Aging and Develomental Psychology

Aging is inevitable. Although the average life expectancy has increased dramatically in recent years, we have yet to discover the proverbial fountain of youth. As such, our body gradually succumbs to the aging  process. This  process  is  so  powerful  that  it inundates every aspect of life, from changes in appearance and limited physical mobility to cognitive impairments that may rob us of our very essence. These age-related changes are resultant of both pathological and normal aging processes. Although age-related diseases such as Alzheimer’s and Parkinson’s can be traced to pathological aberrations, “normal” aging surely contributes to the downward progression of these disorders as well. Despite what could be perceived as a bleak prognosis, there is striking individual variability in how aging influences our everyday life both between and within demographic groups. Consider the range in physical abilities and the disparity of cognitive abilities in an aged population. For instance, although aging generally leads to a reduction in muscle strength, the actual rate of decline can be affected by lifestyle variables such as activity level, diet, basal metabolic rate, and a host of other contributing factors. Indeed, older athletes who continue to train can maintain high levels of athletic competence. It seems unrealistic to believe that we can forever thwart the “beast” of age. Yet, through a better understanding of how the aging process interacts with biological, cognitive, and social aspects of our lives, we hope to glean insight into how we might age successfully.

Neuroanatomical Correlates Of Aging

It comes as no surprise that our brain ages in a manner much similar to other bodily organs. These changes are evident using techniques that range from the molecular to the psychological, and everything between. Age-related changes in function are associated with structural brain changes that can have profound psychological consequences. For example, visual impairment is one of the first symptoms of aging, with the average 85-year-old demonstrating about 80% less visual acuity than that of a 40-year-old. Fortunately, from an aging brain perspective, retinal degeneration appears to be the major cause of this change because the brain areas involved in visual processing appear to remain generally unaffected. Although this observation may not be cause for celebration, it does suggest that brain circuitry remains relatively intact with advanced age, dispelling a common misconception about how the brain ages: the idea that age-related neuron loss is ubiquitous. Technological advances have been instrumental in debunking this belief, with evidence accumulating that the brain does not atrophy in a nonspecific, passive manner akin to that of a muscle with misuse. In contrast, brain atrophy appears to be limited in extent, selective in regional expression, and subject to considerable individual variability. For example, early research suggested that widespread, senescence-associated cell loss occurred throughout the hippocampus, an area of the brain heavily implicated in the formation of many types of new memories. Consistent with this notion are the observations that the types of memories processed by the hippocampus are frequently compromised in older adults. Yet, recent studies using improved microscopic techniques indicate that hippocampal cell loss is relatively minimal and restricted to specific hippocampal subfields. These  regional  and  discrete  observations parallel those of memory decline seen in old age; not all aspects of memory function are impaired; rather, only specific modes are influenced by the aging process. In particular, hippocampal function is strongly correlated with the ability to form durable memory traces, with older adults showing greater declines in memory for newly acquired information dependent on this ability, relative to well-established, long-standing memories that are more readily retrieved by older adults. Other areas of the brain yield similar observations. Consider, for example, the cerebellum—a brain region that plays a major role in orchestrating directed movement. Impaired motor coordination and balance are common complaints in old age, which could suggest impaired cerebellar function. In part, this is true because there is significant age-associated neuron loss in the anterior lobe of this structure, yet the entire cerebellum is not equally affected.

Academic Writing, Editing, Proofreading, And Problem Solving Services

Get 10% OFF with 24START discount code

Despite the evidence that widespread cell loss is not a recurrent theme in the aging brain, some brain regions are particularly susceptible to the deleterious effects of aging. The cerebral cortex has been the focus of a great deal of research because it is highly developed in evolutionarily advanced animals— humans included. Moreover, the cortex is organized in a highly conserved, laminar pattern that greatly facilitates identifying cell layers (and the subsequent input and output pathways) and subregions of the cortex itself. In regard to aging, one area in particular in the cortical region has been the focus of intense research scrutiny—the prefrontal cortex. The prefrontal cortex is a brain region involved in controlling an array of functions, all generally related to the ability to regulate and organize behavior. At a cellular level, dendritic arborization in superficial cortical layers of this region is diminished with advanced age, whereas deeper cortical layers are relatively unaffected. On a more global level, the prefrontal cortex appears to be particularly susceptible to the effects of age because this area experiences a greater overall volumetric loss than is experienced in other cortical regions. These structural observations also have functional correlates, with reduced prefrontal activation during performance of cognitive tasks. Behaviorally, these changes manifest as declines in the ability to engage strategic memory  processing  (i.e.,  the  coordinating,  interpreting, and elaborating of information that occurs during memory encoding to place it in its appropriate context and facilitate its later retrieval). It is this specific strategic use of memory that appears to show the greatest decline in old age, with the largest decrements seen in free recall, whereby strategic memory processing must be engaged in order for successful retrieval to occur.

An age-associated reduction in dendritic arborization of supragranular neurons is also seen in the parietal region of cortex, specifically in Wernicke’s area in the parietal lobe (a cortical region involved in language comprehension). In fact, these effects, as well as those previously mentioned for prefrontal cortex, appear to reflect their developmental progression. Specifically, dendrites in supragranular cortical layers continue to expand well into adulthood, whereas dendrites in deep cortical layers are relatively stable much earlier in life. Consistent with these observations, the distal sectors of the dendritic arbor appear also to be more responsive to experiential effects. Ironically, this property of enduring brain plasticity in these distal regions across the life span may ultimately predispose this region to age-related deterioration.

At the cellular level, dendritic spines (the site of most excitatory synaptic contacts between neurons), neurotransmitter levels (the chemicals used by neurons to communicate with each other), and even cellular receptors (the site at which neurotransmitters have their primary effect) have been shown to be quite responsive to differential experience. Considering these parallels between developmental and experiential plasticity, and the seeming increased susceptibility during aging, it is not surprising that there is an age-associated reduction in spine and receptor density in selective brain regions and that the physiological properties of neurons are dramatically affected by such changes.

Nonneuronal Brain Changes

Although most attention has traditionally emphasized the role of neurons in brain function, the contributions of glia (historically viewed as support cells; involved in processes such as neuronal insulation and phagocytic activity) and dynamic changes in vasculature are becoming rapidly appreciated. Like that for neurons, there are glia-specific changes in response to behavioral experiences and both regional and cell-specific modifications. Similar to neurons, the types of glia and their functions are differentially modified in senescence. For example, age-related alterations in myelination (the insulation of nerve fibers) have been reported, an observation that parallels changes in cognitive abilities. Specifically, small-diameter fibers appear particularly vulnerable to age-related degeneration, with a loss of about 10% per decade. Conversely, astrocytic activity (typically associated with repair and restorative functions) has been reported elevated in several brain areas of aged subjects.

Much like that for neurons and glia, the cerebral vasculature has been shown to be quite responsive to altered demands. Changes in brain vasculature are reflected most obviously by the increased incidence of stroke in aged individuals. Unfortunately, by the time a stroke is overtly diagnosable, a series of smaller such episodes have already occurred. Our inability to detect these smaller strokes is limited, in large, by the  spatial  resolution  of  modern  neuroimaging  of the cerebrovasculature. By analogy, vascular blockage of the heart must be quite severe to be detected. As such,  impaired  cardiovascular  health  oftentimes  is undiagnosed for many years. Surely, similar effects also occur in the brain. If so, the loss of a significant number of these small-diameter vessels would likely go undetected for a great period of time using current imaging techniques. We know that the cerebral vasculature, like that for neurons and glia, is responsive to differential experience—creating more vessels in response to neuronal demand. This robust plasticity of the cerebral vasculature suggests that blood flow, or the lack thereof, may play a key role in the cognitive decline frequently observed with normal aging. Congruent with this notion, it has been shown that poor cardiovascular health is linked to greater incidence of Alzheimer’s disease.

Theoretical View of the Influences on Brain  Aging

Taken together, the previously mentioned observations suggest that the very mechanisms that enable our brain to change in response to experiences earlier in life may be implicated for the decrements observed later in life. Yet, as previously discussed, there are striking interindividual differences in behavioral outcomes. Moreover, a great deal of variability exists in the underlying anatomy and physiology. Are some individuals prone to cognitive impairments with advanced age and others somehow relatively immune to such declines? That is, why do some individuals succumb to the deleterious cognitive effects of age early in senescence while others appear to be relatively unaffected well into advanced age? It has been suggested that a decline in synaptic density may “set the stage” for age-related changes in cognition for both normal aging and pathological conditions. The underlying anatomy is influenced by both genetic and experiential factors. Yet, to date, we have been unable to identify the source of such anatomical differences and, as such, cannot completely account for individual differences.

It is here that a theoretical approach to these processes is of particular value. One such theory is the canalization model of development proposed by C. H. Waddington  (see  Grossman  et al.,  2003). Although originally conceived to address developmental progression, this model can be readily modified to incorporate  both  genetic  and  nongenetic  factors  linked to age-related declines in cognition as well. In this model, envision a sloped canal with early life events depicted  at  the  top  of  the  canal. An  individual  is represented by a “ball” that travels along the canal surface, “downward” as life progresses. The slope of the canal is defined by genetic influences and serves to guide the developmental progression of an individual in a normalizing manner, along the bottom of the canal. In the model, genetic and experiential events are encountered along the walls of the canal and can serve to promote normal development (the ball rolls toward the middle of the canal), or push development  up the slope toward a threshold that defines abnormal behavior (in this case, age-related deficiencies). In regard to aging, this model incorporates a host of factors such as a progressive loss of neurons or spines, individual differences such as genetic predispositions and congenital perturbations, and environmental influences such as toxic assaults (all leading to reduced synapse numbers and altered neuronal function). Such factors would serve to push an individual toward, and possibly to surpass, an individually defined threshold of overt behavioral deficits. Likewise, the model captures  the  influence  of  canalizing  experiences that serve to normalize or restore function. Indeed, evidence is accumulating that advanced education, physical exercise, continued cognitive challenges, and genetic differences all lead to maintained synapse numbers and robust, healthy neuronal function. Together, these interventions may, to some extent, “immunize” the brain to progressive pathology later in life. All told, these findings point to the conclusion that although neuronal cell loss occurs with advancing age, the brain can be protected to some extent by differential experiences, explaining why some individuals face impairments in mental function in old age, whereas others appear to be relatively impervious to such effects. Moreover, these general findings suggest that neural vitality (and by parallel—behavioral) is best maintained through a philosophy of “use it or lose it,” dispelling another misconception: that eventual “wear and tear” is the underlying cause for such deficits. Increasing amounts of data from a number of longitudinal studies support these claims: education and intellectually engaging activities buffer against cognitive decline in old age.

Psychosocial Influences On Brain–Behavior Relationships

Although the above discussion suggests that changes in brain structure and function, at both the neuronal and nonneuronal levels, are strongly linked to cognitive-behavioral decrements in the older adult, these decrements are neither inevitable nor irreversible. The real story is much more complicated because cognitive function in old age is characterized by growth, decline, and stability. Strategic memory processing, for example, which has been described as being particularly problematic in old age, is an important aspect of one type of memory that has been linked to reliable age differences—declarative memory. Declarative memory generally refers to the conscious experience of remembering, usually tested through recall or recognition measures, and can be thought of in terms of “knowing that.” Examples of these types of memories include remembering the state capital, the name of a spouse, or the rules of a game. This type of memory can be dissociated from nondeclarative memory, usually measured indirectly by observing changes in performance that result from prior experience, without any conscious recollection or reference to that experience. Nondeclarative memory encompasses many different forms, supported by distinct neural pathways, and generally shows little, if any, appreciable decline with advancing age. This type of memory can be thought of in terms of “knowing how.” Some examples of nondeclarative memory function include skill learning and repetition priming (facilitated processing of previously encountered stimuli, i.e., a change in the speed, accuracy, or bias towards old stimuli, relative to baseline or novel stimuli). Regarding skill learning, research indicates that the old adage “You can’t teach an old dog new tricks” is not universally true. Both simple and complex skills can be acquired well into old age. Two important caveats are worth noting, however. First, the acquisition rate of new skills, and colloquially, new memories, proceeds much more slowly in older adults than it does in young adults. The major implication here is that older adults require more extensive practice than younger adults before skill mastery occurs. Second, even though new skills can be acquired by older adults, a growing amount of research indicates that to the extent that the skill relies on declarative memory or motoric function, age differences in skill performance will be present. This is an important observation because it maps onto a key distinction in the skill acquisition  process—the  distinction  between  early and late stages of learning. Each stage relies on different supporting cortical regions, some of which experience more changes with advancing age than others, so that age differences in skill acquisition and performance may actually represent learning stage differences, and not memory decrements, per se. To illustrate, during the early stage of learning, strategic processes  are  heavily  involved  in  the  monitoring and regulation of the many cognitive processes that become engaged to attain the final goal of successful skill performance. Some of these processes include breaking up the skill into its individual steps, using feedback  about  performance  to  make  adjustments, and planning the next sequence of actions. With practice and training, these initial steps in skill acquisition become more proceduralized and automated, so that strategic processing becomes less necessary in skill performance. The early stage of learning has been linked to brain activity in the prefrontal cortex, an area that shows significant anatomical changes with advancing age. Once tasks have become proceduralized and less strategic in terms of the cognitive processes involved, however, there is a shift in brain activation from the prefrontal cortex to posterior cortical regions. These latter areas do not experience the same magnitude of change with advancing age as the prefrontal cortex. Such findings indicate that age differences in skill acquisition are stage dependent: age differences are larger during the early stage of learning than during the later stage. This conclusion is supported by the observation that for skills acquired early in life, for which presumably a high level of expertise has been afforded and performance is likely automated, age differences are greatly attenuated, and in some cases even eliminated. Well-learned skills, then, tend to be immune to the effects of aging, whereas newly learned skills may not be expected to be resistant to aging effects.

A number of arguments exist as to why expertise reduces age differences in skilled performance. One argument has been that older experts develop a compensatory mechanism that allows them to offset the negative effects of aging by relying on relevant domain-specific knowledge to enable them to perform at levels comparable to that of young adults. For example, in studies examining older and younger pilots versus nonpilots, age differences in the performance of tasks related to pilot communication activities were present only in the nonpilots. Older pilots compensated for potential age differences in task performance by relying on their knowledge about pilot communication to readily perform the task. Similar types of compensation have been observed in older chess experts, typists, pianists, and bridge experts. These mechanisms are believed to develop unconsciously over time as the aging individual strives to maintain performance levels in the face of decline. Although the compensatory effects of expertise tend to be domain specific (i.e., they have larger buffering effects within areas of expertise than in other areas), arguments have been made that expert performance is supported by a long-term working memory system (see, for example, Horn & Masunaga, 2000). This system is characterized by its ability to hold and manipulate large amounts of information over extended periods of time so that it can be quickly accessed during task performance. This type of memory appears to emerge over time, as expertise develops to allow for superior performance. The evidence from older experts indicates that long-term working memory may be resistant to the effects of aging, although additional research investigating this claim is needed.

Beyond skilled performance, other areas of cognitive function also demonstrate growth or stability in old age. These areas include verbal comprehension, logical reasoning, induction, and concept formation. Collectively, these abilities represent an aspect of mental ability known as crystallized intelligence and represent experiential, or culturally valued, knowledge. They generally reflect the development of everyday judgment, understanding, and thinking— skills that mature over time. These abilities are often contrasted with fluid intelligence abilities, those mental abilities that are not acquired through experience or culture. Some examples of fluid abilities are spatial reasoning and perceptual processing speed. Arguments have been made that these abilities reflect central nervous system integrity, so that, consequently, they reveal a pattern of decline in old age. As is the case regarding memory function, with age-related performance dissociations existing between declarative and nondeclarative memory, general intellectual ability also shows age-related performance dissociations, providing additional support for our argument that aging does not produce universal declines in function.

In addition to the issues just described, a growing corpus of research indicates that psychosocial factors such as education, environmental complexity (e.g., community dwelling versus institutionalized living), physical activity, sense of control, and self-efficacy may strongly influence cognitive function to attenuate age differences. For example, older adults with strong perceived control over memory function outperform older adults with weaker perceived control. Similarly,  modifying one’s sense of control or self-efficacy regarding memory function (i.e., adopting a more positive perspective) can lead to improvements in memory performance. Combined, these findings indicate that although changes in brain function alter older adults’ cognitive abilities, psychosocial and environmental factors can help maintain cognitive competencies—the situational use of cognitive abilities— in old age. Despite the losses to cognitive abilities (with memory loss being the most notable of these changes), cognitive competency, particularly in occupational and daily living activities, can increase across the life span.

Finally, one additional factor that has been shown to provide buffering effects against cognitive decline in old age is the social environment of older adults. Recent large-scale longitudinal studies reveal that greater levels of social engagement (i.e., more contact with friends and family and greater involvement in group activities), as well as greater levels of emotional support from friends and family, can provide some protective effects against cognitive decline in old age. These effects appear to be independent of other factors that might predict cognitive decline, so that social isolation in and of itself has a tremendous impact on the cognitive function of older adults.


Aging is a complex and often misunderstood process. Although the aging process itself is inevitable, aging does not always produce decline and impairments in function. Gains and losses are both part of the aging process, so that aging can take many different paths. We have focused our discussion on brain changes and their consequent effects on cognitive and mental abilities because these aspects of human behavior have strong ties to an individual’s psyche, such that within some individuals, these changes can preclude the development of mood disorders, mental illness, or even dementia. However, most individuals experience these changes without significant deleterious effects in their everyday lives, illustrating a key factor related to successful aging: aging varies across individuals. Although some general conclusions about the effects of biological aging can be made, individuals can play an active role in determining the course and eventual outcome of these changes to minimize decline, maintain stability, and achieve growth, at any age.


  1. American Psychological  Association—Division  20.  Adult Development and Aging,
  2. Grossman, A. W., Churchill, J. , McKinney, B. C., Kodish, I. M., Otte, S.  L.,  &  Greenough,  W.  T.  (2003).  Experience effects on brain development: Possible contributions to psychopathology. Journal of Child Psychology and Psychiatry, 44(1), 33–63.
  3. Horn, J. L., & Masunaga, H. (2000). New directions for research into aging and intelligence: The development of e In T. J. Perfect & E. A. Maylor (Eds.), Models of cognitive aging (pp. 125–159). Oxford, UK: Oxford University Press.
  4. Institute for the Study of Aging,
  5. Masliah, , Mallory, M., Hansen, L., DeTeresa, R., & Terry, R. D. (1993). Quantitative synaptic alterations in the human neocortex during normal aging. Neurology, 43(1), 192–197.
  6. National Institute on Aging,
  7. Peters, (2002). Structural changes that occur during normal aging of primate cerebral hemispheres. Neuroscience and Biobehavioral Reviews, 26(7), 733–741.
  8. Prull, W.,  Gabrieli,  J.  D.  E.,  &  Bunge,  S. A.  (2000). Age-related changes in memory: A cognitive neuroscience perspective. In F. I. M. Craik & T. A. Salthouse (Eds.), The handbook of aging and cognition (2nd ed., pp. 91–153). Hillsdale, NJ: Erlbaum.
  9. Rowe, J.  W.,  &  Kahn,  R.  L.  (1997).  Successful  Gerontologist, 37, 433–440.