A resilience framework for understanding cognitive aging implies a search for factors that buffer against existing risk, enabling one to thrive in what might otherwise be adverse circumstances. The cascade of biological processes associated with senescence and a cultural context that does not take into account this biological imperative each create risk for cognitive decline in later adulthood. We propose that (a) engagement, a sustained investment in mental stimulation, and (b) personal agency, which enables one to construct a niche for successful life span development, constitute the centerpiece of cognitive resilience. Numerous factors at the level of the individual and the sociocultural context set the stage for engagement and agency, thereby contributing to life span cognitive resilience, which can in turn impact factors promoting engagement and agency (e.g., health management, disposition affecting how experience in regulated) to support cognitive growth. Cognitive development shows wide variation among individuals through the adult life span, and there is long-standing concern with why some age more successfully than others. Our goal in this chapter is to explore the nature of such cognitive resilience through adulthood.
Historically, the concept of resilience arose in the child development literature as a framework to understand why some children who grow up under circumstances of great adversity, nevertheless, thrive (Masten & Wright, 2010). Thus, framing successful aging in terms of resilience puts the emphasis on the factors that protect against risks associated with aging, so that an understanding of resilience requires an analysis of both the threats to successful development and protective factors. We take for granted that both developmental continuity (e.g., Evans & Schamberg, 2009) and plasticity (e.g., Hertzog, Kramer, Wilson, & Lindenberger, 2008) are powerful forces of development, with threats and protective factors that buffer against those threats defining a lifelong resilience process. Senescence, the biological process of aging, certainly circumscribes limits on cognitive components requiring speeded information processing and executive control in later life, and increases vulnerability to pathological processes that can compromise cognitive health.
However, as a natural part of the life cycle, senescence itself is not so much a threat to successful development as is a cultural context that does accommodate this developmental period of later life. Cultural threats might be viewed as of two forms. First, it is widely recognized that attitudes about aging can be internalized in the form of negative aging stereotypes, which can compromise cognition by discouraging the recruitment of effort to the task at hand (Hess, Auman, Colcombe, & Rahhal, 2003). Beyond that, however, cultural institutions and social structures can also be a threat to successful development to the extent that they do not afford resources to prepare people to live long lives (Riley & Riley, 2000). As we will detail in this review, protective factors are diverse, but we take the organizing principle of cognitive resilience to be twofold: (a) engagement, a sustained investment in experiences that enrich mental capacity (Stine-Morrow, 2007), and (b) agency, the ability to create a niche to support such an investment amidst changing circumstances (Hertzog & Jopp, 2010; Werner, 1995).
Thus, lifelong cognitive resilience depends on the capacity to adapt to internal factors (e.g., senescence) and external factors (e.g., stressors, culture-bound expectations) to maintain habits of personally satisfying mental engagement. Agency in sustaining an engaged lifestyle does not just derive from naive optimism (e.g., the “little engine that could”), but rather from a whole constellation of resources crafted over the life span that puts force behind ones sense of agency (e.g., Infurna, Gerstorf, Ram, Schupp, & Wagner, 2011). Cognitive capacity does not come for free. By some estimates, proficiency in a substantive skill requires about 10,000 hours of deliberate practice (Ericsson, Krampe, Tesch-Römer, 1993; Gladwell, 2008). The normalization of optimal life span cognitive development, then, will ultimately derive from cultural and social institutions (e.g., health care, educational resources) that position individuals for effective engagement in experiences and activities that nurture cognition on a large scale over extended time. In the pages that follow, we consider the factors that have potential to contribute to cognitive resilience through the life span.
The ” 10,000-hour rule” implies that not all skills will be developed to an equal extent and that cognitive resilience must entail selectivity in what is optimized, as well as compensatory strategies for managing activities that depend on nonoptimized skills. Because plasticity decreases with age, the “10,000-hour rule” might be expected to become something like a “15,000-hour rule” for new skills developed in late life. However, the 10,000-hour rule also implies that by mid-to-later adulthood, investment in skill development of various sorts creates selected areas of established proficiencies, so that expanding on existing skill may depend on more like a “5,000-hour rule.”
Nevertheless, this scale of investment requires a life context that permits such a commitment. As such, developmental forces of selection, optimization, and compensation (SOC) become increasingly important to resilience through adulthood (Baltes, 1997). At the same time, resilience is a sociocultural process as well, insofar as affordances for adaptation are co-constructed by the individual and the sociocultural context in which one is embedded (Baltes, Reuter-Lorenz, & Rosier, 2006). We consider a number of broad factors that contribute to processes of life span cognitive resilience: (a) health; (b) education and cognitive reserve; (c) knowledge; (d) lifelong intellectual engagement; (e) dispositions, temperament, and motivational reserve; (0 social support; and (g) sociocultural context.
A burgeoning literature has emerged in recent years suggesting that a healthy mind requires a healthy body Health is not simply the absence of disease, but rather a coordinated system of regulatory capacities that afford wellness.
Among the most consistently demonstrated health effects on cognition is that of physical activity (Hillman, Erickson, & Kramer, 2008). For example, in a meta-analysis of intervention studies in which late middle-aged and older adults were randomly assigned to an exercise condition (either aerobic or aerobic combined with strength training) or a control condition, Colcombe and Kramer (2003) showed that change in the exercise group from pretest to posttest (effect size = .49) was reliably greater than change in the control group (effect size = .16). Exercise was found to improve an array of cognitive components, but the strongest of the effects was on executive function (effect size = .68), which was robust even for interventions of a relatively modest duration (1-3 months). Effects of aerobic conditioning on cognition were found to be somewhat greater when it was augmented with strength training.
There is much recent research activity devoted to understanding the mechanisms underlying these effects using both animal and human models (Hillman et al., 2008). It appears that aerobic activity exerts its effects on mental capacity via a number of biochemical pathways that enhance neurogenesis, angiogenesis, and functional architecture of the brain. For example, improved aerobic fitness has been related to the expansion of gray matter in the prefrontal and temporal regions, changes that are related to improvements in performance. One of the most reliable effects of exercise is increased growth and survival of cells in the dentate gyrus of the hippocampus, a brain structure essential for memory consolidation. This cell growth is supported by the growth of vasculature needed for transport of nutrients and stimulated by increased upregulation of brain-derived neurotropic factor (BDNF).
fMRI data suggest that physically fit individuals exert more top-down control to avoid response conflict and show different patterns of neural recruitment (more middle frontal gyrus and superior parietal, but less activation of the anterior cingulated) that support better selective attention performance. Collectively, then, fitness engendered by aerobic exercise is an important facet of cognitive resilience. Importantly, these effects begin very early in the life span (Hillman et al., 2008), with math and reading achievement showing strong relationships with aerobic capacity. So although the Colcombe and Kramer metaanalysis shows that increased fitness can be a powerful cognitive intervention in later life, it is likely that building habits of exercise in childhood and young adulthood is a source of lifelong cognitive resilience.
Maintain a Healthy Weight
There is a fair amount of empirical support for a link between maintaining a healthy weight and cognitive resilience. For example, using longitudinal data from the Swedish Adoption/Twin Study of Aging, Dahl et al. (2010) showed that, controlling for education, cardiovascular disease (CVD), smoking, and alcohol use, the body mass index (BMI) at midlife was predictive of cognitive decline into later adulthood. This relationship was obtained whether or not individuals who were diagnosed with dementia during the study were included for analysis. The causal mechanisms of the weight-cognition link remain unclear. Obesity rarely occurs alone, but rather in conjunction with other factors that compromise health. The term “metabolic syndrome” is used to characterize the clustering of symptoms – including abdominal obesity (in which fat tissue is disproportionately distributed around the abdomen), cholesterol disorders, hypertension, and insulin resistance – that collectively increase the risk of CVD and type II diabetes.
Diabetes, which has been shown to predict declines in speed of processing and memory performance (Elias, Elias, Sullivan, Wolf, & D’Agostino, 2005; Ryan, 2005), may affect brain health through a variety of mechanisms, such as disrupting neurotransmitter pathways and transport of glucose across the blood-brain barrier. Based on a review of population-based prospective studies, Hao, Wu, Wang, and Liu (2011) concluded that metabolic syndrome was predictive of later cognitive declines, but did not increase the risk of Alzheimer’s disease (AD). Gatto et al. (2008) compared groups of postmenopausal women with and without metabolic syndrome who were screened to be free of CVD and diabetes.
Those without metabolic syndrome had an advantage on a measure of global cognition (an effect that could not have been attributable to cardiovascular health or absence of diabetes). Based on an analysis of longitudinal data from the Lothian Birth Cohort Study, Corley Gow, Starr, and Deary (2010) concluded that the correlation between BMI and cognition could be accounted for in terms of socioeconomic status and early life cognition without any direct effect of weight on cognition. Given the neurobiological correlates of obesity and related metabolic effects, a dismissal of a causal link between maintaining a healthy weight and good cognitive functioning is probably premature. Their data do suggest, however, that advantages early in life set up patterns of self-regulation through the life span that play out in both higher levels of cognitive functioning and better weight management.
We are designed to adapt to challenging circumstance. Our physiology is wired for “fight or flight” as needed for adaptation to changing situations. This capacity to dynamically adjust to external demands, of course, has survival value, but unchecked chronic stress is toxic (Oitzl, Champagne, van der Veen, & de Kloet, 2010). Preparation to deal with a stressor involves the release of Cortisol that impacts carbohydrate metabolism to release energy reserves, suppresses the immune system, and affects cognitive function by both suppressing processing of information that is not relevant to the situation and promoting memory consolidation – all effects that enable coping with challenge in the short run. However, chronic exposure to Cortisol sets up a cascade of processes that can self-perpetuate damage to neurons, especially in the hippocampus. Ones emotional response to challenging situations may moderate the stress response.
Although some individuals respond to challenge with positive affect, some may be more likely to perceive challenge as threatening and react with negative affect. Such a disposition (typically characterized as neuroticism, as we will detail in the following text) may exacerbate the damaging effects of stress on cognition. Neupert, Mroczek, and Spiro (2008) analyzed diary reports of participants from the Normative Aging Study over 8 days and found that reports of stress were coupled with reports of memory failures, but that this effect was heightened for those higher in neuro ticism. There is empirical evidence for the long-term cost of stress on cognition earlier in the life span.
Evans and Schamberg (2009) showed that the link between childhood poverty and working memory capacity in adolescence could be entirely accounted for with a composite measure reflecting the cumulative physiological cost of stress (“allostatic load”), including resting blood pressure and urinary Cortisol. Research with animal models (Oitzl et al., 2010) suggests that vulnerability to stress and the development of buffers against the negative effects of stress are likely lifelong processes. Thus, the development of strategies for managing stress is an important source of cognitive resilience.
Sleep can play an important role in protecting cognition through adulthood. It has long been known that insomnia predicts poorer cognitive performance, but it is also the case that even minor sleep disturbances in otherwise healthy, community-dwelling elders can negatively impact cognition. Nebes, Buysse, Halligan, Houck, and Monk (2009) found that individuals with better sleep quality (e.g., who fell asleep more quickly and were able to stay asleep) performed significantly better on measures of working memory, abstract problem solving, and executive control. Variation in sleep quality did not significantly relate to speed of processing or inhibition, suggesting that sleep specifically protects the ability to sustain focus in complex tasks. Day-to-day variation in sleep can impact cognitive performance as well. Gamaldo, Allaire, and Whitfield (2010) assessed sleep and cognitive performance on 8 different days across a period of 2-3 weeks, and showed that withinindividual deviations (either more or less) away from one’s mean level of sleep (in this sample, about 6 hours) was coupled with relatively poorer cognition the next day.
The causal mechanisms for this relationship are unclear. Although it seems entirely plausible that variations in sleep could directly impact cognition, it is also the case that (as noted previously) daily stress co varies with daily cognitive performance, so it may also be that stress is a third variable that compromises both cognition and sleep. Collectively, the empirical literature suggests that consistency in good quality sleep is an important source of cognitive resilience. Interestingly, poor sleepers often have higher resting levels of Cortisol, so it is probably the case that good sleep and managing stress are inevitably linked in protecting cognition.
Although there is some evidence that moderate levels of alcohol consumption can be favorable to cognition, the empirical case is somewhat stronger that avoiding excessive alcohol consumption is even better (Gross et al., 201 1). Gross et al. used data from the Johns Hopkins Precursors Study, a longitudinal study of medical students into middle age and later adulthood to prospectively examine the effects of alcohol consumption on cognition in old age. They showed that regardless of the time point at which alcohol use was measured, it was a negative predictor of phonemic fluency, a measure of executive control. For example, beyond about 15 drinks per week, alcohol consumption was a negative predictor of phonemic fluency 12 years later.
This brief review suggests that the effects of physical health on cognition are diverse. Health and wellness likely impact cognition in a number of ways, including direct biochemical and neural pathways that enhance plasticity, and indirect pathways of enhanced capacity to sustain engagement and agency in cognitively challenging situation. Cognitive capacity can also impact health, so that cognition and health may sustain one another through reciprocal causation. For example, cognitive capacity appears to buffer the impact of stress on affect (Stawski, Almeida, Lachman, Tun, Rosnick, 2010); also, cognitive capacity can be an important resource for continued management of health and wellness (Morrow & Durso, 2011).
EDUCATION AND COGNITIVE RESERVE
An important factor contributing to lifelong resilience in cognition is an extended period of engagement in formal education early in the life span, an effect that has been attributed to “cognitive reserve” (Stern, 2009). The explanation is that early educational experiences, when brain and behavior are at their maximum potential for plasticity, build neural networks and behavioral strategies that buffer against subsequent insults, so that the manifestation of brain pathology or damage is delayed. Approximately a quarter of community-dwelling individuals who show no obvious performance impairments before death will show evidence of brain pathology at autopsy. This proportion is greater for individuals with higher educational levels than it is for lower levels of education, suggesting that education builds a reserve, in terms of efficiency of neural networks, capacity, and/or flexibility in the use of networks or strategies that enable individuals with incipient pathology to recruit this reserve to preserve function.
A number of studies have shown that more highly educated individuals tend to be diagnosed with AD at later ages than less educated adults, but once diagnosed, their cognitive decline is more precipitous. Also, data from the Nun Study, in which a number of long-term lifestyle factors are controlled, have suggested that the rate of AD is lower among those with more years of formal education early in the life span (Mortimer, Snowdon, & Markesbery, 2003). However, based on a large sample from the Canberra Longitudinal Study, Batterham, Mackinnon, and Christensen (2011) concluded that this may depend on the particular cognitive domain assessed, with only speed of processing showing a delayed change point and slightly accelerated decline with increasing education (and not global cognitive status or memory).
One particular sort of early educational experience that shows evidence of wide-ranging effects on cognition is learning a second language (Bialystok, Craik, Green, & Gollan, 2009). Although early studies of bilingualism focused on aspects of delayed acquisition in childhood, this was a misconception that was a consequence of neglecting the combined acquisition (e.g., vocabulary) of both languages. More recent research suggests strong advantages of bilingualism for cognitive resilience. Globalization has contributed to an acceleration of research on the cognitive processes underlying bilingualism and multhingualism, with much of this literature showing lifelong benefits for language and thought. For example, the experience of negotiating two languages early in the life span gives the bilingual child an early window into the insight that one object can have more than one name, and by extension, that the description of events can depend on the observer.
There is much evidence that bilinguals communicating in their second language, nevertheless activate features of their first language, which must be suppressed in order to effectively manage the target language. This lifelong practice with flexible switching between two language systems, and controlling interference between the two, exercises executive control on a routine basis. In fact, older bilinguals show reduced decline on measures of executive control relative to monolinguals, and multhinguals appear to show a further advantage still. Collectively, educational experiences early in the life span impact cognitive resilience via a number of routes. Education builds a cognitive and neural reserve that buffers late-life pathology, but also affords skills and regulatory capacities that engender continue engagement.
Knowledge developed throughout the life span is a key resource for resilience in cognition. The growth of knowledge occurs in multiple arenas. Verbal ability, including vocabulary knowledge and proceduralized skills in reading, can show positive development into adulthood with continued practice in literacy activities. Domain knowledge continues to develop with continued investment in occupational and avocational activities. Such particularized knowledge can be complex and build a reservoir of declarative knowledge that can provide a context through which to assimilate new information, as well as skills that engender both effective selection and greater efficiency in domain-related learning (Miller, 2009).
COGNITIVE STIMULATION AND INTELLECTUAL ENGAGEMENT
The aphorism to “use it or lose it” has become a commonplace, and in fact, there is a well-replicated relationship between a lifestyle that incorporates engagement in intellectually stimulating activity and level of cognitive ability (e.g., Hultsch, Hertzog, Small, & Dixon, 1999; Jopp & Hertzog, 2007; Kemper, Greiner, Marquis, Prenovost, & Mitzner, 2001; Parisi, Stine-Morrow, Noh, & Morrow, 2009; Schooler, Mulatu, & Oates, 2004; Verghese et al, 2003). Intellectual stimulation has been assessed in myriad ways, including complex work or leisure activities, and frequency of participation in novel activities. Many of these studies provide interesting data consistent with the idea that habits of intellectual engagement can buffer age-related declines, with demonstrations of a crosssectional or a prospective correlation. There are two difficulties with drawing firm conclusions about causation, however.
First, if an intellectually stimulating lifestyle really acts as a buffer, one might expect for age declines or age differences to be reduced among those who are more intellectually active (statistically, an age by experience interaction), but there is actually little evidence for this. Rather, intellectually active individuals (either measured as disposition or self-reported activity) often have a cognitive advantage over inactive individuals that is sustained over the life span, but they do not age better (Salthouse, 2006). However, assuming that the senescence process places some constraints on the developmental trajectory of cognition (Hertzog et al., 2008), the expectation for differential cognitive growth among intellectually active people throughout the life span may set a bar for evidence that is too high.
A more serious concern with drawing causal conclusions from these studies is that they are vulnerable to the interpretation that those who are cognitively impaired may differentially withdraw from activity, so that it is the decline in mental capacity that leads to withdrawal from cognitive activity, rather than the reverse. Rohwedder and Willis (2010) took a clever approach to addressing this issue by comparing cognitive scores cross-nationally as a function of retirement policies – over which individuals have minimal direct control. To do this analysis, they took advantage of data from three cross-national surveys that were collaboratively designed to provide comparable assessments: the Health and Retirement Study in the United States; the English Longitudinal Study of Aging; and the Survey of Health, Ageing, and Retirement in Europe, which collected data from 11 European countries.
Surveys were based on large nationally representative samples and administered over the phone. The cognitive assessment incorporated into the larger survey was delayed recall for 10 concrete nouns, a task that very often shows reliable age declines in the literature. Results showed that individuals in countries that had policies incentivizing early retirement (e.g., by taxing earned income at a higher rate) had steeper declines in memory between the early 50s and early 60s. Thus, even though correlational, the relationship between engagement in work and mental decline reported by Rohwedder and Willis strongly implies that the mental demands of work promote cognitive resilience. Another way to address the causal ambiguity of the correlational literature is to conduct experiments in which participants are randomly assigned to some condition that promotes intellectual stimulation or to a control.
Although there is a rich history in the psychology of aging examining the effects of cognitive interventions, a long-standing focus on ability- specific training studies has been expanded in recent years to include more lifestyle interventions (Stine-Morrow & Basak, 2011). In short, the training literature has been clear in showing that there is substantial neural and behavioral plasticity into very late life, but the effects of training are highly specific to the ability trained, with no transfer to even factorial-related abilities. Lifestyle interventions embed individuals in complex environments in which multiple abilities may be exercised and/or in which individuals can shape the way they response to challenges through selective use of different abilities. There is some evidence that engagement with video games that require strategic reasoning can augment executive control among older adults (Basak, Boot, Voss, & Kramer, 2008). Community-based programs also show promise.
The Experience Corps program that places older adults in schools to work with children directly or as support staff has shown evidence of enhancing cognition (Carlson et al., 2008). The Senior Odyssey program in which older adults engage in team -based creative problem solving geared toward tournament competition has also shown evidence of improving cognition (Stine-Morrow, Parisi, Morrow, & Park, 2008). In spite of the robust of effects of early education on late-life cognition documented in an earlier section, it is possible that a lifestyle of continued cognitive stimulation can trump early experience. For example, data from the Midlife in the United States (MIDUS) study suggest that the negative effects of early impoverished educational experiences on episodic memory can to some extent be offset by frequent engagement in cognitive activities, such as literacy activities and puzzles (Lachman, Agrigoroaei, Murphy, & Tun, 2010).
Investment in activities that push the boundaries on ones abilities can be exhilarating. To the extent that one builds cognitive capacity through such patterns of engagement, one would expect cognitive capacity to be maintained or continue to grow, which makes subsequent encounters with cognitive challenge more pleasurable. In fact, there is evidence that older adults with higher levels of cognitive ability derive more pleasure with more cogniti vely challenging activities, whereas adults with lower levels of abilities enjoy less challenging activities (Payne, Jackson, N oh, & Stine-Morrow, 2011). Thus, lifestyle habits of intellectual engagement may be self-perpetuating.
DISPOSITION, TEMPERAMENT, AND MOTIVATIONAL RESERVE
Aside from the particular habits of intellectual engagement that are likely to build behavioral and brain reserve, there may be certain aspects of disposition and temperament that can impact the value of ordinary experience as an avenue for cognitive enrichment, as well as engendering or inhibiting stimulating behavioral repertoires. A rich literature is developing, which examines interrelationships between cognition and personality traits (Duberstein et al., 2011). For example, openness to experience – a trait marked by enjoyment of novelty, fantasy, and emotional experience; attunement to the environment; and mental flexibility – has been shown in a number of studies to be related to measures of cognitive performance (Parisi et al., 2009; Soubelet & Salthouse, 2010, 2011), as well as to reduced risk of AD (Duberstein et al.). This is perhaps not that surprising inasmuch as habitual enjoyment with intellectual activity would presumably enhance routine engagement of cognitive capacities to incorporate mental exercise into everyday activities, thereby building intellectual capacity. In fact, there is evidence that those who are high in the intellect facet of openness recruit more neural resources during a working memory task (De Young, Shamosh, Green, Braver, & Gray, 2009). By contrast, neuroticism, a tendency to worry and to feel anxious and threatened in ordinary situations, has been hypothesized to be a risk factor for cognitive impairment. Such thought patterns – of course – are likely to create distraction from the intellectual aspects of experience, but also neuroticism is related to higher levels of production of Cortisol, which as noted earlier is a stress hormone known to damage the hippocampus. Neuroticism has been shown to be a risk factor for AD (Duberstein et al., 2011).
However, evidence for a negative relationship between neuroticism and cognitive function in a healthy sample has been mixed (e.g., Soubelet & Salthouse , 201 1), and the effect of neuroticism on cognition may depend on its context in the larger structure of personality (Crowe, Andel, Pedersen, Fratiglioni, & Gatz, 2006). Belief that one is an active agent in effecting outcomes in the world has a profound effect on how the mind works. In a clever demonstration of this principle, Rigoni, Kühn, Sartori, and Brass (201 1) measured event-related potentials for undergraduates as they performed a volitional motor task after being randomly assigned to either an experimental condition to weaken the belief in free will (subjects read Cricks argument that free will is an illusion) or a control condition that did not (they read another passage from the same book about consciousness).
The early component of the readiness potential, a negative-going wave that precedes the conscious experience of the intention to move was reliably reduced among those whose beliefs in free will were challenged, a finding the investigators interpreted as indicating a reduced effort to formulating the intention to perform the motor act. In fact, a number of dispositional factors related to motivation have been related to cognition. Beliefs that one can influence the events in ones life and confidence that the investment of effort will pay off in performance gains, conceptualized as self-efficacy and perceived control (Lachman, 2006; Valenti] n, et al., 2006), have often been shown to predict performance and have been targeted for intervention (West, Bagwell, & Dark-Freudeman, 2008). Such beliefs are an important source of cognitive resilience insofar as they underpin effective allocation of effort to activities that support and sustain cognition.
For example, Payne and colleagues (2011) showed that self-efficacy at pretest predicted perseverance in a 1 6-week program of reasoning training as well as actual improvement in reasoning ability as a consequence of the training. Even as a resource for cognitive resilience, such beliefs are likely to be constructed over the life span (Forstmeier & Maercker, 2008; Infurna et al., 2011). For example, Lachman and Leff (1989) have found fluid ability to predict perceived control 5 years later. Based on data from the German Socio-Economie Panel, a large-scale longitudinal study with a nationally representative sample, Infurna and colleagues (2011) showed that level of social participation contributed to control beliefs measured 8-10 years later. Forstmeier and Maercker (2008) have coined the term “motivational reserve,” arguing that, analogous to cognitive reserve, motivational resources that underpin cognition and health constitute a set of abilities that buffer age-related neuropathological insults. Using the Occupational Information Network (O* N ET) system to characterize work- related activities in midlife and relate the motivational reserve developed by these activities to current cognitive status.
Forstmeier and Maercker coded jobs for motivational demands (e.g., the need to develop goals and action plans for achieving them) separately from cognitive demands (e.g., the ability to concentrate on a task for a long time without distraction). Although cognitive demands were correlated with a measure of premorbid intelligence, motivational demands were not, which is the basis for the claim that motivational reserve is a construct that is separable from cognitive reserve in contributing to cognitive resilience. They found that, controlling for the cognitive demands of work, motivational reserve was predictive of cognitive status, as measured by a composite of processing speed, working memory, fluency, and inhibitory control. One might question whether motivational reserve, as measured in this study, is truly independent of cognitive reserve. However, the notion that motivation itself may be a skill that can be developed through adulthood and targeted for intervention is an interesting one.
Resilience is a broad concept that is not simply a trait of an individual. Resilience has been conceptualized as a dynamic system that arises from processes and interactions beyond the boundaries of the individual (Masten & Wright, 2010). Social engagement is an essential part of the dynamic system that enables positive adaptation when an individual confronts challenges or threat and has been widely studied in light of its impact on health in general (Berkman, Glass, Brissette, & Seeman, 2000). For example, social and community ties have been found to be associated with mortality and the occurrence of dementia (Fratiglioni, Wang, Ericsson, Maytan, & Winblad, 2000). We will focus on how social engagement may have an impact on individuals’ adaptation in the cognitive domain. Social engagement can be characterized along a number of distinct dimensions (Krueger et al., 2009). Social network size is the number of people with which an individual has significant contact.
Social activity is the engagement in experiences that involve other people. Social support is the subjective evaluation of the quality of social relations as warm, affirming, and a resource for help in times of stress. Social engagement may be increasingly important for individuals’ adaptation with age (Charles & Carstensen, 2010). According to socioemotional selectivity theory (SST), with increasing age comes recognition of a more limited temporal horizon, which stimulates a shift in priorities. Although knowledge acquisition is adaptive when the temporal horizon is expansive, as time becomes more limited, individuals prioritize emotionally meaningful experiences, and thus, substantive social engagement. Consequently, with aging, emotional satisfaction and meaning from close interpersonal relationships (i.e., social support) are expected to have priority over opportunities for information gain that would be more likely to be engendered by large social networks.
Based on a nonclinical cross-sectional sample of community-dwelling older adults, Krueger et al. (2009) found that, controlling for age, sex, and education, both social support and social activity were positively associated with global cognitive functioning. In contrast, consistent with SST, social network size was not. In addition, a recent study showed that more frequent social activity was associated with reduced rates of decline in episodic memory, semantic memory, working memory, perceptual speed, and visuospatial ability over 5 years (James, Wilson, & Barnes, 2011). In a national sample of older adults, social integration (measured in terms of marital status, contact with social partners, and volunteering) was found to be associated with a reduced memory decline in a 6-year period (Ertel, Glymour, & Berkman, 2008). In this study, the measure of social integration appeared to reflect a combination of social network size and social activity, leaving open the possibility that social network size contributed to the reduced memory decline observed in this study.
Bennett et al. (2006) found that the association between cognitive functioning and indices of brain pathology measured postmortem at autopsy was reduced among those with larger social networks, even controlling for levels of cognitive and social activity. Such data suggest that social networks can act as a sort of cognitive reserve that cannot be explained by cognitive stimulation or social activity. Interestingly, in Bennetts measure of social networks, participants were asked to include people with whom they interacted frequently and with whom they felt comfortable and could call on for help, suggesting that social support may have been a source of reserve. A review of empirical findings leads to the conclusion that social context is an important source of cognitive reserve. Older adults tend to regulate the social environment to prioritize interactions with close ties, and thus social support, and derive a sense of well-being through this process. However, it may also be the case that complex social networks, social activity, or some combination with social support will also contribute to cognitive resilience.
Thus, further study is needed to tease apart the different facets of social context that have direct and indirect impact on cognitive performance in older adults. An obvious concern with drawing conclusions about a protective effect of social engagement on cognition from the correlational data is whether low social activity puts individuals at a higher risk of memory decline or whether poor memory causes individuals to withdraw from social activities. Capitalizing on the availability of longitudinal data, two studies (Ertel et al., 2008; James et al., 201 1) showed that cognitive function at baseline could not explain the observed memory decline. Similarly, another longitudinal study (Lovdén, Ghisletta, & Lindenberger, 2005) showed that prior scores of social activity influenced perceptual speed in older adults 8 years later. A test of reverse causation showed that perceptual speed did not influence social activity. Taken together, the empirical evidence suggests that social engagement serves as a resource to enable older adults’ optimal cognitive functioning. However, more research is needed to isolate the mechanism(s) of how social engagement impacts cognitive functioning.
Sociocultural context includes the social institutions and cultural practices that offer opportunities for cognitive engagement (e.g., by defining roles) and influence access to resources that impact cognition (e.g., social communities, health care, technology). Resilience at the cultural level has been increasingly discussed (Zautra, Hall, & Murray, 2010) since Baltes (1997) argued that life span psychology research has largely ignored cultural and historical context. In particular, the co-cons true tionist approach emphasizes the interactions between biological and cultural evolution (Baltes et al., 2006; Li, 2003). On the one hand, individuals navigate the sociocultural milieu, negotiate the resources needed for their development, and thereby shape the culture. On the other hand, communities and cultures adapt to individual needs (Hall & Zautra, 2010). Some cultures are more able than others to mobilize existing resources to buffer the effects of stress at the individual level (Zautra et al., 2010). Few studies have investigated sociocultural effects on cognition, though there are emerging examples (e.g., Carlson et al., 2008).
Recent evidence from neuroimaging studies also showed differences in neurocognitive processes, neural activation patterns, and neural structures between individuals from individualistic and collective cultures (Park & Huang, 2010). Individualistic (Western) cultures that value individual achievement appear to wire brains so that processing is biased toward processing central objects, whereas collectivistic (Eastern) cultures that place more value on collaboration and interdependence appear to wire brains for more holistic processing. These are interesting and provocative findings with more research needed to address whether other culture-related attributes might contribute to this adaptation. In the cognitive domain, educational institutions are infrastructures that communities and cultures provide that have direct effects on individuals’ cognitive performance. At the individual level, the effect of education can be readily revealed by examining the behavioral and neurological differences in literate and illiterate individuals within cultures that distribute opportunities for literacy development arbitrarily (Reis & Castro-Caldas, 1997; Reis, Petersson, CastroCaldas, & Ingvar, 2001).
At the sociocultural level, educational institutions shape who has access to literacy and how opportunities for cognitive enrichment are distributed. The opportunities for formal education also contribute to the levels of literacy that one is likely to encounter among individuals who will form one’s social network. In turn, literacy in the social network impacts individuals’ intellectual activities such as reading. Opportunities for learning may be organized differently across social groups with lasting effects. For instance, experience with racial segregation at school age can have a lifelong impact on cognitive performance. Focusing on the effect of desegregation, Whitfield and Wiggins (2003) found that older African Americans who attended desegregated schools ultimately achieved more years of education than those who attended segregated schools. In addition, after controlling for age, gender, and years of education, African Americans who attended desegregated schools showed better vocabulary and spatial ability, compared to those who attended segregated schools.
However, Allaire and Whitfield (2004) reported that age declines in cognitive ability (including inductive reasoning and speed) were found only among those who had experienced desegregation. Also, educational attainment had more of a positive impact on cognition in later adulthood among those who had attended segregated schools. The authors speculate that the experience of racism, as well as the quality of education, in the early efforts at desegregation may explain these effects. The differential impact of segregation on cognitive ability and declines in cognitive ability seem to suggest resilience in older adults who experienced less than optimal education experiences early in life. Although attending segregated schools might lead to fewer years of education and lower level of cognitive ability, individuals who attended segregated schools seem to be better able to retain cognitive skills with age. These results, although complex, hint at the profound lifelong impact of a sociocultural context that affords truly equal opportunities for access to education.
CONCLUSIONS AND IMPLICATIONS FOR RESEARCH AND PRACTICE
Cognitive resilience is a multidimensional process in which resources assembled through the life span buffer against late life threats to cognitive health (Lachman & Agrigoroaei, 2010). In our view, the nexus of the resilience process is a sense of personal agency, engendered by an array of individual factors (e.g., health; cognitive and motivational reserve), social factors (e.g., social support), and sociocultural factors (e.g., social equity, effective structures for life span education), that nurture sustained intellectual engagement. Certainly, all of these elements contribute to the individual capacity for adaptations (e.g., SOC) that give rise to continued autonomy. To be sure, cognitive resilience is certainly a resource for autonomy through which to maintain and nurture health, social networks, and behavioral repertoires that are rich and satisfying. Early research in cognitive aging was geared primarily toward distinguishing processing components that are age-sensitive or not. Theoretical perspectives on successful aging and resilience, combined with developments in multivariate statistical methods, have shifted the focus to principles that define individual differences in trajectories of development.
It is important that future research continue to explore the factors that buffer against internal and external threats to successful development and find translation in effective intervention. An area ripe for exploration is the way in which buffering factors interact and reinforce one another. For example, how do health and fitness enable individuals to maintain supportive social networks and to take advantages of opportunities for cognitive enrichment? To what extent are the positive effects of fitness on cognition direct and to what extent are they attributable to an increased sensitivity to cognitively enriching experiences? How can social structures be arranged to promote the capacity for agency and offer opportunities for intellectual stimulation? What are effective translati onal models for intervention?
Assuming that resilience is an ongoing process in which different factors reciprocally reinforce one another (e.g., healthy sleep patterns and exercise habits promote effective coping with challenge to enable cognitive growth, which in turn enable self- regulatory efficacy in lifestyle management), what are the best options for intervening in this cycle when things go awry? Another important open question has to do with life span timing. Finally, effective translation will require research into dose-response functions for the resiliency factors that we have outlined here, especially as they might change through the life span.
It is easy enough to be inspired to make the relatively simple choices in lifestyle that have promise for cognitive resilience – exercise, eat right, engage in activities that enrich the mind, enjoy the comfort of friends and family – but to be undone by uncertainty about how to balance these worthy investments of effort. To the extent that agency is central to sustained engagement, practitioners find themselves having to strike a delicate balance between both creating social/ institutional structures and therapeutic interventions that articulate principles of life span resilience and affording choice.
We are grateful for support from the National Institute on Aging (Grant ROl AG029475). Many thanks to Brennan Payne, Josh Jackson, Patrick Hill, Xuefei Gao, and Dan Morrow for helpful discussions and insightful comments on earlier drafts. References
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Helena Chui, PhD, University of Illinois, Urbana-Champaign, Champaign, IL Elizabeth A. L. Stine-Morrow, PhD, University of Illinois at Urbana-Champaign, Champaign, IL Word count: 9197
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