15.3: Cognition and Memory in Late Adulthood
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Learning Objectives
By the end of this section, you will be able to:
- Describe the features of cognition in late adulthood
- Describe how expertise and wisdom may contribute to everyday problem-solving
Ahmad’s career as a high school history teacher required constant mental activity and use of cognitive skills such as memory, attention, and problem-solving. When he was starting out, Ahmad relied on older, more experienced teachers for advice, but as the years progressed, he became the one younger colleagues came to for guidance, which was a gratifying feeling. After retiring, Ahmad continued to engage in cognitively stimulating activities like reading and doing puzzles, and he began volunteering with an organization that crafted blankets and hats for babies in NICUs. Ahmed enjoyed learning how to knit and crochet, even though some things took a little longer to grasp than they might have thirty years ago.
Theories about cognitive development debate whether people have a single or multiple intelligences. Most research on older adults does not focus on a single intelligence score, however, because abilities tend to change at different times and in different directions. For some cognitive tasks, such as crystallized intelligence and vocabulary, improvements occur across adulthood. For others, such as fluid intelligence and processing speed, performance typically peaks earlier in adulthood and then declines (Veríssimo et al., 2022). This section covers various cognitive abilities in later life, including processing speed, wisdom, and different types of memory, as well as the broader context of these cognitive skills for solving problems in everyday life.
Cognitive Abilities in Late Adulthood
Ageist stereotypes can lead many to assume that moments of forgetfulness in older adults are an indicate of cognitive decline. However, in reality all of us have moments of forgetfulness or confusions. What does the research on memory and cognition in late adulthood reveal? In fact, cognitive aging is much more nuanced than the stereotypes suggest.
Processing Speed
Processing speed, one of the most basic cognitive skills we can study, measures the length of time it takes for us to perceive sensory information and follow with a response. One way to measure this response time experimentally is to have participants press a button on a keyboard as soon as a shape or object appears on a screen. The number of milliseconds their response requires indicates each individual’s reaction time. Processing speed is associated with overall cognitive ability, and people with quick processing speed tend to perform well on other cognitive tasks like memory and attention (Papadopoulos et al., 2018; Ticha et al., 2023).
Research consistently finds that processing speed slows across all of adulthood (Davis et al., 2017; Marrero-Polegre et al., 2023; Ticha et al., 2023). However, this decline doesn’t manifest the same way for everyone. For example, education received in young adulthood is positively correlated with performance on cognitive tasks in late adulthood, although it doesn’t appear to prevent decline; instead, it seems to produce a higher baseline level of functioning that means cognitive skills are better maintained with age (Davis et al., 2017; Vonk et al., 2020). Similarly, speed on cognitive tasks in young adulthood and middle age is often predictive of performance in later life. Members of the fastest tenth percentile, despite consistent declines, may still be faster in their seventies than many of the slower groups were in their twenties (Introzzi et al., 2020; Salthouse, 2000).
More complex processing-speed tasks, like driving, require more complex responses and reveal age-related declines (Figure 15.15). If a car pulls out in front of you or the driver ahead of you slams their brakes, you may have to react quickly to avoid an accident, a response heavily based on processing speed but more complex than simple reaction time because of the number of possible reactions. You could honk your horn, swerve to the right, swerve to the left, brake hard, accelerate, or choose a combination of responses. Research suggests that older adults’ processing speed is predictive of driving mistakes during simulations, but that older adults tend to compensate for slower processing speeds by driving more slowly, which affords them time to respond to hazards. Thus, it’s difficult to discern how much increased risk of driving accidents among older adults is due to slowed processing speed and how much may be due to other relevant issues such as visual declines (Doroudgar et al., 2017).
Link to Learning
People concerned about their ability to drive (due to age, stroke, or other circumstances) can arrange for a special test to assess their safety behind the wheel, such as the one described in this Pre-Driving Assessment from the Marshfield Clinic Health System. Usually conducted by an occupational therapist, the test focuses less on the understanding of traffic rules and more on cognitive, sensory, and motor functioning.
Fluid and Crystallized Intelligence
Many developmental paths you’ve learned about continue throughout later adulthood. For example, fluid intelligence declines with age, while crystallized abilities increase, with both trends continuing in those aged sixty years and older. Recall from 7.3 Intelligence in Middle Childhood that fluid intelligence is the ability to use logic and solve problems in new ways, and crystallized intelligence is our existing knowledge developed through education and experience. Initial theories indicated that relying on crystallized abilities could be a way to compensate for declining fluid abilities. More recent research, however, suggests this may not be the case. Tucker-Drob and colleagues (2022) found in two large longitudinal studies that individuals who declined the least in fluid abilities also increased the most in crystallized abilities across adulthood. Similarly, individuals experiencing the greatest declines in fluid abilities had less increase and sometimes even a decline in crystalized abilities. This suggests that crystalized abilities do not strengthen to compensate for declines in fluid abilities. Instead, there are more individual differences in the way cognitive ability changes throughout adulthood.
Memory and Older Age
Short-term memory has very limited capacity and retains information for a very brief period. Research suggests that it declines over time, noticeably at ages sixty through sixty-nine years and in more pronounced ways after age seventy years. Working memory requires more effort to use as we manipulate and apply information in short-term memory . These demands result in a more noticeable drop in working memory, especially over age seventy years, although some studies have noted little to no change between middle age and the start of late adulthood (Sharma & Babu, 2017). Short-term memory (with which we can, say, immediately recall numbers that were presented) is more passive and demonstrates more subtle declines (Dobbs & Rule, 1989; Pliatsikas et al., 2018).
Working and short-term memories typically last less than thirty seconds. When it comes to long-term memories, research has distinguished many types. One of these, implicit memory , is not conscious. That is, previous experiences allow us to complete a task without even realizing how. Think of learning to ride a bicycle (Figure 15.16). People often describe it as difficult and very intentional at first. Once they master the skill, though, they are able to ride a bicycle automatically, without remembering what to do each time (and if you learned to ride a bike, you likely still can even if it has been a long time). (May et al., 2005).
In contrast, explicit memories are long-term memories of which we are quite aware. One type is semantic memory , which stores facts, vocabulary, and other information about the world. Semantic memory tends to remain intact and does not decline with age (Lalla et al., 2022). It overlaps considerably with crystalized intelligence, but retrieval problems grow more common with age (Crook & West, 1990; Srokova et al., 2022). For instance, the tip-of-the-tongue phenomenon , a temporary failure to recall a stored memory, increases in later years. But the basic functioning of semantic memory seems to remain fairly stable.
Another type of long-term memory is episodic memory , which stores details and information from life events. Research suggests that declines in episodic memory are among the more common age-related memory declines (Kinugawa et al., 2013). Most of this research, however, uses cross-sectional age comparisons. Longitudinal data confirm declines in episodic memory but suggest they may be more subtle and gradual than cross-sectional research indicates (Dixon et al., 2004). These declines have been associated with decreased functioning of the hippocampus; hippocampus functioning and episodic memory demonstrate accelerated declines after age sixty-five years (Nyberg, 2016).
An entire category of memory, autobiographical memory , is dedicated to remembering information about ourselves and our personal life and history. Episodic memories, which seem similar, are indeed part of our autobiographical memory, but autobiographical memory includes many semantic memories as well. For example, you may remember the experience of your first day of high school—how you felt, what the classrooms looked like, and so on. This is episodic memory, which is often represented through images and feelings. Autobiographical memory, on the other hand, includes that episodic information plus semantic, factual information, such as the names of your teachers and whether it rained that day. We know a lot of factual information (semantic memories) about ourselves that is not linked to specific events of our life—our name, birth date, the name of the high school we attended are all examples of autobiographical memories that are relevant to semantic memories and not typically linked to a specific episode or life event (Frankenberg et al., 2021; Roediger & Marsh, 2003).
Research examining changes in autobiographical memory in later years parallels research related to episodic and semantic memory. There are declines in episodic aspects of autobiographical memory but not in the semantic aspect (Jacques & Levine, 2007). One unique finding related to autobiographical memory is the positivity effect , the tendency to remember things from our past more positively as we get older (Kennedy et al., 2004). Some research suggests this may be due to increases in emotional regulation ability, combined with shifting priorities for maintaining a positive emotional state.
Cognitive Aging in Context
Cognitive skills are interesting as a topic, but what’s even more useful is the way people use these skills to accomplish real-life tasks. One area associated with more positive aging stereotypes is wisdom . Defining wisdom is a difficult task. Wide-ranging descriptions appear across both history and the academic literature, but in general, wisdom is the ability to use intellect and past experiences to make good decisions about the future. Wisdom requires considering the perspectives of others, compromising, and understanding uncertain or changing contexts (Grossman, 2017). Wisdom is similar to postformal thought, but it is more reliant on using life experiences.
The scientific literature on wisdom suggests the potential for wisdom to increase over time, though age alone is not a good indicator of wisdom. Age is, however, positively correlated with wisdom, since more experience can be beneficial for acquiring the life lessons needed for wisdom, and more experience can be had with age. The true defining characteristics of wisdom, however, are being open to experiences, learning from them, and being willing to change and adapt in the future based on these experiential lessons (Sternberg, 2005). People who take this approach to life are more likely to see their wisdom increase as they get older and subsequently gain more experiences and life lessons.
Research on everyday problem-solving looks at the way people solve cognitively demanding tasks and make decisions in the real world, including in social settings (Hertzog et al., 2021; Marsiske & Margrett, 2006; Strough et al., 2003). Many cognitive tasks typically used to study reasoning or problem-solving skills rely on abstract situations, unlike day-to-day life, a research approach that has been criticized for lacking validity (Artistico et al., 2019; Chaytor et al., 2003; Hamilton et al., 2022; Jones et al., 2021). If instead we measure skills used in activities of daily living ( ADL ), like figuring out a bus schedule or making financial decisions, participants can better demonstrate their abilities without being disadvantaged by the unfamiliar context of traditional cognitive tests. Artistico and colleagues (2019) found that asking older adults to solve problems they perceived as personally relevant resulted in more and better solutions than asking them to solve problems experienced by other people.
Are older adults more effective at solving everyday problems than young adults? Early research findings yield mixed results; some studies suggested they are (Blanchard-Fields et al., 2007), whereas other studies found these skills increased until middle adulthood before declining (Denney & Pearce, 1989), and some suggested a plateau lasting through most of early and middle adulthood before a decline in later years (Thornton & Dumke, 2005). More recent research has suggested several reasons for these inconsistent findings.
First, everyday problem-solving and decision-making likely tap into different underlying abilities (such as crystallized and fluid abilities) that change differently with age; older adults tend to rely more on crystallized abilities than young and middle-aged adults do (Artistico et al., 2019; Chen et al., 2017), so performance may differ based on which abilities are required by the task in question. Also, this research has focused mostly on cross-sectional data, so what is interpreted as age differences could instead represent cohort differences (or a combination of both). For example, the primary stressors of young and middle-aged adults tend to relate to school and work, while for older adults they are health concerns. Because health concerns are longer term and harder to avoid and control than school and work concerns, they may require a different problem-solving approach that includes regulating emotional reactions instead of “fixing” the situation (Chen et al., 2018). If research uses solution-focused strategies as the determinant of success regardless of the task at hand, it may overlook other adaptive measures participants use to solve problems.
One potential contributor to everyday problem-solving is expertise , a very high level of knowledge or skill related to a specific area (Figure 15.17). Developing expertise often requires having a lot of experience with something; this is associated with age for obvious reasons, but as with wisdom, age alone doesn’t make someone an expert. Experience appears to be more important. For example, one study examining financial decision-making found that expertise (defined here as deep experience with financial decision-making and a high level of knowledge of finance issues) was beneficial. Again, like wisdom, expertise may thus provide a path to making sound decisions that is not as affected by declines in fluid abilities, such as processing speed and reasoning (Li et al., 2015).
All the cognitive declines you’ve learned about are normative age-related results that most people tend to experience. They are usually small in scale, with very limited impact on everyday functioning (Harada et al., 2013). If cognitive declines are severe enough to interfere with someone’s day-to-day life and threaten their independence, another problem such as dementia is likely the cause. While some small cognitive declines are normative with age in certain areas of memory, those with expertise can show improvements in everyday problem solving as they age. Much like other stages of the lifespan, there are many individual differences in cognitive abilities based on peoples environments, resources, and contexts. Many older adults can cognitively adapt well to changes as they age through good adaptive cognition and a supportive environment (Blanchard-Fields & Chen, 1996).
References
Artistico, D., Cervone, D., & Garcia, C. M. (2019). My problems are solvable: Idiographic methods offset age differences in interpersonal problem solving among young, middle-aged, and older adults. Frontiers in Psychology, 10 , 276. https://doi.org/10.3389/fpsyg.2019.00276
Blanchard-Fields, F., & Chen, Y. (1996). Adaptive cognition and aging. American Behavioral Scientist, 39 (3), 231–248. doi.org/10.1177/0002764296039003003
Blanchard-Fields, F., Mienaltowski, A., & Seay, R. B. (2007). Age differences in everyday problem-solving effectiveness: Older adults select more effective strategies for interpersonal problems. The Journals of Gerontology: Series B , 62 (1), P61–P64. doi.org/10.1093/geronb/62.1.p61
Chaytor, N., & Schmitter-Edgecombe, M. (2003). The ecological validity of neuropsychological tests: A review of the literature on everyday cognitive skills. Neuropsychology Review , 13 (4), 181–197. https://doi.org/10.1023/b:nerv.0000009483.91468.fb
Chen, X., Hertzog, C., & Park, D. C. (2017). Cognitive predictors of everyday problem solving across the lifespan. Gerontology, 63 (4), 372–384. doi.org/10.1159/000459622
Chen, Y., Peng, Y., Xu, H., & O’Brien, W. H. (2018). Age differences in stress and coping: Problem-focused strategies mediate the relationship between age and positive affect. International Journal of Aging & Human Development, 86 (4), 347–363. doi.org/10.1177/0091415017720890
Crook, T. H., & West, R. L. (1990). Name recall performance across the adult life-span. British Journal of Psychology , 81 (3), 335–349. doi.org/10.1111/j.2044-8295.1990.tb02365.x
Davis, D., Bendayan, R., Muniz Terrera, G., Hardy, R., Richards, M., & Kuh, D. (2017). Decline in search speed and verbal memory over 26 years of midlife in a British birth cohort. Neuroepidemiology, 49 (3-4), 121–128. doi.org/10.1159/000481136
Denney, N. W., & Pearce, K. A. (1989). A developmental study of practical problem solving in adults. Psychology and Aging , 4 (4), 438–442. doi.org/10.1037/0882-7974.4.4.438
Dixon, R. A., Wahlin, Å., Maitland, S. B., Hultsch, D. F., Hertzog, C., & Bäckman, L. (2004). Episodic memory change in late adulthood: Generalizability across samples and performance indices. Memory & Cognition , 32 (5), 768–778. https://doi.org/10.3758/bf03195867
Dobbs, A. R., & Rule, B. G. (1989). Adult age differences in working memory. Psychology and Aging , 4 (4), 500–503. doi.org/10.1037/0882-7974.4.4.500
Doroudgar, S., Chuang, H. M., Perry, P. J., Thomas, K. L., Bohnert, K., & Canedo, J. (2017). Driving performance comparing older versus younger drivers. Traffic Injury Prevention , 18 (1), 41–46. doi.org/10.1080/15389588.2016.1194980
Frankenberg, C., Knebel, M., Degen, C., Siebert, J., Wahl, H., & Schröder, J. (2021). Autobiographical memory in healthy aging: A decade-long longitudinal 2study. Aging, Neuropsychology, and Cognition , 29 (1), 158–179. doi.org/10.1080/13825585.2020.1859082
Grossmann, I. (2017). Wisdom in context. Perspectives on Psychological Science , 12 (2), 233–257. doi.org/10.1177/1745691616672066
Hamilton, L. J., Gourley, A. N., & Krendl, A. C. (2022). They cannot, they will not, or we are asking the wrong questions: Re-examining age-related decline in social cognition. Frontiers in Psychology, 13 , 894522. https://doi.org/10.3389/fpsyg.2022.894522
Harada, C. N., Love, M. C. N., & Triebel, K. L. (2013). Normal cognitive aging. Clinics in Geriatric Medicine , 29 (4), 737–752. https://doi.org/10.1016/j.cger.2013.07.002
Hertzog, C., Pearman, A., Lustig, E., & Hughes, M. (2021). Fostering self-management of everyday 3memory in older adults: A new intervention approach. Frontiers in Psychology , 11 . https://doi.org/10.3389/fpsyg.2020.560056
Introzzi, I., Zamora, E. V., Aydmune, Y., Richard’s, M. M., Comesaña, A., & Juric, L. C. (2020). The change processes in selective attention during adulthood. Inhibition or processing speed? The Spanish Journal of Psychology , 23 . https://doi.org/10.1017/sjp.2020.41
Jacques, P. L., & Levine, B. (2007). Ageing and autobiographical memory for emotional and neutral events. Memory , 15 (2), 129–144. doi.org/10.1080/09658210601119762
Jones, W. E., Benge, J. F., & Scullin, M. K. (2021). Preserving prospective memory in daily life: A systematic review and meta-analysis of mnemonic strategy, cognitive training, external memory aid, and combination interventions. Neuropsychology, 35 (1), 123. dx.doi.org/10.1037/neu0000704
Kennedy, Q., Mather, M., & Carstensen, L. L. (2004). The role of motivation in the age-related positivity effect in autobiographical memory. Psychological Science , 15 (3), 208–214. doi.org/10.1111/j.0956-7976.2004.01503011.x
Kinugawa, K., Schumm, S., Pollina, M., Depré, M., Jungbluth, C., Doulazmi, M., Sebban, C., Zlomuzica, A., Pietrowsky, R., Pause, B. M., Mariani, J., & Dere, E. (2013). Aging-related episodic memory decline: Are emotions the key? Frontiers in Behavioral Neuroscience , 7 . https://doi.org/10.3389/fnbeh.2013.00002
Lalla, A., Tarder-Stoll, H., Hasher, L., & Duncan, K. (2022). Aging shifts the relative contributions of episodic and semantic memory to decision-making. Psychology and Aging , 37 (6), 667–680. doi.org/10.1037/pag0000700
Li, W., Yang, W., Li, W., Li, Y., Wei, D., Li, H., Qiu, J., & Zhang, Q. (2015). Brain structure and resting-state functional connectivity in university professors with high academic achievement. Creativity Research Journal, 27 (2), 139–150. doi.org/10.1080/10400419.2015.1030311
Marrero-Polegre, D., Finke, K., Roaschio, N., Haupt, M., Reyes-Moreno, C., & Ruiz-Rizzo, A. L. (2023). Lower visual processing speed relates to greater subjective cognitive complaints in community-dwelling healthy older adults. Frontiers in Psychiatry, 14 , 1063151. https://doi.org/10.3389/fpsyt.2023.1063151
Marsiske, M., & Margrett, J. A. (2006). Everyday problem solving and decision making. In Elsevier eBooks (pp. 315–342). https://doi.org/10.1016/b978-012101264-9/50017-3
May, C. P., Hasher, L., & Foong, N. (2005). Implicit memory, age, and time of day. Psychological Science , 16 (2), 96–100. doi.org/10.1111/j.0956-7976.2005.00788.x
Nyberg, L. (2016). Functional brain imaging of episodic memory decline in ageing. Journal of Internal Medicine , 281 (1), 65–74. doi.org/10.1111/joim.12533
Papadopoulos, T. C., Georgiou, G. K., Deng, C., & Das, J. P. (2018). The structure of speed of processing across cultures. Advanced Cognitive Psychology, 14 (3), 112–125. https://doi.org/10.5709/acp-0243-7
Pliatsikas, C., Veríssimo, J., Babcock, L., Pullman, M., Glei, D. A., Weinstein, M., Goldman, N., & Ullman, M. T. (2018). Working memory in older adults declines with age, but is modulated by sex and education. Quarterly Journal of Experimental Psychology , 72 (6), 1308–1327. doi.org/10.1177/1747021818791994
Roediger, H. L., & Marsh, E. J. (2003). Episodic and autobiographical memory. Handbook of psychology: Experimental psychology , 4 , 475–497. doi.org/10.1002/0471264385.wei0417
Salthouse, T. A. (2000). Aging and measures of processing speed. Biological Psychology , 54 (1–3), 35–54. https://doi.org/10.1016/s0301-0511(00)00052-1
Sharma, S., & Babu, N. (2017). Interplay between creativity, executive function and working memory in middle-aged and older adults. Creativity Research Journal, 29 (1), 71–77. doi.org/10.1080/10400419.2017.1263512
Srokova, S., Hill, P. F., & Rugg, M. D. (2022). The retrieval-related anterior shift is moderated by age and correlates with memory performance. The Journal of Neuroscience , 42 (9), 1765–1776. https://doi.org/10.1523/jneurosci.1763-21.2021
Sternberg, R. J. (2005). Older but not wiser? The relationship between age and wisdom. Ageing International , 30 (1), 5–26. https://doi.org/10.1007/BF02681005
Strough, J., Hicks Patrick, J., Swenson, L. M, Cheng, S., & Barnes, K. A. (2003). Collaborative everyday problem solving: Interpersonal relationships and problem dimensions. The International Journal of Aging & Human Development, 56 (1), 43–66. doi.org/10.2190/Y3XN-RW1A-7EWT-KXTC
Thornton, W. J. L., & Dumke, H. A. (2005). Age differences in everyday problem-solving and decision-making effectiveness: A meta-analytic review. Psychology and Aging , 20 (1), 85–99. doi.org/10.1037/0882-7974.20.1.85
Ticha, Z., Georgi, H., Schmand, B., Heissler, R., & Kopecek, M. (2023). Processing speed predicts SuperAging years later. BMC Psychology, 11 (1), 34. https://doi.org/10.1186/s40359-023-01069-7
Tucker-Drob, E. M., La Fuente J, D., Köhncke, Y., Brandmaier, A. M., Nyberg, L., & Lindenberger, U. (2022). A strong dependency between changes in fluid and crystallized abilities in human cognitive aging. Science Advances , 8 (5). doi.org/10.1126/sciadv.abj2422
Veríssimo, J., Verhaeghen, P., Goldman, N., Weinstein, M., & Ullman, M. T. (2022). Evidence that ageing yields improvements as well as declines across attention and executive functions. Nature Human Behaviour, 6 (1), 97–110. https://doi.org/10.1038/s41562-021-01169-7
Vonk, J. M. J., Higby, E., Nikolaev, A., Cahana-Amitay, D., Spiro, A., Albert, M. L., & Obler, L. K. (2020). Demographic effects on longitudinal semantic processing, working memory, and cognitive speed. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 75 (9), 1850–1862. doi.org/10.1093/geronb/gbaa080