volume and cingulate cortical thickness) (Garo-Pascual et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pezzoli et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It is possible that lateralisation was not captured by structural measures but exists functionally. Although several studies examined functional connectivity using resting-state magnetic resonance imaging (fMRI) (de Godoy et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Diamond et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), this approach may not be best suited to examine laterality with the exception of one study reporting left-dominant functional connectivity within the cholinergic system (Jia et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To fully address this possibility, task-based fMRI using both types of memory tests would be needed. Alternatively, the lack of laterality effects could reflect a unitary superior episodic memory capacity that transcends domain-specific processes, whereby superageing captures a global advantage even when assessments primarily index the verbal domain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Measurements: retrieval tests and single vs composite scores\u003c/h2\u003e \u003cp\u003eAnother source of heterogeneity, even within verbal-memory tests, is the retrieval task employed: immediate free or cued recall, delayed free or cued recall, and recognition. Additionally, some studies combine these tests to create composite scores. Like the verbal-visuospatial memory distinction, the retrieval format profoundly shapes which cognitive and neural processes are assessed, potentially leading to distinct superager phenotypes.\u003c/p\u003e \u003cp\u003eIn our review sample, over half the studies (41 out of 78) rely exclusively on delayed free recall scores, which place high demands on encoding and self-initiated retrieval mediated by hippocampal-frontal-parietal engagement (Baldo and Shimamura \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). These neural systems align with the structural preservation reported in superager cohorts identified using free recall tests (Keenan et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nevertheless, it is important to note performance on free recall tests can also be influenced by other factors, that might not affect recognition to the same extent (e.g. mood disorders, vascular changes, attention and executive function impairments).\u003c/p\u003e \u003cp\u003eRecognition tests minimise retrieval demands and can be supported by either hippocampal-dependent recollection or medial temporal lobe-dependent familiarity processes (Montaldi and Mayes \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yonelinas \u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Given that recall is more sensitive to ageing than recognition (Rhodes et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as well as cued recall and associate-recognition tasks (Lowndes et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), these formats likely capture partially independent memory abilities and/or processes (Healey and Kahana 2016). As such, recognition is often used as part of a composite score to categorise superagers (Dominguez et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mapstone et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pezzoli et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zammit et al. \u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In most cases, performance is aggregated across different tests \u0026ndash; recognition and recall, hindering our ability to examine different performance patterns across the two measurements.\u003c/p\u003e \u003cp\u003eDespite this issue, composite scores are quite common, used by 31 of the 78 articles identified in this review. Most often they combine multiple memory measures, e.g. immediate and delayed recall or free and cued recall (Dekhtyar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Josefsson et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), but have also been used to combine measures across cognitive domains (Hermansen et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, 20; Uribe-Kirby et al. \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComposite measures can provide greater statistical reliability by reducing measurement error inherent in single tests (Kane and Case 2004), and may capture a general episodic memory factor that surpasses specific task demands (Jonaitis et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), providing a more holistic picture of superageing. An individual maintaining superior performance across word lists, paragraph recall, and visual memory tests likely represents a more robust superageing phenotype than a person excelling at only one format. Composite scores also address the ceiling effects that can occur in high-functioning older adults on individual tests, providing greater discriminative power in the superior performance range. Moreover, composite scores better reflect real-world memory demands, which rarely involve pure word-list learning but rather integrate verbal, visual, and contextual information (Tulving \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, these benefits need to be weighed against the loss of mechanistic specificity; composite scores can obscure whether superior performance reflects globally preserved memory systems or compensatory strengths in specific domains masking deficits in others. Relatedly, they could be skewed by different age-related trajectories across cognitive functions, such as the steeper decline in episodic memory compared to other domains and between individuals (Lin et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Pudas et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR166\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComposite score use could also reflect convenience or availability rather than active choice, such as in large-scale cohorts like UK Biobank or Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI), which are not specifically designed to study superageing. The ADNI episodic memory composite score (Crane et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) combines RAVLT (trials 1\u0026ndash;5, 30-minute delayed recall, and recognition), Logical Memory (immediate and delayed), and select items from the ADAS-Cog. This exemplifies a composite score mixing retrieval formats and cognitive domains, which could potentially illuminate differential performance if sub-scales are provided. Therefore, where possible, studies should report both composite scores and individual components, allowing the field to determine whether superageing represents uniform preservation across all episodic memory processes or selective resilience in specific domains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Organisation of information used\u003c/h2\u003e \u003cp\u003eThe internal structure of to-be-remembered material \u0026ndash; whether semantically organised or unrelated \u0026ndash;, places different demands on encoding strategies, executive control, and semantic processing abilities, thereby altering which cognitive processes drive superior performance. The most commonly used test, the RAVLT (almost a third of studies in our review; 21 out of 78 used it as the sole criterion), consists of 15 unrelated words that do not permit traditional semantic clustering strategies, although participants may employ alternative organisational approaches such as serial clustering or individually devised strategies (Baldo and Shimamura \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In contrast, tests like the CVLT and HVLT present words from distinct semantic categories (e.g., fruits, tools, animals), explicitly enabling semantic clustering strategies during encoding and retrieval (Sun et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The Free and Cued Selective Reminding Test (FCSRT) takes this approach further by providing semantic category cues during the learning phase, promoting reliance on semantic processing and cue-target associations during encoding (Baldo and Shimamura \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Tasks like the FCSRT therefore leverage controlled encoding and semantic cues to distinguish genuine storage deficits from retrieval failures, probing hippocampal-dependent associative binding.\u003c/p\u003e \u003cp\u003eThese differences modulate task cognitive demands, potentially identifying superagers who excel through different underlying mechanisms. When tested on semantically organised material (e.g., CVLT), superagers showed significantly higher semantic clustering scores than typical older adults, and matched young adults, indicating greater use of executive control strategies leveraging semantic relationships among words (Sun et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tremont et al. \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This contrasts with findings from RAVLT-based studies, where executive function and attention composite scores predict approximately 20% of variance in superager episodic memory performance (Cook Maher et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The reliance on executive control for successful performance might be driven by the RAVLT's unrelated word structure, which precludes semantic strategies, and forces superior performers to develop alternative organisational approaches (Baldo and Shimamura \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis leads to the final organisational difference: standalone words versus short stories. While most studies employing verbal learning tests use word lists, others use paragraph recall tasks such as Logical Memory from the Wechsler Memory Scales (Baran and Lin 2018; Harrison et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Saloner et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Story-based tests provide narrative structure, semantic coherence, and contextual relationships that fundamentally differ from word list learning (Tremont et al. \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Stories enable participants to leverage discourse comprehension, existing schemas, and meaningful connections between ideas \u0026ndash; cognitive processes that may be less vulnerable to ageing or represent different aspects of preserved function. Consequently, individuals meeting superager criteria through superior story recall might rely on intact language comprehension and narrative processing abilities, while those excelling at word lists might demonstrate superior associative binding or strategic organisational skills.\u003c/p\u003e \u003cp\u003eCombined, these findings suggest that semantically organised tests may identify a phenotypic feature of superageing reflecting excellence in strategic encoding processes, while unrelated word lists may capture individuals with superior raw binding capacity or ability to generate organisational strategies. An individual meeting superager criterion through superior semantic clustering on the CVLT might show average performance level on the RAVLT if their advantage is derived from strategic semantic processing. Conversely, a superager identified based on RAVLT performance might not leverage semantic structure effectively on category-based tests, potentially reflecting qualitatively different cognitive mechanisms underlying superageing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Considerations for episodic memory assessment\u003c/h2\u003e \u003cp\u003eA significant step forward for superageing research would be establishing a consistent operational definition that enables meaningful comparison while preserving the ability to capture qualitatively different forms of successful ageing. One potential approach is establishing a multi-format assessment battery that includes both verbal and visuospatial memory tests and different retrieval formats. Scoring should involve individual components (for normative comparisons) and composite scores, allowing researchers to determine whether superior performance reflects globally preserved memory systems or selective strengths masking specific deficits.\u003c/p\u003e \u003cp\u003eAdditionally, future research could incorporate process-level analyses to understand how superagers maintain superior memory function. While standardised neuropsychological tests are needed for normative data, they could be complemented with experimental paradigms that directly probe underlying cognitive processes. For example, process dissociation or remember/know procedures could distinguish recollection-based from familiarity-based recognition advantages, while source memory (Josefsson et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Dennis et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), mnemonic similarity discrimination (Stark et al. \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Frank et al. 2020), and associative binding (Yonelinas \u003cspan citationid=\"CR168\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) tasks could isolate hippocampal-dependent functions from broader strategic abilities. Importantly, experimental paradigms could also extend brain imaging studies beyond brain-behaviour correlations by introducing cognitive manipulations that enable investigation of neural mechanisms. For example, task-based neuroimaging using different memory processes could reveal whether superagers show distinct patterns of neural recruitment (Katsumi et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, multi-format assessment batteries incorporating experimental paradigms beyond standard neuropsychological tasks are rarely available for existing cohorts studying superageing. Consequently, the episodic memory measures employed in the current literature remain informative for understanding cognitive mechanisms. However, as highlighted in this section, careful consideration is required when interpreting and comparing findings, as each approach carries distinct strengths and limitations.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Longitudinal Trajectory of Episodic Memory","content":"\u003cp\u003eResearch on successful episodic memory ageing can be divided into studies that incorporate longitudinal trajectory of episodic memory into their definition and those that do not. Of the 78 studies reviewed, approximately 70% (54 studies) employed a cross-sectional design, while the remaining 30% (24 studies) adopted a longitudinal approach (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among cross-sectional studies, about half set an age cut-off at 80 years or older (Borelli et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Calandri et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Harrison et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), while the remainder included participants below this threshold (Park et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, only a minority of longitudinal studies (7 out of 24) focused on participants over 80 years (Cook et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Garo-Pascual et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hoenig et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with the majority examining younger samples (Baran and Lin 2018; Gardener et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pudas et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A key consideration in longitudinal designs is the duration of follow-up, which ranges from 18 months (Cook Maher et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to two decades (Josefsson et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe choice between cross-sectional and longitudinal designs in selection criteria is critical for conceptualising and interpreting mechanisms of successful episodic memory ageing. As Nyberg (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) notes, a high episodic memory score at a single time point does not necessarily reflect preserved function over time. Older adults with strong baseline performance may follow divergent trajectories, either sustained high performance into advanced age or subsequent decline. This variability is evident in studies where cross-sectionally defined superagers did not always maintain their status at follow-up. Reported proportions of stable superagers range widely, from 10.9% over three years in a cohort defined at \u0026ge;\u0026thinsp;80 years (Cervenkova et al. 2020) to 60% over three years in a cohort defined at \u0026ge;\u0026thinsp;75 years (Dekhtyar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Such discrepancies underscore limited understanding of factors supporting the maintenance of episodic memory in ageing.\u003c/p\u003e \u003cp\u003eCross-sectional definitions assume that high episodic memory performance at a single time point suffices to identify superagers. This approach is sensitive to age cut-offs and may include individuals with high cognitive reserve who are already undergoing preclinical dementia, characterised by delayed yet rapid cognitive decline (Stern \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Stern et al. \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). When criteria are applied close to the age at which episodic memory decline typically begins, cross-sectional studies are more likely to capture individuals who had inherently high episodic memory capacity. By contrast, longitudinal designs that track performance across the ageing process are better suited to identify those resistant to age-related decline. However, cross-sectional studies remain valuable for efficiently characterising neural signatures, genetic profiles, and cognitive mechanisms associated with different superager phenotypes. They also enable systematic comparison of assessment approaches to identify overlapping versus distinct cognitive profiles, providing essential foundational knowledge for designing targeted longitudinal investigations.\u003c/p\u003e \u003cp\u003eAdopting a longitudinal perspective in defining successful episodic memory ageing raises important considerations about the heterogeneity of identified populations. Cross-sectional definitions \u0026ndash; especially when applied to younger cohorts of older adults \u0026ndash; are more likely to capture heterogeneous groups, encompassing individuals who may or may not maintain high performance over time. While no consensus exists on optimal longitudinal follow-up duration, longer observation periods provide stronger evidence of stability and reduce interpretative uncertainty. This does not negate the value of cross-sectional approaches, which can shed light on why some high performers subsequently decline while others maintain optimal episodic memory function with age.\u003c/p\u003e"},{"header":"7. Non-memory Assessment","content":"\u003cp\u003eAlthough episodic memory is the core neuropsychological criterion in defining superagers, and the main source of heterogeneity (\u003cem\u003esee Sections 4\u0026ndash;6\u003c/em\u003e), an important question arises regarding other cognitive abilities: should these be expected to fall within the normal range or also be supernormal? Some studies do not consider performance in other cognitive domains beyond demonstrating the lack of pathology by reaching thresholds on standard cognitive tests used as a screening tool for cognitive decline, e.g. MMSE (Maccora et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Others require superagers to perform \u003cem\u003ewithin\u003c/em\u003e the normal range for their age in cognitive tasks assessing other cognitive domains, such as attention, processing speed, or executive function (Harrison et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), while some include participants who perform within or \u003cem\u003eabove\u003c/em\u003e the normal range for their age (Gefen et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Another approach includes participants that happen to be higher performers in other cognitive domains (Baran and Lin 2018). To complicate matters further, some studies (Maccora et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) include maintenance of superior cognition across time as part of the definition, adding a longitudinal dimension to the criteria. This section addresses the variability in including additional cognitive domains in the superager definition and its implications.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e7.1. Cognitive architecture in superageing\u003c/h2\u003e \u003cp\u003eThe choice of non-memory cognitive criteria to define superageing is far from trivial, as it reflects an underlying theoretical stance on cognitive architecture \u0026ndash; particularly the role of general cognitive ability, or the \u003cem\u003eg\u003c/em\u003e factor. Performance on a broad range of cognitive tests tends to correlate, and this shared variance is commonly attributed to the \u003cem\u003eg\u003c/em\u003e factor (Spearman \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e1904\u003c/span\u003e). The \u003cem\u003eg\u003c/em\u003e factor, thought to capture an individual\u0026rsquo;s general capacity to reason, learn, and adapt across diverse tasks, accounts for a substantial proportion of variance in cognitive performance. Its relevance for studying superageing lies in the fact that episodic memory is itself moderately to strongly correlated with \u003cem\u003eg\u003c/em\u003e, and age-related declines in episodic memory are frequently paralleled by declines in other \u003cem\u003eg\u003c/em\u003e-related domains such as processing speed and executive function (Glisky et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Harada et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hertzog et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Schwarz et al. \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tucker-Drob et al. \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Crawford \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Zaninotto et al. 2018; Ghisletta et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Thus, whether superior memory performance in older adults reflects domain-specific preservation of episodic memory systems, or broader resilience of general cognitive ability (Cook Maher et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), remains a central question for understanding the cognitive architecture of superageing.\u003c/p\u003e \u003cp\u003eUnderlying the \u003cem\u003eg\u003c/em\u003e factor framework is the assumption that cognitive abilities are fundamentally interrelated and draw on shared resources, reflecting a unitary component of intelligence. As outlined in Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e5.3\u003c/span\u003e, basic processes such as attention are core components of memory. The relationship between attention and memory is complex, as attentional control not only affects encoding efficiency but also interacts with other cognitive processes, such as working memory and inhibitory control, to influence memory outcomes (Cowan et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Long et al. 2018; Sherman et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Another fundamental component is processing speed, often measured using tasks such as the Trail Making Test or digit-symbol substitution. Processing speed constrains the efficiency of both encoding and retrieval (Luo and Craik 2008; Salthouse \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and age-related slowing can disproportionately affect memory performance (Lee et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Levitt et al. 2006). Alongside processing speed, working memory, which provides a workspace for temporarily maintaining and manipulating information, is strongly associated with individual differences in episodic memory in ageing (Korkki et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), including with the preserved performance seen in superagers (Cook Maher et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nyberg et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to this assumption of interdependence, modular accounts of cognition (Fodor \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) propose that the cognitive architecture consists of specialised modules that operate relatively independently. Each module is dedicated to processing a particular type of information, thus supporting cognitive flexibility and allowing the preservation of function in the event of localised brain injury. This has implications for how we conceptualise superageing and interpret its underlying neural structures. If we consider functions as particularly modular, then each function, and the brain regions supporting it may follow different developmental and decline trajectories from other functions. Therefore, superagers may be better defined as excelling in one function, like episodic memory, as the other functions may show differential decline. If episodic memory is treated as an isolated function, the focus tends to fall narrowly on medial temporal lobe regions, particularly the hippocampus. By contrast, when episodic memory is viewed in relation to its component processes such as attentional control, working memory, or processing speed, a wider set of brain regions becomes relevant (Moscovitch et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), highlighting broader, network-level contributions. These definitional choices therefore shape the neural \u0026lsquo;signatures\u0026rsquo; that emerge. How superageing is defined therefore implicitly aligns with one or the other of these assumptions, privileging either a domain-general account of preserved cognition or a domain-specific, modular interpretation, with the unintended consequence of shaping and constraining the interpretation of study findings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e7.2. Considerations for non-memory assessments\u003c/h2\u003e \u003cp\u003eAs suggested in Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e5.4\u003c/span\u003e, to understand the basis of superior memory in superagers, studies would benefit from incorporating process-level analyses. These analyses are designed to capture the strategies and intermediate mechanisms underlying episodic memory performance, such as monitoring learning curves across repeated trials, identifying error patterns (e.g., intrusions or false alarms), or measuring reliance on recollection versus familiarity. However, in the frequent case where episodic memory cannot be studied with specialised experimental paradigms (as in big cohort studies), information from performance on other standardised cognitive tasks becomes invaluable. Standardised neuropsychological measures of core functions such as attention, processing speed, and working memory enable the identification of specific cognitive, and possibly neural, mechanisms that support preserved memory in ageing. Ultimately, this strategy may help explain why some older adults maintain exceptional memory performance.\u003c/p\u003e \u003cp\u003eAn additional consideration concerns how cognitive abilities beyond episodic memory are characterised over time. Do they follow a similar course to episodic memory, or are there distinct trajectories for each domain (Hartshorne and Germine \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Whitley et al. \u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)? Tracking changes across multiple domains allows researchers to determine whether episodic memory preservation occurs in isolation or as part of a more general pattern of cognitive resilience. Integrating these considerations \u0026ndash; episodic memory, other cognitive skills, longitudinal trajectories, and reference group selection, is critical for defining superagers in a way that is both conceptually meaningful and methodologically rigorous. This methodological variability complicates cross-study comparisons and may underlie apparent inconsistencies in the literature.\u003c/p\u003e \u003cp\u003eThere are also practical reasons to foreground other cognitive functions in studying superageing. The field is largely motivated by the aim of identifying mechanisms of resistance to Alzheimer\u0026rsquo;s disease, which has positioned episodic memory as a central focus. However, this emphasis may be too narrow, as decline is observed in other cognitive functions like executive function (Dubbelman et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Restricting assessment to episodic memory may overlook broader patterns of cognitive preservation or vulnerability that are relevant for understanding resistance to neurodegenerative pathology. Tasks assessing additional cognitive domains can provide valuable complementary information. By broadening the scope of cognitive assessment in superageing research, studies can more accurately characterise the cognitive profile associated with resistance to Alzheimer disease and offer insights into how cognitive health could be maintained.\u003c/p\u003e \u003c/div\u003e"},{"header":"8. Diversity in Ageing Populations","content":"\u003cp\u003eThis section examines the extent to which the superageing literature reflects the global ageing landscape and its diversity. Geographically, according to the United Nations World Population Prospects, the global population aged over 65 in 2023 was estimated at 808.37\u0026nbsp;million, with Asia representing the largest proportion (469.40\u0026nbsp;million, over 58% of the total; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) (Ritchie and Roser 2019). In contrast, 62% (48 out of 78) of cohorts studied in this field were from the United States (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), highlighting a clear geographical bias.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePopulation diversity, however, extends beyond geographical bias. Even within highly represented countries, study samples tend to be skewed toward highly educated, urban, and generally healthy individuals, frequently excluding social or ethnic minorities. For instance, according to the United Nations, the average number of years of schooling among adults aged over 25 years in the United States in 2023 was 13.9 (UNDP (United Nations Development Programme) \u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In contrast, in the majority of studies reviewed based on United States cohorts, average education levels were higher than the general population. Notably, at least 25% of these studies involved highly educated participants, with mean years of education exceeding 17 (Harrison et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hoenig et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Only three studies reported lower average educational attainment than the national mean, all of which applied the superager framework to populations living with human immunodeficiency virus (HIV) (Saloner et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis observation highlights another limitation in the field, as most superageing studies overlook the fact that morbidity in old age is common (Salive \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and tend to over-represent exceptionally healthy participants. Nevertheless, some exceptions exist. For instance, superager studies have been conducted among adults living with HIV (Saloner et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e) and among newly diagnosed patients with Parkinson\u0026rsquo;s disease (Uribe-Kirby et al. \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although these studies included participants aged 50 years and above, slightly younger than most cohorts in the field, their cognitive performance was compared with that of healthy individuals aged 25 years, and they still identified superagers. These findings reinforce the notion that successful episodic memory ageing can coexist with systemic infection or even early stages of neurodegenerative disease. A further example concerns the urban-rural distribution of the populations screened using the superager paradigm. In the 27 European Union member states, most adults aged 65 years or over live in rural areas (Eurostat \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), yet participants in these cohorts are typically recruited from major cities. Overall, not only does the diversity of ageing cohorts studied within the superageing paradigm remain limited, but these groups may not closely reflect the characteristics of the average ageing population.\u003c/p\u003e \u003cp\u003eExtending this research field to underrepresented populations is crucial for fully understanding the phenotype of superagers. Investigating diverse populations allows comprehensive evaluation of the multifactorial determinants of brain health, including genetic and exposome factors, which vary geographically and across socio-economic status (Baez et al. 2023; Greene et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Resende et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Only through such an inclusive approach can we identify the whole range of protective factors that superagers can reveal against age-related or pathological decline of episodic memory.\u003c/p\u003e \u003cp\u003eFactors contributing to human diversity in ageing populations modulate cognitive and brain ageing. For instance, older brain ages have been reported in Latin American and Caribbean countries compared with the Global North, and in societies with greater socioeconomic inequality (Moguilner et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Faster ageing has likewise been observed in sociodemographic groups with shorter lifespans (Balachandran et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Even in Switzerland, a country with one of the highest Human Development Index values in the world (United Nations \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), individuals exposed to socioeconomic disadvantage from childhood to adulthood aged 10% faster than those with consistently advantageous backgrounds (Schrempft et al. \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These differences concern population-level effects rather than individual variability, as superagers often exhibit slower brain ageing, evidenced by younger estimated brain ages relative to typical peers (Gaser et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Park et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, although recent studies suggest that higher educational attainment does not influence the rate of memory decline over time (Fjell et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; L\u0026ouml;vd\u0026eacute;n et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Seblova et al. \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) nor affects age-sensitive cortical regions or hippocampal atrophy (Nyberg et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the relationship between education level and episodic memory age-related decline or brain ageing trajectories remains controversial (Li et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Steffener \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, the influence of education on baseline memory performance is well established, with higher educational attainment consistently associated with superior episodic memory performance at baseline (Fjell et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Glymour et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schneeweis et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reflected in the common practice of adjusting normative cognitive test values according to educational level (Delis et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Magalh\u0026atilde;es and Hamdan 2010; Pe\u0026ntilde;a-Casanova et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApplying the superager selection criteria to more diverse populations may require adaptations (Rajah et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Variations in life expectancy, educational attainment, and socio-economic context should be considered when adapting elements of the superager criteria, such as age thresholds and neuropsychological assessment tests. Regarding age cut-offs, the same chronological age to define the target population in countries with markedly different disability level results in groups with distinct characteristics (Sudharsanan and Bloom \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For this reason, determining age thresholds based on remaining life expectancy has been proposed as a more accurate proxy for a population\u0026rsquo;s functional status than chronological age alone (Riffe et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As highlighted in Section 3, this threshold may need adjustment according to population characteristics, with suggestions to lower it by around five years in developing countries to improve comparability (Borelli et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and better align the target population. Most neuropsychological assessments were developed for highly educated, English-speaking Western populations, limiting their applicability elsewhere (Alladi and Hachinski 2018; Parra et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As raised in Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e5.1\u003c/span\u003e, most tests used to evaluate episodic memory in the superageing literature are verbal learning tasks that depend on language ability and are influenced by educational level (Fjell et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Glymour et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schneeweis et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This reliance may pose a barrier when assessing individuals with limited formal education (Borelli et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pellicer-Espinosa and D\u0026iacute;az-Orueta 2022). Non-verbal memory tests could be an alternative for populations with lower educational backgrounds, although such tests are not entirely independent of educational level (Rosselli and Ardila 2003). Consequently, although heterogeneous selection criteria complicate integrating findings across studies, a one-size-fits-all approach is inappropriate when seeking to enhance our understanding of superageing in an increasingly diverse ageing population.\u003c/p\u003e"},{"header":"9. Conclusions","content":"\u003cp\u003eThis scoping review examines the divergent criteria used to define superagers within the episodic memory ageing literature, based on the heterogeneity identified through a systematic search of MEDLINE and Scopus, which yielded 78 articles. The review identified as the main sources of heterogeneity in superager selection criteria: i. the age range of the target sample; ii. the specific tests used to assess episodic memory; iii. the benchmarks applied to episodic memory performance to distinguish successful from typical ageing; iv. whether longitudinal trajectories of episodic memory ability are incorporated into the definition; and v. whether non-memory cognitive abilities are included as additional criteria. The findings indicate that the combinations of these factors are nearly unique to each cohort or study, thereby maximising the number of possible definitions and increasing overall heterogeneity in the field. This variability makes the cross-study comparison of results challenging. The review discusses the implications of these definitional variations to facilitate the interpretation of findings, as studies may be reporting outcomes that are only loosely comparable.\u003c/p\u003e \u003cp\u003eHeterogeneity in the superager selection criteria should not be viewed as a flaw in the field. Focusing on this variability is not intended to impose a preferred criterion or establish a hierarchy among definitions. Rather, this diversity enriches our understanding of successful episodic memory ageing by providing multiple perspectives and represents a necessary avenue for advancing the field. Such heterogeneity is not solely determined by study design. In some cases, it arises from the need to extend these criteria to diverse ageing populations. For instance, to determine whether the superager phenomenon occurs across different sociodemographic conditions, it may be necessary to adapt assessment tools or criteria, for example, to ensure their relevance for populations with lower levels of formal education.\u003c/p\u003e \u003cp\u003eFinally, this review offers a conceptual framework to guide future research. It can facilitate cross-study comparisons, highlight gaps in the literature, and support selection of appropriate criteria for specific cohort designs by clarifying which questions can and cannot be addressed. The framework also aids readers in interpreting existing findings, promoting a more coherent understanding of superageing across studies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of criteria defining the superager group across the reviewed studies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory assessment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBenchmark memory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLongitudinal memory criterion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-memory asessement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSample size superager group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaran et al., 2018(Baran and Lin 2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: RAVLT, ADAS-Cog and Logical Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: finite mixture modeling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigit Span backwards score, semantic fluency, TMT-A, TMT-B and the Clock Drawing Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatra et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Batra et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFull Scale IQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorelli et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e(Borelli et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50-65-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalandri et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e(Calandri et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArgentina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score \u0026gt; -1 SD of 50-60-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervenkova et al., 2020(Cervenkova et al. 2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCzech Republic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 60-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e(Chen et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: RAVLT, ADAS-Cog and Logical Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: finite mixture modeling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigit Span backwards score, semantic fluency, TMT-A, TMT-B and the Clock Drawing Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCook et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e(Cook et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCook Maher et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e(Cook Maher et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50-65-year-old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCook Maher et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e(Cook Maher et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 56\u0026ndash;64 year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e(Dang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCVLT: long delayed free recall score (second edition)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 30-44-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigit Symbol Substitution Test, Victoria Stroop Test words score, Digit Span, lexical fluency and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e(Dang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCVLT: long delayed free recall score (second edition)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 30-44-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigit Symbol Substitution Test, Victoria Stroop Test words score, Digit Span, lexical fluency and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ede Godoy et al., 2021(de Godoy et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50-60-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigit Span forward and backward score, BNT, TMT-A, TMT-B, RCFT, semantic and lexical fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ede Godoy et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e(de Godoy et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50-60-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigit Span forward and backward score, BNT, TMT-A, TMT-B, RCFT, semantic and lexical fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ede Souza et al., \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2022\u003c/span\u003e(de Souza et al. \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: memory score\u0026thinsp;\u0026gt;\u0026thinsp;1.5 SD for age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDekhtyar et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e(Dekhtyar et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, 20217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: MCT delayed free and cued recall score, FNAME delayed recall and SRT delayed recall and delayed multiple choice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: memory composite\u0026thinsp;\u0026ge;\u0026thinsp;0.5 SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiamond et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Diamond et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominguez et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e(Dominguez et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNACC cohort: WMS Logical Memory IIA delayed recall (revised); 90\u0026thinsp;+\u0026thinsp;cohort: CVLT long delayed free recall score (short form)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: NACC cohort, top 50th percentile; 90\u0026thinsp;+\u0026thinsp;cohort, at or above the top 50th percentile for their age group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNACC cohort: TMT-B; 90\u0026thinsp;+\u0026thinsp;cohort: TMT-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNACC cohort\u0026thinsp;=\u0026thinsp;105; 90\u0026thinsp;+\u0026thinsp;cohort\u0026thinsp;=\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDominguez et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Dominguez et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADNI cohort: WMS Logical Memory IIA delayed recall (revised); 90\u0026thinsp;+\u0026thinsp;cohort: CVLT long delayed free recall score (short form)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: ADNI cohort, top 50th percentile; 90\u0026thinsp;+\u0026thinsp;cohort, at or above the top 50th percentile for their age group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eADNI cohort: TMT-B; 90\u0026thinsp;+\u0026thinsp;cohort: TMT-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eADNI cohort\u0026thinsp;=\u0026thinsp;58; 90\u0026thinsp;+\u0026thinsp;cohort\u0026thinsp;=\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoyle et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Doyle et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePuerto Rico and United States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWord List: immediate and delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;median of 55-64-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePREHCO cohort\u0026thinsp;=\u0026thinsp;45; HRS cohort\u0026thinsp;=\u0026thinsp;31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngelmeyer et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e(Engelmeyer et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFili et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Fili et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: prospective memory, pairs matching memory, fluid intelligence and reaction time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: Optimal Labeling with Bayesian Optimization (OLBO) in-house algorithm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFili et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e(Fili et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: prospective memory, pairs matching memory, fluid intelligence and reaction time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: Optimal Cognitive Scoring (OptiCS) (in-house algorithm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGardener et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e(Gardener et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCVLT: long delayed free recall score (second edition)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 30-44-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLogical Memory, RCFT delayed score, Stroop speed of colors/speed of dots ratio, Digit Span, Digit Symbol Coding, Controlled Oral Word Association Task, semantic fluency, and BNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaro-Pascual et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e(Garo-Pascual et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;79.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFCSRT: delayed free recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50-56-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, digit symbol substitution test and semantic fluency test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaro-Pascual et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Garo-Pascual et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;79.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFCSRT: delayed free recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50-56-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, digit symbol substitution test and semantic fluency test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGefen et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e(Gefen et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGefen et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e(Gefen et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGefen et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e(Gefen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGefen et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e(Gefen et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50-60-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGefen et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e(Gefen et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarrison et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e(Harrison et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarrison et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e(Harrison et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCVLT: long delayed free recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 18-32-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTMT-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarrison et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Harrison et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: RAVLT, ADAS-Cog and Logical Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: memory composite slope\u0026thinsp;\u0026ge;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHermansen et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Hermansen et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u0026ndash;95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: fluency test, digits forward test, digits backward test, immediate recall test and delayed recall test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: cognitive composite\u0026thinsp;\u0026gt;\u0026thinsp;mean of 50-60-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1905 birth cohort\u0026thinsp;=\u0026thinsp;27; 1915 birth cohort\u0026thinsp;=\u0026thinsp;33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoenig et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e(Hoenig et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: RAVLT, ADAS-Cog and Logical Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: memory composite z-score\u0026thinsp;\u0026gt;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuentelman et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e(Huentelman et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50-65-year-old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJaneczek et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e(Janeczek et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJia et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e(Jia et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: memory score\u0026thinsp;\u0026gt;\u0026thinsp;1 SD for age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTMT-A, TMT-B, BNT and WAIS-III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJosefsson et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e(Josefsson et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026ndash;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: immediate free recall of 16 imperative verb\u0026ndash;noun sentences enacted by participant, delayed cued recall of nouns from enacted sentences, immediate free recall of 16 verb\u0026ndash;noun sentences verbally and visually presented, delayed cued recall of nouns from the previously presented sentences and immediate free recall of 12 verbally presented nouns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: memory composite\u0026thinsp;\u0026gt;\u0026thinsp;1 SD from the estimated average score for age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJosefsson et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e(Josefsson et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026ndash;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: immediate free recall of 16 imperative verb\u0026ndash;noun sentences enacted by participant, delayed cued recall of nouns from enacted sentences, immediate free recall of 16 verb\u0026ndash;noun sentences verbally and visually presented, delayed cued recall of nouns from the previously presented sentences and immediate free recall of 12 verbally presented nouns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: memory composite\u0026thinsp;\u0026gt;\u0026thinsp;1 SD from the estimated average score for age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKarpouzian-Rogers et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e(Karpouzian-Rogers et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50-65-year-old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKatsumi et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e(Katsumi et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCVLT: long delayed free recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 18-32-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTMT-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKatsumi et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e(Katsumi et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHVLT: long delayed free recall score (revised)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 16-29-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTMT-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKeenan et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Keenan et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: immediate and forgetting score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 20-29-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTMT-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKim et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e(Kim et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: SVLT delayed recall score and RCFT delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory composite\u0026thinsp;\u0026ge;\u0026thinsp;mean of 45-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKim et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Kim et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: SVLT delayed recall score and RCFT delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory composite\u0026thinsp;\u0026ge;\u0026thinsp;mean of 45-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSeoul Neuropsychological Screening Battery-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKopeček et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e(Kopeček et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCzech Republic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean 60-64-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLin et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e(Lin et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: RAVLT, ADAS-Cog and Logical Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: finite mixture modeling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigit Span backwards score, semantic fluency, TMT-A, TMT-B and the Clock Drawing Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLin et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e(Lin et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: RAVLT, ADAS-Cog and Logical Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: memory composite Z-score\u0026thinsp;\u0026gt;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLin et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Lin et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCERAD W-L: three immediate and delayed score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026gt;\u0026thinsp;mean of 60-64-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSemantic fluency test and digit symbol substitution test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaccora et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e(Maccora et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCVLT: immediate and delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;median of participants in the study\u0026rsquo;s 20s cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMapstone et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e(Mapstone et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: learning, retrieval and recognition score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: memory Z-score\u0026thinsp;\u0026gt;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigit Span forward and backward score, TMT-A and TMT-B, BNT and Hooper Visual Organization Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMcPhee et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2025\u003c/span\u003e(McPhee et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States and Canada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u0026ndash;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFNA: associative recognition score (hit rate minus false alarm rate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50-69-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpatial Working Memory, Stroop interference task and Letter-Number Alternations task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMohammadiarvejeh et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Mohammadiarvejeh et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: prospective memory, pairs matching memory, fluid intelligence and reaction time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: principal component analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNassif et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2022\u003c/span\u003e(Nassif et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePark et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2022\u003c/span\u003e(Park et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: SVLT delayed recall score and RCFT delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory composite\u0026thinsp;\u0026ge;\u0026thinsp;mean of 45-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigit Span, BNT, Controlled Oral Word Association Test, Color Word Stroop Test, Digit Symbol Coding and TMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePark et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2025\u003c/span\u003e(Park et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: SVLT delayed recall score and RCFT delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory composite\u0026thinsp;\u0026ge;\u0026thinsp;mean of 45-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigit Span, BNT, Controlled Oral Word Association Test, Color Word Stroop Test, Digit Symbol Coding and TMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePetkus et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2021\u003c/span\u003e(Petkus et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u0026ndash;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: CVLT immediate recall scores and long delayed recall score (modified), BVRT number of errors, Digit Span forward and backward scores, Card Rotations Test, phonemic test and semantic verbal fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: latent class analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePezzoli et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Pezzoli et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: CVLT short delayed free recall score, CVLT long delayed free recall score, Visual reproduction I and II, Logical Memory total Score, and Verbal Paired Associates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: definition (1): cognitive age gap (cognitive predicted age - chronological age) derived from cognitive composite at lowest 20th percentile; definition (2): memory composite at top 20% for their age; definition (3): non-memory cognition composite at 80th percentile for their age; definition (4): CVLT long delayed free recall\u0026thinsp;\u0026ge;\u0026thinsp;mean of 18-32-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStroop in 60 seconds, Digit Symbol, TMT-A, TMT-A subtracted from TMT-B (Trails B\u0026ndash;A), Digit Span Backward, Animal Naming, and Vegetable Naming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePezzoli et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2025\u003c/span\u003e(Pezzoli et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: CVLT free total (trials 1\u0026ndash;5) recall score, short delayed cued recall score, long delayed cued recall score, TMT-A and TMT-B, Stroop test, verbal fluency (FAS test), Animal Naming, Vegetable Naming, Digit Symbol, Logical Memory total recall, Visual Reproduction I, II and recognition total, Digit Span forward and backward scores, and BNT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: cognitive age gap (cognitive predicted age - chronological age) derived from cognitive composite\u0026thinsp;\u0026lt;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003enot specified\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePudas et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2013\u003c/span\u003e(Pudas et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u0026ndash;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: immediate free recall of 16 imperative verb\u0026ndash;noun sentences enacted by participant, delayed cued recall of nouns from enacted sentences, immediate free recall of 16 verb\u0026ndash;noun sentences verbally and visually presented, delayed cued recall of nouns from the previously presented sentences and immediate free recall of 12 verbally presented nouns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: memory composite\u0026thinsp;\u0026gt;\u0026thinsp;1 SD from the estimated average score for age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNot applied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRogalski et al., \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2013\u003c/span\u003e(Rogalski et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRogalski et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2019\u003c/span\u003e(Rogalski et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50\u0026ndash;60 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaliasi et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2015\u003c/span\u003e(Saliasi et al. \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNetherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: phonemic fluency tests, semantic fluency tests, Digit Span forward and backward scores, TMT-A, ratio TMT-(B/A), immediate recall score, delayed recall score and response speed score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: graph theory approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaloner et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2019\u003c/span\u003e(Saloner et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: semantic fluency, lexical fluency, Paced Auditory Serial Addition Task, WAIS\u0026ndash;III Letter-Number Sequencing, WMS-III Spatial Span, WAIS\u0026ndash;III Digit Symbol, WAIS\u0026ndash;III Symbol Search, TMT-A, Stroop Color and Word Test Color Score, Wisconsin Card Sorting Test-64, Perseverative Errors, TMT-B, Stroop Color \u0026amp; Word Test Interference Score, Halstead Category Test, HVLT total learning score (revised), Brief Visuospatial Memory Test (revised), Total Learning, Story Memory Test Learning, Figure Memory Test Learning, HVLT delayed recall score (revised), Brief Visuospatial Memory Test delayed recall score (revised), Story Memory Test delayed recall score, Figure Memory Test delayed recall score and Grooved Pegboard Test dominant and non-dominant hand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: cognitive composite within 1 SD of 25-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaloner et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e(Saloner et al. \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: semantic fluency, lexical fluency, Paced Auditory Serial Addition Task, WAIS\u0026ndash;III Letter-Number Sequencing, WMS-III Spatial Span, WAIS\u0026ndash;III Digit Symbol, WAIS\u0026ndash;III Symbol Search, TMT-A, Stroop Color and Word Test Color Score, Wisconsin Card Sorting Test-64, Perseverative Errors, TMT-B, Stroop Color \u0026amp; Word Test Interference Score, Halstead Category Test, HVLT total learning score (revised), Brief Visuospatial Memory Test (revised), Total Learning, Story Memory Test Learning, Figure Memory Test Learning, HVLT delayed recall score (revised), Brief Visuospatial Memory Test delayed recall score (revised), Story Memory Test delayed recall score, Figure Memory Test delayed recall score and Grooved Pegboard Test dominant and non-dominant hand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: cognitive composite within 1 SD of 25-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaloner et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e(Saloner et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: semantic fluency, lexical fluency, Paced Auditory Serial Addition Task, WAIS\u0026ndash;III Letter-Number Sequencing, WMS-III Spatial Span, WAIS\u0026ndash;III Digit Symbol, WAIS\u0026ndash;III Symbol Search, TMT-A, Stroop Color and Word Test Color Score, Wisconsin Card Sorting Test-64, Perseverative Errors, TMT-B, Stroop Color \u0026amp; Word Test Interference Score, Halstead Category Test, HVLT total learning score (revised), Brief Visuospatial Memory Test (revised), Total Learning, Story Memory Test Learning, Figure Memory Test Learning, HVLT delayed recall score (revised), Brief Visuospatial Memory Test delayed recall score (revised), Story Memory Test delayed recall score, Figure Memory Test delayed recall score and Grooved Pegboard Test dominant and non-dominant hand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: cognitive composite within 1 SD of 25-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpencer et al., \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2022\u003c/span\u003e(Spencer et al. \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRAVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 50-65-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSun et al., \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e(Sun et al. \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCVLT: long delayed free recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 18-32-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTMT-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTicha et al., \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2023\u003c/span\u003e(Ticha et al. \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCzech Republic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePVLT: delayed recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean 60-64-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBNT, TMT-B and semantic fluency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrammell et al., \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2024\u003c/span\u003e(Trammell et al. \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCraft Story: delayed recall score (version 21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score within 1 SD of 50-60-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTMT-B, lexical fluency and the Multilingual Naming Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUribe-Kirby et al., \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e2025\u003c/span\u003e(Uribe-Kirby et al. \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultiple sites in United States and Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u0026ndash;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: Letter-Number Sequence, Symbol Digit Modalities Test, semantic fluency, HVLT immediate and delayed verbal recall score, and Judgment of Line Orientation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: cognitive composite\u0026thinsp;\u0026ge;\u0026thinsp;0.5 SD of 25-year-olds on \u0026ge;\u0026thinsp;3 tests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang et al., \u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e2019\u003c/span\u003e(Wang et al. \u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMemory composite: RAVLT, ADAS-Cog and Logical Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: finite mixture modeling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDigit Span backwards score, semantic fluency, TMT-A, TMT-B and the Clock Drawing Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYu et al., \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2020\u003c/span\u003e(Yu et al. \u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingapore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: RBANS consisting of 12 subtests that assess immediate memory, delayed memory, language, attention and visuospatial construction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge-Peer: cognitive composite in \u0026ge;\u0026thinsp;one domain according to age norms (index scores\u0026thinsp;\u0026ge;\u0026thinsp;115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZammit et al., \u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e2018\u003c/span\u003e(Zammit et al. \u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: FCSRT free recall score, Logical Memory, semantic fluency, BNT, Digit Span, TMT-A, TMT-B, Digit Symbol Coding, Block Design and Controlled Oral Word Fluency Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: latent class analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZammit et al., \u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e2020\u003c/span\u003e(Zammit et al. \u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.3\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: Logical Memory total score, Word List recall, BNT, semantic fluency, Digit Span forward and backward scores, Digit Ordering, Matrices and Line Orientation, Symbol Digits Modalities Test and Number Composition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: latent transition analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZammit et al., \u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e2021\u003c/span\u003e(Zammit et al. \u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.3\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive composite: Word List memory, recall and recognition scores, Story Recall immediate and delayed scores, Logical Memory I and II, BNT, Verbal Fluency, Reading Test, Digit Span forward and backward score, Digit Ordering, Symbol Digits Modalities Test, Number Comparison, Stroop color naming and word reading, Judgment Line Orientation, Standard Progressive Matrices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutomatic: latent class analysis and time-varying effects models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMemory and non-memory tests were combined into a cognitive composite, as indicated in the Memory Assessment column\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang et al., \u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e2020\u003c/span\u003e(Zhang et al. \u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCVLT: long delayed free recall score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYounger: memory score\u0026thinsp;\u0026ge;\u0026thinsp;mean of 18-32-year-olds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTMT-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBenchmark memory refer to age-specific norms when applicable and details regarding gender or education adjustments are available in the original articles. The Mini-Mental State Examination (MMSE) was excluded from assessment counts as it is considered a cognitive screening tool. Memory assessment and non-memory assessment classification was based solely on specific memory and non-memory cognitive tests.\u003c/p\u003e \u003cp\u003e \u003cem\u003eADAS-Cog, Alzheimer's Disease Assessment Scale Cognitive Subscale; BNT, Boston Naming Test; BVRT, Benton Visual Retention Test; CERAD W-L, Consortium to Establish a Registry for Alzheimer's Disease Word List Memory Task; CVLT, California Verbal Learning Test; FCSRT, Free and Cued Selective Reminding Test; FNA, Face Name Association; FNAME, Face Name Associative Memory Exam; HVLT, Hopkins Verbal Learning Test; MCT, Memory Capacity Test; PVLT, Philadelphia Verbal Learning Test; RAVLT, Rey Auditory Verbal Learning Test; RBANS, Repeatable Battery for the Assessment of Neuropsychological Status; RCFT, Rey-Osterrieth Complex Figure Test; SD, standard deviation; SRT, Selective Reminding Test; SVLT, Seoul Verbal Learning Test; TMT-A, Trail Making Test part A; TMT-B, Trail Making Test part B; WAIS\u0026ndash;III, Wechsler Adult Intelligence Scale (third edition) and WMS, Wechsler Memory Scale.\u003c/em\u003e \u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eCompeting interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions:\u003c/h2\u003e \u003cp\u003eMGP and BD contributed to the conceptualisation of the study, MGP, DF and CR contributed to the investigation performing the literature search and review, MGP, DF and CR drafted the original manuscript, MGP, DF, CR and BD review and edited the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eB.D. is supported by the Swiss National Science Foundation (project grant no. 32003B_212466, 32NE30_221732, 33IC30_213595 and CRSII5_209510), InnoSuisse Flagship Swiss brAInHealth project, ERA_NET NEURON JTC2023-ELSA: BrainTree projects. D.F. is supported by a Royal Society University Research Fellowship (URF/R1/241499)\u003c/p\u003e \u003cp\u003eWe thank Alexia Candal-Z\u0026uuml;rcher for providing valuable clinical perspective for this study, and the inAGE laboratory members for their helpful feedback on the study visuals.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlladi S, and Vladimir Hachinski (2018) Neurology 91(6):264\u0026ndash;270. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/WNL.0000000000005941\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000005941\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u0026lsquo;World Dementia: One Approach Does Not Fit All\u0026rsquo;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrade GS, Wiezel PF, Amer Cavalheiro H (2023) Instruments for the Assessment of SuperAgers: A Systematic Review. 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Older individuals with episodic memory exceeding typical age-related performance are often termed \u0026lsquo;superagers\u0026rsquo;. Previous studies have used varying age ranges, reference benchmarks to distinguish successful from typical ageing or memory assessment tasks. Despite major advances in the field, the marked heterogeneity in defining superageing can hinder progress if not considered when interpreting results.\u003c/p\u003e \u003cp\u003eWe conducted a scoping review, using systematic searches of MEDLINE and Scopus, identifying 78 eligible studies. This review investigates the main sources of variability in superager definition across the following domains: age criteria, episodic memory criteria, and other cognitive criteria. We demonstrate how variation in each domain alters the composition of the target group and the implications for interpreting cognitive and neural mechanisms of superageing.\u003c/p\u003e \u003cp\u003eRather than establishing a hierarchy of definitions, we propose a conceptual framework to facilitate cross-study comparison and identify gaps in the literature. Understanding the implications of each selection criterion will enhance the interpretability of findings and accelerate insights into the superageing phenotype, ultimately contributing to better understanding of healthy episodic memory ageing.\u003c/p\u003e","manuscriptTitle":"Defining ‘Successful’ Episodic Memory Ageing: Implications of Methodological Heterogeneity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 06:53:51","doi":"10.21203/rs.3.rs-8916228/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"faf657a0-6fdd-47de-8609-a580a346bcce","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":63242252,"name":"Cognitive Neuroscience"}],"tags":[],"updatedAt":"2026-05-07T13:43:59+00:00","versionOfRecord":{"articleIdentity":"rs-8916228","link":"https://doi.org/10.1016/j.neubiorev.2026.106707","journal":{"identity":"neuroscience-and-biobehavioral-reviews","isVorOnly":true,"title":"Neuroscience \u0026 Biobehavioral Reviews"},"publishedOn":"2026-04-28 00:00:00","publishedOnDateReadable":"April 28th, 2026"},"versionCreatedAt":"2026-02-20 06:53:51","video":"","vorDoi":"10.1016/j.neubiorev.2026.106707","vorDoiUrl":"https://doi.org/10.1016/j.neubiorev.2026.106707","workflowStages":[]},"version":"v1","identity":"rs-8916228","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8916228","identity":"rs-8916228","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}