problem solving in early adulthood

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Emerging Adulthood & Cognition

Emerging adulthood.

Historically, early adulthood was considered to last from approximately the age of 18 (the end of adolescence) until 40 or 45 (the beginning of middle adulthood). More recently, developmentalists have divided this 25 year age period into two separate stages: Emerging adulthood followed by early adulthood. Although these age periods differ in their physical, cognitive, and social development, overall the age period from 18 to 40 is a time of peak physical capabilities and the emergence of more mature cognitive development, financial independence, and the establishment of intimate relationships.

Emerging Adulthood Defined

Emerging adulthood is the period between the late teens and early twenties ; ages 18-25, although some researchers have included up to age 29 in their definitions (Society for the Study of Emerging Adulthood, 2016). Jeffrey Arnett (2000) argues that emerging adulthood is neither adolescence nor is it young adulthood. Individuals in this age period have left behind the relative dependency of childhood and adolescence but have not yet taken on the responsibilities of adulthood. “Emerging adulthood is a time of life when many different directions remain possible, when little about the future is decided for certain, when the scope of independent exploration of life’s possibilities is greater for most people than it will be at any other period of the life course” (Arnett, 2000, p. 469). Arnett identified five characteristics of emerging adulthood that distinguish it from adolescence and young adulthood (Arnett, 2006).

• It is the age of identity exploration . In 1950, Erik Erikson proposed that it was during adolescence that humans wrestled with the question of identity. Yet, even Erikson (1968) commented on a trend during the 20th century of a “prolonged adolescence” in industrialized societies. Today, most identity development occurs during the late teens and early twenties rather than adolescence. It is during emerging adulthood that people are exploring their career choices and ideas about intimate relationships, setting the foundation for adulthood.
•  Arnett also described this time period as the age of instability (Arnett, 2000; Arnett, 2006). Exploration generates uncertainty and instability. Emerging adults change jobs, relationships, and residences more frequently than other age groups.
• This is also the age of self-focus . Being self-focused is not the same as being “self-centered.” Adolescents are more self-centered than emerging adults. Arnett reports that in his research, he found emerging adults to be very considerate of the feelings of others, especially their parents. They now begin to see their parents as people not just parents, something most adolescents fail to do (Arnett, 2006). Nonetheless, emerging adults focus more on themselves, as they realize that they have few obligations to others and that this is the time where they can do what they want with their life.
• This is also the age of feeling in-between. When asked if they feel like adults, more 18 to 25 year-olds answer “yes and no” than do teens or adults over the age of 25 (Arnett, 2001). Most emerging adults have gone through the changes of puberty, are typically no longer in high school, and many have also moved out of their parents’ home. Thus, they no longer feel as dependent as they did as teenagers. Yet, they may still be financially dependent on their parents to some degree, and they have not completely attained some of the indicators of adulthood, such as finishing their education, obtaining a good full-time job, being in a committed relationship, or being responsible for others. It is not surprising that Arnett found that 60% of 18 to 25 year-olds felt that in some ways they were adults, but in some ways, they were not (Arnett, 2001).
• Emerging adulthood is the age of possibilities . It is a time period of optimism as more 18 to 25 year-olds feel that they will someday get to where they want to be in life. Arnett (2000, 2006) suggests that this optimism is because these dreams have yet to be tested. For example, it is easier to believe that you will eventually find your soul mate when you have yet to have had a serious relationship. It may also be a chance to change directions, for those whose lives up to this point have been difficult. The experiences of children and teens are influenced by the choices and decisions of their parents. If the parents are dysfunctional, there is little a child can do about it. In emerging adulthood, however, people can move out and move on. They have the chance to transform their lives and move away from unhealthy environments. Even those whose lives were happy and fulfilling as children, now have the opportunity in emerging adulthood to become independent and make their own decisions about the direction they would like their lives to take.

Socioeconomic Class and Emerging Adulthood.  The theory of emerging adulthood was initially criticized as only reflecting upper middle-class, college-attending young adults in the United States and not those who were working class or poor (Arnett, 2016). Consequently, Arnett reviewed results from the 2012 Clark University Poll of Emerging Adults, whose participants were demographically similar to the United States population. Results primarily indicated consistencies across aspects of the theory, including positive and negative perceptions of the time-period and views on education, work, love, sex, and marriage. Two significant differences were found, the first being that emerging adults from lower socioeconomic classes identified more negativity in their emotional lives, including higher levels of depression. Secondly, those in the lowest socioeconomic group were more likely to agree that they had not been able to find sufficient financial support to obtain the education they believed they needed. Overall, Arnett concluded that emerging adulthood exists wherever there is a period between the end of adolescence and entry into adult roles, but also acknowledged that social, cultural, and historical contexts were important.

Cross-cultural Variations.  The five features proposed in the theory of emerging adulthood originally were based on research involving Americans between ages 18 and 29 from various ethnic groups, social classes, and geographical regions (Arnett, 2004, 2016). To what extent does the theory of emerging adulthood apply internationally?

The answer to this question depends greatly on what part of the world is considered. Demographers make a useful distinction between the developing countries that comprise the majority of the world’s population and the economically developed countries that are part of the Organization for Economic Co-operation and Development (OECD), including the United States, Canada, Western Europe, Japan, South Korea, Australia, and New Zealand. The current population of OECD countries (also called developed countries) is 1.2 billion, about 18% of the total world population (United Nations Development Programme, 2011). The rest of the population resides in developing countries, which have much lower median incomes, much lower median educational attainment, and much higher incidence of illness, disease, and early death. Let us consider emerging adulthood in other OECD countries as little is known about the experiences of 18-25 year-olds in developing countries.

The same demographic changes as described above for the United States have taken place in other OECD countries as well. This is true of increasing participation in postsecondary education, as well as increases in the median ages for entering marriage and parenthood (UNdata, 2010). However, there is also substantial variability in how emerging adulthood is experienced across OECD countries. Europe is the region where emerging adulthood is longest and most leisurely. The median ages for entering marriage and parenthood are near 30 in most European countries (Douglass, 2007). Europe today is the location of the most affluent, generous, and egalitarian societies in the world, in fact, in human history (Arnett, 2007). Governments pay for tertiary education, assist young people in finding jobs, and provide generous unemployment benefits for those who cannot find work. In northern Europe, many governments also provide housing support. Emerging adults in European societies make the most of these advantages, gradually making their way to adulthood during their twenties while enjoying travel and leisure with friends.

The lives of emerging adults in developed Asian countries, such as Japan and South Korea, are in some ways similar to the lives of emerging adults in Europe and in some ways strikingly different. Like European emerging adults, Asian emerging adults tend to enter marriage and parenthood around age 30 (Arnett, 2011). Like European emerging adults, Asian emerging adults in Japan and South Korea enjoy the benefits of living in affluent societies with generous social welfare systems that provide support for them in making the transition to adulthood, including free university education and substantial unemployment benefits.

However, in other ways, the experience of emerging adulthood in Asian OECD countries is markedly different than in Europe. Europe has a long history of individualism, and today’s emerging adults carry that legacy with them in their focus on self-development and leisure during emerging adulthood. In contrast, Asian cultures have a shared cultural history emphasizing collectivism and family obligations.

Although Asian cultures have become more individualistic in recent decades, as a consequence of globalization, the legacy of collectivism persists in the lives of emerging adults. They pursue identity explorations and self-development during emerging adulthood, like their American and European counterparts, but within narrower boundaries set by their sense of obligations to others, especially their parents (Phinney & Baldelomar, 2011). For example, in their views of the most important criteria for becoming an adult, emerging adults in the United States and Europe consistently rank financial independence among the most important markers of adulthood. In contrast, emerging adults with an Asian cultural background especially emphasize becoming capable of supporting parents financially as among the most important criteria (Arnett, 2003; Nelson, Badger, & Wu, 2004). This sense of family obligation may curtail their identity explorations in emerging adulthood to some extent, and compared to emerging adults in the West, they pay more heed to their parents’ wishes about what they should study, what job they should take, and where they should live  (Rosenberger, 2007).

When Does Adulthood Begin? According to Rankin and Kenyon (2008), in years past the process of becoming an adult was more clearly marked by rites of passage. For many, marriage and parenthood were considered entry into adulthood. However, these role transitions are no longer considered the important markers of adulthood (Arnett, 2001). Economic and social changes have resulted in more young adults attending college (Rankin & Kenyon, 2008) and delaying marriage and having children (Arnett & Taber, 1994; Laursen & Jensen-Campbell, 1999) Consequently, current research has found financial independence and accepting responsibility for oneself to be the most important markers of adulthood in Western culture across age (Arnett, 2001) and ethnic groups (Arnett, 2004).

In looking at college students’ perceptions of adulthood, Rankin and Kenyon (2008) found that some students still view rites of passage as important markers. College students who placed more importance on role transition markers, such as parenthood and marriage, belonged to a fraternity/sorority, were traditionally aged (18–25), belonged to an ethnic minority, were of a traditional marital status (i.e., not cohabitating), or belonged to a religious organization, particularly for men. These findings supported the view that people holding collectivist or more traditional values place more importance on role transitions as markers of adulthood. In contrast, older college students and those cohabitating did not value role transitions as markers of adulthood as strongly.

Young Adults Living Arrangements.  In 2014, for the first time in more than 130 years, adults 18 to 34 were more likely to be living in their parents’ home than they were to be living with a spouse or partner in their own household (Fry, 2016). The current trend is that young Americans are not choosing to settle down romantically before age 35. Since 1880, living with a romantic partner was the most common living arrangement among young adults. In 1960, 62% of America’s 18- to 34-year-olds were living with a spouse or partner in their own household, while only 20% were living with their parents.

By 2014, 31.6% of early adults were living with a spouse or partner in their own household, while 32.1% were living in the home of their parent(s). Another 14% of early adults lived alone, were a single parent, or lived with one or more roommates. The remaining 22% lived in the home of another family member (such as a grandparent, in-law, or sibling), a non-relative, or in group quarters (e.g., college dormitories). Comparing ethnic groups, 36% of black and Hispanic early adults lived at home, while 30% of white young adults lived at home.

As can be seen in Figure 20.2, gender differences in living arrangements were also noted in that young men were living with parents at a higher rate than young women. In 2014, 35% of young men were residing with their parents, while 28% were living with a spouse or partner in their own household. Young women were more likely to be living with a spouse or partner (35%) than living with their parents (29%). Additionally, more young women (16%) than young men (13%) were heading up a household without a spouse or partner, primarily because women are more likely to be single parents living with their children. Lastly, young men (25%) were more likely than young women (19%) to be living in the home of another family member, a non-relative, or in some type of group quarters (Fry, 2016).

What are some factors that help explain these changes in living arrangements? First, early adults are increasingly postponing marriage or choosing not to marry or cohabitate. Lack of employment and lower wages have especially contributed to males residing with their parents. Men who are employed are less likely to live at home. Wages for young men (adjusting for inflation) have been falling since 1970 and correlate with the rise in young men living with their parents. The recent recession and recovery (2007-present) has also contributed to the increase in early adults living at home. College enrollments increased during the recession, which further increased early adults living at home. However, once early adults possess a college degree, they are more likely to establish their own households (Fry, 2016).

Cognitive Development in Early Adulthood

Emerging adulthood brings with it the consolidation of formal operational thought, and the continued integration of the parts of the brain that serve emotion, social processes, and planning and problem solving. As a result, rash decisions and risky behavior decrease rapidly across early adulthood. Increases in epistemic cognition are also seen, as young adults’ meta-cognition, or thinking about thinking, continues to grow, especially young adults who continue with their schooling.

Perry’s Scheme.  One of the first theories of cognitive development in early adulthood originated with William Perry (1970), who studied undergraduate students at Harvard University.  Perry noted that over the course of students’ college years, cognition tended to shift from dualism (absolute, black and white, right and wrong type of thinking) to multiplicity (recognizing that some problems are solvable and some answers are not yet known) to relativism (understanding the importance of the specific context of knowledge—it’s all relative to other factors). Similar to Piaget’s formal operational thinking in adolescence, this change in thinking in early adulthood is affected by educational experiences.

Table 8.1 Stages of Perry's Scheme

Adapted from Lifespan Development by Lumen Learning

Some researchers argue that a qualitative shift in cognitive development tales place for some emerging adults during their mid to late twenties. As evidence, they point to studies documenting continued integration and focalization of brain functioning, and studies suggesting that this developmental period often represents a turning point, when young adults engaging in risky behaviors (e.g., gang involvement, substance abuse) or an unfocused lifestyle (e.g., drifting from job to job or relationship to relationship) seem to “wake up” and take ownership for their own development. It is a common point for young adults to make decisions about completing or returning to school, and making and following through on decisions about vocation, relationships, living arrangements, and lifestyle. Many young adults can actually remember these turning points as a moment when they could suddenly “see” where they were headed (i.e., the likely outcomes of their risky behaviors or apathy) and actively decided to take a more self-determined pathway.

Optional Reading: Current Trends in Post-secondary Education

According to the National Center for Higher Education Management Systems (NCHEMS) (2016a, 2016b, 2016c, 2016d), in the United States:

• 84% of 18 to 24 year olds and 88% of those 25 and older have a high school diploma or its equivalent
• 36% of 18 to 24 year olds and 7% of 25 to 49 year olds attend college
• 59% of those 25 and older have completed some college
• 32.5% of those 25 and older have a bachelor’s degree or higher, with slightly more women (33%) than men (32%) holding a college degree (Ryan & Bauman, 2016).

The rate of college attainment has grown more slowly in the United States than in a number of other nations in recent years (OCED, 2014). This may be due to fact that the cost of attaining a degree is higher in the U.S. than in most other nations.

In 2017, 65% of college seniors who graduated from private and public nonprofit colleges had student loan debt, and nationally owed an average of $28,650, a 1% decline from 2016 (The Institute for College Access & Success (TICAS), 2018).

According to the most recent TICAS annual report, the rate of debt varied widely across states, as well as between colleges. The after graduation debt ranged from $18,850 in Utah to $38,500 in Connecticut. Low-debt states are mainly in the West, and high-debt states in the Northeast. In recent years there has been a concern about students carrying more debt and being more likely to default when attending for-profit institutions. In 2016, students at for-profit schools borrowed an average of $39,900, which was 41% higher than students at non-profit schools that year. In addition, 30% of students attending for-profit colleges default on their federal student loans. In contrast, the default level of those who attended public institutions is only 4% (TICAS, 2018).

College student debt has become a key political issue at both the state and federal level, and some states have been taking steps to increase spending and grants to help students with the cost of college. However, 15% of the Class of 2017’s college debt was owed to private lenders (TICAS, 2018). Such debt has less consumer protection, fewer options for repayment, and is typically negotiated at a higher interest rate. See Table 7.1 for a debt comparison of 6 U.S. States.

Graduate School: Larger amounts of student debt actually occur at the graduate level (Kreighbaum, 2019). In 2019, the highest average debts were concentrated in the medical fields. Average median debt for graduate programs included:

• $42,335 for a master’s degree
• $95,715 for a doctoral degree
• $141,000 for a professional degree

Worldwide, over 80% of college educated adults are employed, compared with just over 70% of those with a high school or equivalent diploma, and only 60% of those with no high school diploma (OECD, 2015). Those with a college degree will earn more over the course of their life time. Moreover, the benefits of college education go beyond employment and finances. The OECD found that around the world, adults with higher educational attainment were more likely to volunteer, felt they had more control over their lives, and thus were more interested in the world around them. Studies of U.S. college students find that they gain a more distinct identity and become more socially competent and less dogmatic and ethnocentric compared to those not in college (Pascarella, 2006).

Is college worth the time and investment? College is certainly a substantial investment each year, with the financial burden falling on students and their families in the U.S., and covered mainly by the government in many other nations. Nonetheless, the benefits both to the individual and the society outweighs the initial costs. As can be seen in Figure 7.18, those in America with the most advanced degrees earn the highest income and have the lowest unemployment.

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Arnett, J. J. (2001). Conceptions of the transitions to adulthood: Perspectives from adolescence to midlife. Journal of Adult Development, 8, 133-143.

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Arnett, J. J. (2011). Emerging adulthood(s): The cultural psychology of a new life stage. In L.A. Jensen (Ed.), Bridging cultural and developmental psychology: New syntheses in theory, research, and policy (pp. 255–275). New York, NY: Oxford University Press.

Arnett, J. J. (2016). Does emerging adulthood theory apply across social classes? National data on a persistent question. Emerging Adulthood, 4 (4), 227-235.

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Fry, R. (2018). Millenials are the largest generation in the U. S. labor force. Washington, D.C.: Pew Research Center. Retrieved from: https://www.pewresearch.org/fact- tank/2018/04/11/millennials-largest-generation-us-labor-force/

Laursen, B., & Jensen-Campbell, L. A. (1999). The nature and functions of social exchange in adolescent romantic relationships. In W. Furman, B. B. Brown, & C. Feiring (Eds.), The development of romantic relationships in adolescence (pp. 50–74). New York: Cambridge University Press.

Nelson, L. J., Badger, S., & Wu, B. (2004). The influence of culture in emerging adulthood: Perspectives of Chinese college students. International Journal of Behavioral Development, 28 , 26–36.

Perry, W.G., Jr. (1970). Forms of ethical and intellectual development in the college years: A scheme. New York, NY: Holt, Rinehart, and Winston.

Phinney, J. S. & Baldelomar, O. A. (2011). Identity development in multiple cultural contexts. In L. A. Jensen (Ed.), Bridging cultural and developmental psychology: New syntheses in theory, research and policy (pp. 161-186). New York, NY: Oxford University Press.

Rankin, L. A. & Kenyon, D. B. (2008). Demarcating role transitions as indicators of adulthood in the 21st century. Who are they? Journal of Adult Development, 15 (2), 87-92. doi: 10.1007/s10804-007-9035-2

Rosenberger, N. (2007). Rethinking emerging adulthood in Japan: Perspectives from long-term single women. Child Development Perspectives, 1 , 92–95.

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Society for the Study of Emerging Adulthood (SSEA). (2016). Overview. Retrieved from http://ssea.org/about/index.htm

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Early Adulthood

Why learn about development changes during early adulthood.

When we are children and teens, we eagerly anticipate each and every birthday, waiting for the next big one…when we’ll finally be grown up and have all the freedoms and rights enjoyed by those who are older than us. Indeed, there are opportunities to drive, buy a car, vote, go to college, join the military, drink, move out on our own, date, live together, get married, work, have children, buy a house, and more. This can be an awesome time in our lives, as we tend to be physically and cognitively strong and healthy, we dream and make plans for the future, find people to share our experiences, and try out new roles. It can also be challenging, stressful, and scary as we realize that a lot of responsibility comes with such freedom. We have probably all seen the coffee mugs that proclaim, “Adulting is hard,” or the t-shirts that announce, “I can’t adult today” (typically worn by young adults!).

Development is a process, and we aren’t suddenly adults at a certain age. In fact, we may even take longer to grow up these days. In this module, we’ll learn about norms, trends, and theories about why certain patterns are forming. It’s even been proposed that there is a new stage of development between adolescence and early adulthood, called “emerging adulthood,” when young people don’t quite feel like they are adults yet and wait longer to join the workforce, move out on their own, get married, and have children. Yet by the end of early adulthood, most of us will have accomplished the important developmental tasks of becoming more autonomous, taking care of ourselves and even others, committing to relationships and jobs/careers, getting married, raising families, and becoming part of our communities. There are, of course, many individual and cultural differences.

Think of your own life. When will you feel like an adult? Or do you already feel like an adult? Why or why not? Did your parents become adults earlier or later in their lives, compared to you?

What you’ll learn to do: explain developmental tasks and physical changes during early adulthood

In this section, we will see how young adults are often at their peak physically, sexually, and in terms of health and reproduction; yet they are also particularly at risk for injury, violence, substance abuse, sexually transmitted diseases, and more. As you read, consider whether or not you think young adults are in the prime of their lives.

Learning outcomes

• Summarize the developmental tasks of early adulthood
• Describe physical development and health in early adulthood
• Summarize risky behaviors and causes of death in early adulthood
• Describe sexuality and fertility issues related to early adulthood

Developmental Tasks of Early Adulthood

Before we dive into the specific physical changes and experiences of early adulthood, let’s consider the key developmental tasks during this time—the ages between 18 and 40. The beginning of early adulthood, ages 18-25, is sometimes considered its own phase, emerging adulthood, but the developmental tasks that are the focus during emerging adulthood persist throughout the early adulthood years. Look at the list below and try to think of someone you know between 18 and 40 who fits each of the descriptions.

Havighurst (1972) describes some of the developmental tasks of young adults. These include:

• Achieving autonomy: trying to establish oneself as an independent person with a life of one’s own
• Establishing identity: more firmly establishing likes, dislikes, preferences, and philosophies
• Developing emotional stability: becoming more stable emotionally which is considered a sign of maturing
• Establishing a career: deciding on and pursuing a career or at least an initial career direction and pursuing an education
• Finding intimacy: forming first close, long-term relationships
• Becoming part of a group or community: young adults may, for the first time, become involved with various groups in the community. They may begin voting or volunteering to be part of civic organizations (scouts, church groups, etc.). This is especially true for those who participate in organizations as parents.
• Establishing a residence and learning how to manage a household: learning how to budget and keep a home maintained.
• Becoming a parent and rearing children: learning how to manage a household with children.
• Making marital or relationship adjustments and learning to parent.

Think It Over

To what extent do you think these early adulthood developmental tasks have changed in the last several years? How might these tasks vary by culture?

Physical Development in Early Adulthood

The physiological peak.

People in their twenties and thirties are considered young adults. If you are in your early twenties, you are probably at the peak of your physiological development. Your body has completed its growth, though your brain is still developing (as explained in the previous module on adolescence). Physically, you are in the “prime of your life” as your reproductive system, motor ability, strength, and lung capacity are operating at their best. However, these systems will start a slow, gradual decline so that by the time you reach your mid to late 30s, you will begin to notice signs of aging. This includes a decline in your immune system, your response time, and your ability to recover quickly from physical exertion. For example, you may have noticed that it takes you quite some time to stop panting after running to class or taking the stairs. But, remember that both nature and nurture continue to influence development. Getting out of shape is not an inevitable part of aging; it is probably due to the fact that you have become less physically active and have experienced greater stress. The good news is that there are things you can do to combat many of these changes. So keep in mind, as we continue to discuss the lifespan, that some of the changes we associate with aging can be prevented or turned around if we adopt healthier lifestyles.

In fact, research shows that the habits we establish in our twenties are related to certain health conditions in middle age, particularly the risk of heart disease. What are healthy habits that young adults can establish now that will prove beneficial in later life? Healthy habits include maintaining a lean body mass index, moderate alcohol intake, a smoke-free lifestyle, a healthy diet, and regular physical activity. When experts were asked to name one thing they would recommend young adults do to facilitate good health, their specific responses included: weighing self often, learning to cook, reducing sugar intake, developing an active lifestyle, eating vegetables, practicing portion control, establishing an exercise routine (especially a “post-party” routine, if relevant), and finding a job you love.

Being overweight or obese is a real concern in early adulthood. Medical research shows that American men and women with moderate weight gain from early to middle adulthood have significantly increased risks of major chronic disease and mortality (Zheng et al., 2017). Given the fact that American men and women tend to gain about one to two pounds per year from early to middle adulthood, developing healthy nutrition and exercise habits across adulthood is important (Nichols, 2017).

A Healthy, but Risky Time

Early adulthood tends to be a time of relatively good health. For instance, in the United States, adults ages 18-44 have the lowest percentage of physician office visits than any other age group, younger or older.   However, early adulthood seems to be a particularly risky time for violent deaths (rates vary by gender, race, and ethnicity). The leading causes of death for both age groups 15-24 and 25-34 in the U.S. are unintentional injury, suicide, and homicide. Cancer and heart disease follows as the fourth and fifth top causes of death among young adults (Centers for Disease Control and Prevention, 2019).

Substance Abuse

Rates of violent death are influenced by substance abuse, which peaks during early adulthood. Some young adults use drugs and alcohol as a way of coping with stress from family, personal relationships, or concerns over being on one’s own. Others “use” because they have friends who use and in the early 20s, there is still a good deal of pressure to conform. Youth transitioning into adulthood have some of the highest rates of alcohol and substance abuse. For instance, rates of binge drinking (drinking five or more drinks on a single occasion) in 2014 were: 28.5 percent for people ages 18 to 20 and 43.3 percent for people ages 21-25. Recent data from the Centers for Disease Control and Prevention show increases in drug overdose deaths between 2006 and 2016 (with higher rates among males), but with the steepest increases between 2014 and 2016 occurring among males aged 24-34 and females aged 24-34 and 35-44. Rates vary by other factors including race and geography; increased use and abuse of opioids may also play a role.

Drugs impair judgment, reduce inhibitions, and alter mood, all of which can lead to dangerous behavior. Reckless driving, violent altercations, and forced sexual encounters are some examples. College campuses are notorious for binge drinking, which is particularly concerning since alcohol plays a role in over half of all student sexual assaults. Alcohol is involved nearly 90 percent of the time in acquaintance rape (when the perpetrator knows the victim). Over 40 percent of sexual assaults involve alcohol use by the victim and almost 70 percent involve alcohol use by the perpetrator.

Drug and alcohol use increase the risk of sexually transmitted infections because people are more likely to engage in risky sexual behavior when under the influence. This includes having sex with someone who has had multiple partners, having anal sex without the use of a condom, having multiple partners, or having sex with someone whose history is unknown. Such risky sexual behavior puts individuals at increased risk for both sexually transmitted diseases (STDs) and human immunodeficiency virus (HIV). STDs are especially common among young people. There are about 20 million new cases of STDs each year in the United States and about half of those infections are in people between the ages of 15 and 24. Also, young people are the most likely to be unaware of their HIV infection, with half not knowing they have the virus (Centers for Disease Control and Prevention, 2019).

Sexual Responsiveness and Reproduction in Early Adulthood

Sexual responsiveness.

Men and women tend to reach their peak of sexual responsiveness at different ages. For men, sexual responsiveness tends to peak in the late teens and early twenties .  Sexual arousal can easily occur in response to physical stimulation or fantasizing. Sexual responsiveness begins a slow decline in the late twenties and into the thirties although a man may continue to be sexually active throughout adulthood. Over time, a man may require more intense stimulation in order to become aroused. Women often find that they become more sexually responsive throughout their 20s and 30s and may peak in the late 30s or early 40s. This is likely due to greater self-confidence and reduced inhibitions about sexuality.

There are a wide variety of factors that influence sexual relationships during emerging adulthood; this includes beliefs about certain sexual behaviors and marriage. For example, among emerging adults in the United States, it is common for oral sex to not be considered “real sex”. In the 1950s and 1960s, about 75 percent of people between the ages of 20–24 engaged in premarital sex; today, that number is 90 percent. Unintended pregnancy and sexually transmitted infections and diseases (STIs/STDs) are a central issue. As individuals move through emerging adulthood, they are more likely to engage in monogamous sexual relationships and practice safe sex.

Reproduction

For many couples, early adulthood is the time for having children. However, delaying childbearing until the late 20s or early 30s has become more common in the United States. The mean age of first-time mothers in the United States increased 1.4 years, from 24.9 in 2000 to 26.3 in 2014. This shift can primarily be attributed to a larger number of first births to older women along with fewer births to mothers under age 20 (CDC, 2016).

Couples delay childbearing for a number of reasons. Women are now more likely to attend college and begin careers before starting families. And both men and women are delaying marriage until they are in their late 20s and early 30s. In 2018, the average age for a first marriage in the United States was 29.8 for men and 27.8 for women.

Infertility

Infertility affects about 6.7 million women or 11 percent of the reproductive age population (American Society of Reproductive Medicine [ASRM], 2006-2010. Male factors create infertility in about a third of the cases. For men, the most common cause is a lack of sperm production or low sperm production.  Female factors cause infertility in another third of cases. For women, one of the most common causes of infertility is an ovulation disorder. Other causes of female infertility include blocked fallopian tubes, which can occur when a woman has had pelvic inflammatory disease (PID) or endometriosis . PID is experienced by 1 out of 7 women in the United States and leads to infertility about 20 percent of the time. One of the major causes of PID is Chlamydia , the most commonly diagnosed sexually transmitted infection in young women. Another cause of pelvic inflammatory disease is gonorrhea . Both male and female factors contribute to the remainder of cases of infertility and approximately 20 percent are unexplained.

Fertility Treatment

The majority of infertility cases (85-90 percent) are treated using fertility drugs to increase ovulation or with surgical procedures to repair the reproductive organs or remove scar tissue from the reproductive tract.   In vitro fertilization (IVF)  is used to treat infertility in less than 5 percent of case s. IVF is used when a woman has blocked or deformed fallopian tubes or sometimes when a man has a very low sperm count. This procedure involves removing eggs from the female and fertilizing the eggs outside the woman’s body. The fertilized egg is then reinserted in the woman’s uterus. The average cost of an IVF cycle in the U.S. is $10,000-15,000 and the average live delivery rate for IVF in 2005 was 31.6 percent per retrieval.  IVF makes up about 99 percent of artificial reproductive procedures. (ASRM, 2006-2010)

Less common procedures include  gamete intrafallopian tube transfer (GIFT) which involves implanting both sperm and ova into the fallopian tube and fertilization is allowed to occur naturally.  Zygote intrafallopian tube transfer (ZIFT) is another procedure in which sperm and ova are fertilized outside of the woman’s body and the fertilized egg or zygote is then implanted in the fallopian tube. This allows the zygote to travel down the fallopian tube and embed in the lining of the uterus naturally. 

Insurance coverage for infertility is required in fourteen states, but the amount and type of coverage available vary greatly (ASRM, 2006-2010). The majority of couples seeking treatment for infertility pay much of the cost. Consequently, infertility treatment is much more accessible to couples with higher incomes. However, grants and funding sour ces may be available for lower-income couples seeking infertility treatment.

Fertility for Singles and Same-Sex Couples

The journey to parenthood may look different for singles same-sex couples.  However, there are several viable options available to them to have their own biological children. Men and women may choose to donate their sperm or eggs to help others reproduce for monetary or humanitarian reasons. Some gay couples may decide to have a surrogate pregnancy. One or both of the men would provide the sperm and choose a carrier. The chosen woman may be the source of the egg and uterus or the woman could be a third party that carries the created embryo.

Reciprocal IVF is used by couples who both possess female reproductive organs. Using in vitro fertilization, eggs are removed from one partner to be used to make embryos that the other partner will hopefully carry in a successful pregnancy.

Artificial insemination  ( AI ) is the deliberate introduction of sperm into a female’s cervix or uterine cavity for the purpose of achieving a pregnancy through in vivo fertilization by means other than sexual intercourse. AI is most often used by single women who desire to give birth to their own child, women who are in a lesbian relationship, or women who are in a heterosexual relationship but with a male partner who is infertile or who has a physical impairment that prevents intercourse. The sperm used could be anonymous or from a known donor.

What you’ll learn to do: explain cognitive development in early adulthood

We have learned about cognitive development from infancy through adolescence, ending with Piaget’s stage of formal operations. Does that mean that cognitive development stops with adolescence? Couldn’t there be different ways of thinking in adulthood that come after (or “post”) formal operations?

In this section, we will learn about these types of postformal operational thought and consider research done by William Perry related to types of thought and advanced thinking. We will also look at education in early adulthood, the relationship between education and work, and some tools used by young adults to choose their careers.

• Distinguish between formal and postformal thought
• Describe cognitive development and dialectical thought during early adulthood

Cognitive Development in Early Adulthood

Beyond formal operational thought: postformal thought.

In the adolescence module, we discussed Piaget’s formal operational thought. The hallmark of this type of thinking is the ability to think abstractly or to consider possibilities and ideas about circumstances never directly experienced. Thinking abstractly is only one characteristic of adult thought, however. If you compare a 14-year-old with someone in their late 30s, you would probably find that the latter considers not only what is possible, but also what is likely. Why the change? The young adult has gained experience and understands why possibilities do not always become realities. This difference in adult and adolescent thought can spark arguments between the generations.

Here is an example. A student in her late 30s relayed such an argument she was having with her 14-year-old son. The son had saved a considerable amount of money and wanted to buy an old car and store it in the garage until he was old enough to drive. He could sit in it, pretend he was driving, clean it up, and show it to his friends. It sounded like a perfect opportunity. The mother, however, had practical objections. The car would just sit for several years while deteriorating. The son would probably change his mind about the type of car he wanted by the time he was old enough to drive and they would be stuck with a car that would not run. She was also concerned that having a car nearby would be too much temptation and the son might decide to sneak it out for a quick ride before he had a permit or license.

Piaget’s theory of cognitive development ended with formal operations, but it is possible that other ways of thinking may develop after (or “post”) formal operations in adulthood (even if this thinking does not constitute a separate “stage” of development). Postformal thought is practical, realistic, and more individualistic, but also characterized by understanding the complexities of various perspectives. As a person approaches the late 30s, chances are they make decisions out of necessity or because of prior experience and are less influenced by what others think. Of course, this is particularly true in individualistic cultures such as the United States. Postformal thought is often described as more flexible, logical, willing to accept moral and intellectual complexities, and dialectical than previous stages in development.

Perry’s Scheme

One of the first theories of cognitive development in early adulthood originated with William Perry (1970), who studied undergraduate students at Harvard University.  Perry noted that over the course of students’ college years, cognition tended to shift from dualism (absolute, black and white, right and wrong type of thinking) to multiplicity (recognizing that some problems are solvable and some answers are not yet known) to relativism (understanding the importance of the specific context of knowledge—it’s all relative to other factors). Similar to Piaget’s formal operational thinking in adolescence, this change in thinking in early adulthood is affected by educational experiences.

Dialectical Thought

In addition to moving toward more practical considerations, thinking in early adulthood may also become more flexible and balanced. Abstract ideas that the adolescent believes in firmly may become standards by which the individual evaluates reality. As Perry’s research pointed out, adolescents tend to think in dichotomies or absolute terms; ideas are true or false; good or bad; right or wrong and there is no middle ground. However, with education and experience, the young adult comes to recognize that there are some right and some wrong in each position. Such thinking is more realistic because very few positions, ideas, situations, or people are completely right or wrong.

Some adults may move even beyond the relativistic or contextual thinking described by Perry; they may be able to bring together important aspects of two opposing viewpoints or positions, synthesize them, and come up with new ideas. This is referred to as  dialectical thought and is considered one of the most advanced aspects of postformal thinking (Basseches, 1984). There isn’t just one theory of postformal thought; there are variations, with emphasis on adults’ ability to tolerate ambiguity or to accept contradictions or find new problems, rather than solve problems, etc. (as well as relativism and dialecticism that we just learned about). What they all have in common is the proposition that the way we think may change during adulthood with education and experience.

Learning Objectives

• Describe the role of parenting in early adulthood
• Differentiate between the various parenting styles

Having Children

Do you want children? Do you already have children? Increasingly, families are postponing or not having children. Families that choose to forego having children are known as childfree families, while families that want but are unable to conceive are referred to as childless families. As more young people pursue their education and careers, age at first marriage has increased; similarly, so has the age at which people become parents. With a college degree, the average age for women to have their first child is 30.3, but without a college degree, the average age is 23.8.  Marital status is also related, as the average age for married women to have their first child is 28.8, while the average age for unmarried women is 23.1. Overall, the average age of first-time mothers has increased to 26, up from 21 in 1972, and the average age of first-time fathers has increased to 31, up from 27 in 1972 in the United States. T he age of first-time parents in the U.S. increased sharply in the 1970s after abortion was legalized. Since the age of first-time parents varies by geographic region in the U.S. and women’s rights to abortion are being challenged in some states, it will be interesting to follow the norms and trends for first-time parents in the future.

The decision to become a parent should not be taken lightly. There are positives and negatives associated with parenting that should be considered. Many parents report that having children increases their well-being (White & Dolan, 2009). Researchers have also found that parents, compared to their non-parent peers, are more positive about their lives (Nelson, Kushlev, English, Dunn, & Lyubomirsky, 2013). On the other hand, researchers have also found that parents, compared to non-parents, are more likely to be depressed, report lower levels of marital quality, and feel like their relationship with their partner is more businesslike than intimate (Walker, 2011).

If you do become a parent, your parenting style will impact your child’s future success in romantic and parenting relationships. Recall from the module on early childhood that there are several different parenting styles.  Authoritative  parenting, arguably the best parenting style, is both demanding and supportive of the child (Maccoby & Martin, 1983). Support refers to the amount of affection, acceptance, and warmth a parent provides. Demandingness refers to the degree a parent controls their child’s behavior. Children who have authoritative parents are generally happy, capable, and successful (Maccoby, 1992).

Other, less advantageous parenting styles include authoritarian (in contrast to authorit ative ), permissive, and uninvolved (Tavassolie, Dudding, Madigan, Thorvardarson, & Winsler, 2016).  Authoritarian  parents are low in support and high in demandingness. Arguably, this is the parenting style used by Harry Potter’s harsh aunt and uncle, and Cinderella’s vindictive stepmother. Children who receive authoritarian parenting are more likely to be obedient and proficient but score lower in happiness, social competence, and self-esteem.  Permissive  parents are high in support and low in demandingness. Their children rank low in happiness and self-regulation and are more likely to have problems with authority.  Uninvolved  parents are low in both support and demandingness. Children of these parents tend to rank lowest across all life domains, lack self-control, have low self-esteem, and are less competent than their peers.

Support for the benefits of authoritative parenting has been found in countries as diverse as the Czech Republic (Dmitrieva, Chen, Greenberger, & Gil-Rivas, 2004), India (Carson, Chowdhurry, Perry, & Pati, 1999), China (Pilgrim, Luo, Urberg, & Fang, 1999), Israel (Mayseless, Scharf, & Sholt, 2003), and Palestine (Punamaki, Qouta, & Sarraj, 1997). In fact, authoritative parenting appears to be superior in Western, individualistic societies—so much so that some people have argued that there is no longer a need to study it (Steinberg, 2001). Other researchers are less certain about the superiority of authoritative parenting and point to differences in cultural values and beliefs. For example, while many European-American children do poorly with too much strictness (authorit arian  parenting), Chinese children often do well, especially academically. The reason for this likely stems from Chinese culture viewing strictness in parenting as related to training, which is not central to American parenting (Chao, 1994).

Class and Culture

The impact of class and culture cannot be ignored when examining parenting styles. It is assumed that authoritative styles are best because they are designed to help the parent raise a child who is independent, self-reliant, and responsible. These are qualities favored in “individualistic” cultures such as the United States, particularly by the middle class.

Authoritarian parenting has been used historically and reflects the cultural need for children to do as they are told. African-American, Hispanic, and Asian parents tend to be more authoritarian than non-Hispanic whites. In collectivistic cultures such as China or Korea, being obedient and compliant are favored behaviors. In societies where family members’ cooperation is necessary for survival, as in the case of raising crops, rearing children who are independent and who strive to be on their own makes no sense. But in an economy based on being mobile in order to find jobs and where one’s earnings are based on education, raising a child to be independent is very important.

Working-class parents are more likely than middle-class parents to focus on obedience and honesty when raising their children. In a classic study on social class and parenting styles called  Class and Conformity , Kohn (1977) explained that parents tend to emphasize qualities that are needed for their own survival when parenting their children. Working-class parents are rewarded for being obedient, reliable, and honest in their jobs. They are not paid to be independent or to question the management; rather, they move up and are considered good employees if they show up on time, do their work as they are told, and can be counted on by their employers. Consequently, these parents reward honesty and obedience in their children. Middle-class parents who work as professionals are rewarded for taking initiative, being self-directed, and assertive in their jobs. They are required to get the job done without being told exactly what to do. They are asked to be innovative and to work independently. These parents encourage their children to have those qualities as well by rewarding independence and self-reliance. Parenting styles can reflect many elements of culture.

The Development of Parents

Think back to an emotional event you experienced as a child. How did your parents react to you? Did your parents get frustrated or criticize you, or did they act patiently and provide support and guidance? Did your parents provide lots of rules for you or let you make decisions on your own? Why do you think your parents behaved the way they did?

Psychologists have attempted to answer these questions about the influences on parents and understand why parents behave the way they do. Because parents are critical to a child’s development, a great deal of research has been focused on the impact that parents have on children. Less is known, however, about the development of parents themselves and the impact of children on parents. Nonetheless, parenting is a major role in an adult’s life. Parenthood is often considered a normative developmental task of adulthood. Cross-cultural studies show that adolescents around the world plan to have children. In fact, most men and women in the United States will become parents by the age of 40 years (Martinez, Daniels, & Chandra, 2012).

People have children for many reasons, including emotional reasons (e.g., the emotional bond with children and the gratification the parent-child relationship brings), economic and utilitarian reasons (e.g., children provide help in the family and support in old age), and social-normative reasons (e.g., adults are expected to have children; children provide status) (Nauck, 2007).

The Changing Face of Parenthood

Parenthood is undergoing changes in the United States and elsewhere in the world. Children are less likely to be living with both parents, and women in the United States have fewer children than they did previously. The average fertility rate of women in the United States was about seven children in the early 1900s and has remained relatively stable at 2.1 since the 1970s (Hamilton, Martin, & Ventura, 2011; Martinez, Daniels, & Chandra, 2012). Not only are parents having fewer children, but the context of parenthood has also changed. Parenting outside of marriage has increased dramatically among most socioeconomic, racial, and ethnic groups, although college-educated women are substantially more likely to be married at the birth of a child than are mothers with less education (Dye, 2010). Parenting is occurring outside of marriage for many reasons, both economic and social. People are having children at older ages, too. Despite the fact that young people are more often delaying childbearing, most 18- to 29-year-olds want to have children and say that being a good parent is one of the most important things in life (Wang & Taylor, 2011).

Galinsky (1987) was one of the first to emphasize the development of parents themselves, how they respond to their children’s development, and how they grow as parents. Parenthood is an experience that transforms one’s identity as parents take on new roles. Children’s growth and development force parents to change their roles. They must develop new skills and abilities in response to children’s development. Galinsky identified six stages of parenthood that focus on different tasks and goals (see Table 2).

1. The Image-Making Stage

As prospective parents think about and form images about their roles as parents and what parenthood will bring, and prepare for the changes an infant will bring, they enter the image-making stage. Future parents develop their ideas about what it will be like to be a parent and the type of parent they want to be. Individuals may evaluate their relationships with their own parents as a model of their roles as parents.

2. The Nurturing Stage

The second stage, the nurturing stage, occurs at the birth of the baby. A parent’s main goal during this stage is to develop an attachment relationship with their baby. Parents must adapt their romantic relationships, their relationships with their other children, and with their own parents to include the new infant. Some parents feel attached to the baby immediately, but for other parents, this occurs more gradually. Parents may have imagined their infant in specific ways, but they now have to reconcile those images with their actual baby. In incorporating their relationship with their child into their other relationships, parents often have to reshape their conceptions of themselves and their identity. Parenting responsibilities are the most demanding during infancy because infants are completely dependent on caregiving.

3. The Authority Stage

The authority stage occurs when children are 2 years old until about 4 or 5 years old. In this stage, parents make decisions about how much authority to exert over their children’s behavior. Parents must establish rules to guide their child’s behavior and development. They have to decide how strictly they should enforce rules and what to do when rules are broken.

4. The Interpretive Stage

The interpretive stage occurs when children enter school (preschool or kindergarten) to the beginning of adolescence. Parents interpret their children’s experiences as children are increasingly exposed to the world outside the family. Parents answer their children’s questions, provide explanations, and determine what behaviors and values to teach. They decide what experiences to provide their children, in terms of schooling, neighborhood, and extracurricular activities. By this time, parents have experience in the parenting role and often reflect on their strengths and weaknesses as parents, review their images of parenthood, and determine how realistic they have been. Parents have to negotiate how involved to be with their children, when to step in, and when to encourage children to make choices independently.

5. The Interdependent Stage

Parents of teenagers are in the interdependent stage. They must redefine their authority and renegotiate their relationship with their adolescent as the children increasingly make decisions independent of parental control and authority. On the other hand, parents do not permit their adolescent children to have complete autonomy over their decision-making and behavior, and thus adolescents and parents must adapt their relationship to allow for greater negotiation and discussion about rules and limits.

6. The Departure Stage

During the departure stage of parenting, parents evaluate the entire experience of parenting. They prepare for their child’s departure, redefine their identity as the parent of an adult child, and assess their parenting accomplishments and failures. This stage forms a transition to a new era in parents’ lives. This stage usually spans a long time period from when the oldest child moves away (and often returns) until the youngest child leaves. The parenting role must be redefined as a less central role in a parent’s identity.

Despite the interest in the development of parents among laypeople and helping professionals, little research has examined developmental changes in parents’ experience and behaviors over time. Thus, it is not clear whether these theoretical stages are generalizable to parents of different races, ages, and religions, nor do we have empirical data on the factors that influence individual differences in these stages. On a practical note, how-to books and websites geared toward parental development should be evaluated with caution, as not all advice provided is supported by research.

Influences on Parenting

Parenting is a complex process in which parents and children influence one another. There are many reasons that parents behave the way they do. The multiple influences on parenting are still being explored. Proposed influences on parental behavior include 1) parent characteristics, 2) child characteristics, and 3) contextual and sociocultural characteristics (Belsky, 1984; Demick, 1999).

Parent Characteristics

Parents bring unique traits and qualities to the parenting relationship that affect their decisions as parents. These characteristics include the age of the parent, gender, beliefs, personality, developmental history, knowledge about parenting and child development, and mental and physical health. Parents’ personalities affect parenting behaviors. Mothers and fathers who are more agreeable, conscientious, and outgoing are warmer and provide more structure to their children. Parents who are more agreeable, less anxious, and less negative also support their children’s autonomy more than parents who are anxious and less agreeable (Prinzie, Stams, Dekovic, Reijntjes, & Belsky, 2009). Parents who have these personality traits appear to be better able to respond to their children positively and provide a more consistent, structured environment for their children.

Parents’ developmental histories, or their experiences as children, also affect their parenting strategies. Parents may learn parenting practices from their own parents. Fathers whose own parents provided monitoring, consistent and age-appropriate discipline, and warmth were more likely to provide this constructive parenting to their own children (Kerr, Capaldi, Pears, & Owen, 2009). Patterns of negative parenting and ineffective discipline also appear from one generation to the next. However, parents who are dissatisfied with their own parents’ approach may be more likely to change their parenting methods with their own children.

Child Characteristics

Parenting is bidirectional. Not only do parents affect their children, but children also influence their parents. Child characteristics, such as gender, birth order, temperament, and health status, affect parenting behaviors and roles. For example, an infant with an easy temperament may enable parents to feel more effective, as they are easily able to soothe the child and elicit smiling and cooing. On the other hand, a cranky or fussy infant elicits fewer positive reactions from his or her parents and may result in parents feeling less effective in the parenting role (Eisenberg et al., 2008). Over time, parents of more difficult children may become more punitive and less patient with their children (Clark, Kochanska, & Ready, 2000; Eisenberg et al., 1999; Kiff, Lengua, & Zalewski, 2011). Parents who have a fussy, difficult child are less satisfied with their marriages and have greater challenges in balancing work and family roles (Hyde, Else-Quest, & Goldsmith, 2004). Thus, child temperament is one of the child characteristics that influences how parents behave with their children.

Another child characteristic is the gender of the child. Parents respond differently to boys and girls. Parents often assign different household chores to their sons and daughters. Girls are more often responsible for caring for younger siblings and household chores, whereas boys are more likely to be asked to perform chores outside the home, such as mowing the lawn (Grusec, Goodnow, & Cohen, 1996). Parents also talk differently with their sons and daughters, providing more scientific explanations to their sons and using more emotion words with their daughters (Crowley, Callanan, Tenenbaum, & Allen, 2001).

Contextual Factors and Sociocultural Characteristics

The parent-child relationship does not occur in isolation. Sociocultural characteristics, including economic hardship, religion, politics, neighborhoods, schools, and social support, also influence parenting. Parents who experience economic hardship are more easily frustrated, depressed, and sad, and these emotional characteristics affect their parenting skills (Conger & Conger, 2002). Culture also influences parenting behaviors in fundamental ways. Although promoting the development of skills necessary to function effectively in one’s community is a universal goal of parenting, the specific skills necessary vary widely from culture to culture. Thus, parents have different goals for their children that partially depend on their culture (Tamis-LeMonda et al., 2008). For example, parents vary in how much they emphasize goals for independence and individual achievements, and goals involving maintaining harmonious relationships and being embedded in a strong network of social relationships. These differences in parental goals are influenced by culture and by immigration status. Other important contextual characteristics, such as the neighborhood, school, and social networks, also affect parenting, even though these settings don’t always include both the child and the parent (Brofenbrenner, 1989). For example, Latina mothers who perceived their neighborhood as more dangerous showed less warmth with their children, perhaps because of the greater stress associated with living a threatening environment (Gonzales et al., 2011). Many contextual factors influence parenting.

Child Care Concerns

About 75.7 percent of mothers of school-aged and 65.1 percent of mothers of preschool-aged children in the United States work outside the home. Since more women have been entering the workplace, there has been a concern that families do not spend as much time with their children. This, however, may not be true. Between 1981 and 1997, the amount of time that parents spent with children increased overall (Sandberg and Hofferth, 2001). Modern numbers for this vary widely, as many parents who work outside of the home also devote significant amounts of time to childcare, to 14 hours a week, compared with 10 in 1965. The amount of this time that is undistracted and involved may be close to 34 minutes a day.

Seventy-five percent of children under age 5 are in scheduled child care programs. Others are cared for by family members, friends, or are in Head Start Programs. Older children are often in after school programs, before school programs, or stay at home alone after school once they are older. Quality childcare programs can enhance a child’s social skills and can provide rich learning experiences. But long hours in poor quality care can have negative consequences for young children in particular. What determines the quality of child care? One very important consideration is the teacher/child ratio. States specify the maximum number of children that can be supervised by one teacher. In general, the younger the children, the more teachers required for a given number of children. The lower the teacher to child ratio, the more time the teacher has for involvement with the children and the less stressed the teacher may be so that the interactions can be more relaxed, stimulating, and positive. The more children there are in a program, the less desirable the program as well. This is because the center may be more rigid in rules and structure to accommodate a large number of children in the facility.

The physical environment should be colorful, stimulating, clean, and safe. The philosophy of the organization and the curriculum available should be child-centered, positive, and stimulating. Providers should be trained in early childhood education as well. A majority of states do not require training for their child care providers. And while a formal education is not required for a person to provide a warm, loving relationship to a child, knowledge of a child’s development is useful for addressing their social, emotional, and cognitive needs in an effective way. By working toward improving the quality of childcare and increasing family-friendly workplace policies, such as more flexible scheduling and perhaps childcare facilities at places of employment, we can accommodate families with smaller children and relieve parents of the stress sometimes associated with managing work and family life.

Learning and Behavior Modification

Parenting and behaviorism.

Parenting generally involves many opportunities to apply principles of behaviorism, especially operant conditioning. In discussing operant conditioning, we use several everyday words—positive, negative, reinforcement, and punishment—in a specialized manner. In operant conditioning, positive and negative do not mean good and bad. Instead,  positive  means you are adding something, and  negative  means you are taking something away.  Reinforcement  means you are increasing a behavior, and  punishment  means you are decreasing a behavior. Reinforcement can be positive or negative, and punishment can also be positive or negative. All reinforcers (positive or negative)  increase  the likelihood of a behavioral response. All punishers (positive or negative)  decrease  the likelihood of a behavioral response. Now let’s combine these four terms: positive reinforcement, negative reinforcement, positive punishment, and negative punishment. (See table below.)

The most effective way to teach a person or animal a new behavior is with positive reinforcement. In  positive reinforcement , a stimulus is added to the situation to increase a behavior. Parents and teachers use positive reinforcement all the time, from offering dessert after dinner, praising children for cleaning their room or completing some work, offering a toy at the end of a successful piano recital, or earning more time for recess. The goal of providing these forms of positive reinforcement is to increase the likelihood of the same behavior occurring in the future.

Positive reinforcement is an extremely effective learning tool, as evidenced by nearly 80 years worth of research. That said, there are many ways to introduce positive reinforcement into a situation. Many people believe that reinforcers must be tangible, but research shows that verbal praise and hugs are very effective reinforcers for people of all ages. Further, research suggests that constantly providing tangible reinforcers may actually be counterproductive in certain situations. For example, paying children for their grades may undermine their intrinsic motivation to go to school and do well. While children who are paid for their grades may maintain good grades, it is to receive the reinforcing pay, not because they have an intrinsic desire to do well. The impact is especially detrimental to students who initially have a high level of intrinsic motivation to do well in school. Therefore, we must provide appropriate reinforcement, and be careful to ensure that the reinforcement does not undermine intrinsic motivation.

In  negative reinforcement , an aversive stimulus is removed to increase a behavior. For example, car manufacturers use the principles of negative reinforcement in their seatbelt systems, which go “beep, beep, beep” until you fasten your seatbelt. The annoying sound stops when you exhibit the desired behavior, increasing the likelihood that you will buckle up in the future. Negative reinforcement is also used frequently in horse training. Riders apply pressure—by pulling the reins or squeezing their legs—and then remove the pressure when the horse performs the desired behavior, such as turning or speeding up. The pressure is the negative stimulus that the horse wants to remove.

Sometimes, adding something to the situation is reinforcing as in the cases we described above with cookies, praise, and money. Positive reinforcement involves adding something to the situation in order to encourage a behavior. Other times, taking something away from a situation can be reinforcing. For example, the loud, annoying buzzer on your alarm clock encourages you to get up so that you can turn it off and get rid of the noise. Children whine in order to get their parents to do something and often, parents give in just to stop the whining. In these instances, children have used negative reinforcement to get what they want.

Operant conditioning tends to work best if you focus on encouraging a behavior or moving a person into the direction you want them to go rather than telling them what not to do. Reinforcers are used to encourage behavior; punishers are used to stop the behavior. A punisher is anything that follows an act and decreases the chance it will reoccur. As with reinforcement, there are also two types of punishment: positive and negative.

Positive punishment involves adding something in order to decrease the likelihood that a behavior will occur again in the future. Spanking is an example of positive punishment. Receiving a speeding ticket is also an example of positive punishment. Both of these punishers, the spanking and the speeding ticket, are intended to decrease the reoccurrence of the related behavior.

Negative punishment involves removing something that is desired in order to decrease the likelihood that a behavior will occur again in the future. Putting a child in time out can serve as a negative punishment if the child enjoys social interaction. Taking away a child’s technology privileges can also be a negative punishment. Taking away something desired encourages the child to refrain from engaging in that behavior again to avoid losing the desired object or activity.

Often, punished behavior doesn’t really go away. It is just suppressed and may reoccur whenever the threat of punishment is removed. For example, a child may not cuss around you because you’ve washed his mouth out with soap, but he may cuss around his friends. A motorist may only slow down when the trooper is on the side of the freeway. Another problem with punishment is that when a person focuses on punishment, they may find it hard to see what the other does right or well. Punishment is stigmatizing; when punished, some people start to see themselves as bad and give up trying to change.

Reinforcement can occur in a predictable way, such as after every desired action is performed (called continuous reinforcement), or intermittently, after the behavior is performed a number of times or the first time it is performed after a certain amount of time (called partial reinforcement whether based on the number of times or the passage of time). The schedule of reinforcement has an impact on how long a behavior continues after reinforcement is discontinued. So a parent who has rewarded a child’s actions each time may find that the child gives up very quickly if a reward is not immediately forthcoming. Children will learn quickest under a continuous schedule of reinforcement. Then the parent should switch to a partial reinforcement schedule to maintain the behavior .

Everyday Connection: Behavior Modification in Children

Parents and teachers often use behavior modification to change a child’s behavior. Behavior modification uses the principles of operant conditioning to accomplish behavior change so that undesirable behaviors are switched for more socially acceptable ones. Some teachers and parents create a sticker chart, in which several behaviors are listed. Sticker charts are a form of token economies. Each time children perform the behavior, they get a sticker, and after a certain number of stickers, they get a prize or reinforcer. The goal is to increase acceptable behaviors and decrease misbehavior. Remember, it is best to reinforce desired behaviors, rather than to use punishment. In the classroom, the teacher can reinforce a wide range of behaviors, from students raising their hands to walking quietly in the hall, to turning in their homework. Parents might create a behavior chart at home that rewards children for things such as putting away toys, brushing their teeth, and helping with dinner. For behavior modification to be effective, the reinforcement needs to be connected with the behavior; the reinforcement must matter to the child and be provided consistently.

Time-out is another popular technique used in behavior modification with children. It operates on the principle of negative punishment. When a child demonstrates an undesirable behavior, she is removed from the desirable activity at hand. For example, say that Sophia and her brother Mario are playing with building blocks. Sophia throws some blocks at her brother, so you give her a warning that she will go to time-out if she does it again. A few minutes later, she throws more blocks at Mario. You remove Sophia from the room for a few minutes. When she comes back, she doesn’t throw blocks.

There are several important points that you should know if you plan to implement time-out as a behavior modification technique. First, ensure the child is removed from a desirable activity and placed in a less desirable location. If the activity is something undesirable for the child, this technique will backfire because it is more enjoyable for the child to be removed from the activity. Second, the length of the time-out is important. The general rule of thumb is one minute for each year of the child’s age. Sophia is five; therefore, she sits in a time-out for five minutes. Setting a timer helps children know how long they have to sit in time-out. Finally, as a caregiver, keep several guidelines in mind over the course of a time-out: remain calm when directing your child to time-out; ignore your child during a time-out (because caregiver attention may reinforce misbehavior), and give the child a hug or a kind word when time-out is over.

Do parents socialize children or do children socialize parents?

Bandura’s (1986) findings suggest that there is interplay between the environment and the individual. We are not just the product of our surroundings, rather we influence our surroundings. There is an interplay between our personality, how we interpret events, and how they influence us. This concept is called reciprocal determinism . An example of this might be the interplay between parents and children. Parents not only influence their child’s environment, perhaps intentionally through the use of reinforcement, etc., but children influence parents as well. Parents may respond differently to their first child than with their fourth. Perhaps they try to be the perfect parents with their firstborn, but by the time their last child comes along, they have very different expectations of themselves and their child. Our environment creates us and we create our environment. Today there are numerous other social influences, from TV, games, the Internet, i-pads, phones, social media, influencers, advertisements, etc.

learning outcomes

• Summarize Levinson’s theory of early adulthood transitions

Theories of Early Adult Psychosocial Development

Gaining adult status.

Many of the developmental tasks of early adulthood involve becoming part of the adult world and gaining independence. Young adults sometimes complain that they are not treated with respect, especially if they are put in positions of authority over older workers. Consequently, young adults may emphasize their age to gain credibility from even slightly younger people. “You’re only 23? I’m 27!” a young adult might exclaim. [Note: This kind of statement is much less likely to come from someone in their 40s!]

The focus of early adulthood is often on the future. Many aspects of life are on hold while people go to school, go to work, and prepare for a brighter future. There may be a belief that the hurried life now lived will improve ‘as soon as I finish school’ or ‘as soon as I get promoted’ or ‘as soon as the children get a little older.’ As a result, time may seem to pass rather quickly. The day consists of meeting many demands that these tasks bring. The incentive for working so hard is that it will all result in a better future.

Levinson’s Theory

In 1978, Daniel Levinson published a book entitled, The Seasons of a Man’s Life  in which he presented a theory of development in adulthood. Levinson’s work was based on in-depth interviews with 40 men between the ages of 35-45. According to Levinson, young adults have an image of the future that motivates them. This image is called “the dream” and for the men interviewed, it was a dream of how their career paths would progress and where they would be at midlife.   Dreams are very motivating. Dreams of a home bring excitement to couples as they look, save, and fantasize about how life will be. Dreams of careers motivate students to continue in school as they fantasize about how much their hard work will pay off. Dreams of playgrounds on a summer day inspire would-be parents. A dream is perfect and retains that perfection as long as it remains in the future. But as the realization of it moves closer, it may or may not measure up to its image. If it does, all is well. But if it does not, the image must be replaced or modified. And so, in adulthood, plans are made, efforts follow, and plans are reevaluated. This creating and recreating characterizes Levinson’s theory. (The shift from idealistic dreams to more realistic experiences might remind us of the cognitive development progression from formal to postformal thought in adulthood.)

Levinson’s stages (at least up to midlife) are presented below (Levinson, 1978). He suggested that periods of transition last about five years and periods of stability last about seven years. The ages presented below are based on life in the middle-class several decades ago. Think about how these ages and transitions might be different today, or in other cultures, or for women compared to men.

• Early adult transition (17-22): Leaving home, leaving family; making first choices about career and education
• Entering the adult world (22-28): Committing to an occupation, defining goals, finding intimate relationships
• Age 30 transition (28-33): Reevaluating those choices and perhaps making modifications or changing one’s attitude toward love and work
• Settling down (33 to 40): Reinvesting in work and family commitments; becoming involved in the community
• Midlife transition (40-45): Reevaluating previous commitments; making dramatic changes if necessary; giving expression to previously ignored talents or aspirations; feeling more of a sense of urgency about life and its meaning
• Entering middle adulthood (45-50): Committing to new choices made and placing one’s energies into these commitments

Nearly twenty years after his original research, Levinson interviewed 45 women ages 35-45 and published the book, The seasons of a woman’s life.  He reported similar patterns with women, although women held a “split dream”—an image of the future in both work and family life and a concern with the timing and coordination of the two. Traditionally, by working outside the home, men were seen as taking care of their families. However, for women, working outside the home and taking care of their families were perceived as separate and competing for their time and attention. Hence, one aspect of the women’s dreams was focused on one goal for several years and then their time and attention shifted towards the other, often resulting in delays in women’s career dreams.

Adulthood is a period of building and rebuilding one’s life. Many decisions in early adulthood are made before a person has had enough experience to really understand the consequences of such decisions. And, perhaps, many of these initial decisions are made with one goal in mind – to be se en as an adult. As a result, early decisions may be driven more by the expectations of others. For example, imagine someone who chose a career path based on other’s advice but now finds that the job is not what was expected. 

The age 30 transition may involve recommitting to the same job, not because it’s stimulating, but because it pays well; or the person may decide to return to school and change careers. Settling down may involve settling down with a new set of expectations. As the adult gains status, he or she may be freer to make more independent choices. And sometimes these are very different from those previously made. The midlife transition differs from the age 30 transition in that the person is more aware of how much time has gone by and how much time is left. This brings a sense of urgency and impatience about making changes. The future focus of early adulthood gives way to an emphasis on the present in midlife–we will explore this in our next module. Overall, Levinson calls our attention to the dynamic nature of adulthood.

• How well do you think Levinson’s theory translates culturally? Do you think that personal desire and a concern with reconciling dreams with the realities of work and family is equally important in all cultures? Do you think these considerations are equally important in all social classes, races, and ethnic groups? Why or why not? How might this model be modified in today’s economy?

As we have learned in this module, young adults are often in the “prime of life,” especially physically and sexually. However, young adults may be engaged in risky behaviors and be particularly vulnerable to injuries, accidents, alcohol, and drug use/abuse, sexually transmitted diseases, rape, and suicide. Nutrition and exercise habits in this stage are important since they are associated with health and certain illnesses in middle age. Cognitive and brain development continues, with the influences of education and experience. Young adults may move from formal logical thinking to postformal thinking, becoming better at considering multiple perspectives and contexts, appreciating ambiguity and uncertainty, and using practical experience in making decisions.

Higher education plays an important role for more and more young adults—in this module we examined the connections between education and work and learned about how exploring and choosing one’s career is key during this stage. We saw that establishing intimacy in friendships, romance, and family relationships is another significant aspect of young adulthood; love, dating, cohabitation, marriage, and becoming parents were all examined.

We were introduced to the major theories of adult development, primarily those of Erikson and Levinson, and we learned about Arnett’s “emerging adulthood,” a potentially new stage involving the transition from adolescence to young adulthood, with young adults taking on “adult roles” later than expected. By the late thirties, though, most young adults have become independent of their parents/families of origin and are in the throes of adult work, family, and community activities and responsibilities.

Please read the article below for a summary of some of these early adulthood topics, but from a slightly different perspective—that of generations or cohorts. “Millennials” are defined as individuals who were born between 1981 and 1996, and as such, they make up a large part of today’s young adults. Read about this group regarding education, work, finances, living with parents, getting married, and having children, comparing their norms with those of previous generations and potentially future generations of young adults. Consider “emerging adulthood”; how much do you think generation, history, and culture affect this observed phenomenon? Will it continue to be part of early adulthood development in the future? Why or why not?

Link to Learning: Millenials and other generations

Read this article “ Millennial life: How young adulthood today compares with prior generations ” from the Pew Research Center.

Additional Supplemental Resources

• Read about social and demographic trends in the article “Millennial life: How young adulthood today compares with prior generations”
• Why do we crave love so much, even to the point that we would die for it? To learn more about our very real, very physical need for romantic love, Helen Fisher and her research team took MRIs of people in love — and people who had just been dumped.
• Attachment theory refers to a set of ideas formulated by psychologists in the 1960s that gives us an exceptionally useful guide to how we behave in relationships. Knowing whether we are secure, anxious, or avoidant in our attachment patterns gives us a vocabulary with which to get on top of some very tricky dynamics and helps us grow into more predictable and more joyful companions in love.

Erikson on Intimacy vs Isolation

• Listen to Erik Erikson explain this stage in his theory of psychosocial development in his own words.

Why do we love? A philosophical inquiry – Skye C. Cleary

• Ah, romantic love; beautiful and intoxicating, heart-breaking and soul-crushing… often all at the same time! If romantic love has a purpose, neither science nor psychology has discovered it yet – but over the course of history, some of our most respected philosophers have put forward some intriguing theories. Skye C. Cleary outlines five of these philosophical perspectives on why we love.

The Use of Reinforcement and Punishment in Shaping a Child’s Behavior When a child throws a tantrum, a parent’s sympathetic reaction may only serve to increase such outbursts. More appropriate behavior, though, can be strengthened through negative reinforcement, for example, a reward for improvement in demeanor after a fit of temper.

Parenting styles and their effects on children.

• In this video, we continue our discussion of developmental–or child–psychology by learning about parenting styles. We’ll focus specifically on the outcomes associated with each parenting style, as well as which parenting style is best long term.

Lifespan Development Copyright © 2020 by Julie Lazzara is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Cognitive Predictors of Everyday Problem Solving across the Lifespan

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Xi Chen , Christopher Hertzog , Denise C. Park; Cognitive Predictors of Everyday Problem Solving across the Lifespan. Gerontology 14 June 2017; 63 (4): 372–384. https://doi.org/10.1159/000459622

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Background: An important aspect of successful aging is maintaining the ability to solve everyday problems encountered in daily life. The limited evidence today suggests that everyday problem solving ability increases from young adulthood to middle age, but decreases in older age. Objectives: The present study examined age differences in the relative contributions of fluid and crystallized abilities to solving problems on the Everyday Problems Test (EPT). We hypothesized that due to diminishing fluid resources available with advanced age, crystallized knowledge would become increasingly important in predicting everyday problem solving with greater age. Method: Two hundred and twenty-one healthy adults from the Dallas Lifespan Brain Study, aged 24-93 years, completed a cognitive battery that included measures of fluid ability (i.e., processing speed, working memory, inductive reasoning) and crystallized ability (i.e., multiple measures of vocabulary). These measures were used to predict performance on EPT. Results: Everyday problem solving showed an increase in performance from young to early middle age, with performance beginning to decrease at about age of 50 years. As hypothesized, fluid ability was the primary predictor of performance on everyday problem solving for young adults, but with increasing age, crystallized ability became the dominant predictor. Conclusion: This study provides evidence that everyday problem solving ability differs with age, and, more importantly, that the processes underlying it differ with age as well. The findings indicate that older adults increasingly rely on knowledge to support everyday problem solving, whereas young adults rely almost exclusively on fluid intelligence.

An important aspect of successful aging is maintaining the ability to solve everyday problems encountered in daily life. Instrumental Activities of Daily Living (IADLs) represent one important domain of these problems. IADLs are complex behaviors required for independent management of one's life, including adherence to complex medical regimens, ability to use increasingly complex communication devices, and management of financial resources [ 1 ]. Other everyday problems involve situations where a conflict is present or a goal cannot be reached without some inferential reasoning [ 2 ]. Cross-sectional data show that the practical ability to solve everyday problems increases from young adulthood until middle age [ 3,4,5 ], but that older age is characterized by diminishing performance [ 4,5,6,7 ].

One reason for peak performance during middle adulthood in everyday problem solving may be that middle-aged adults have the ideal balance of fluid and crystallized resources needed for everyday problem solving. Crystallized ability represents accumulated experience and knowledge of the world, and is typically measured by vocabulary and general knowledge. It does not decline, and may even grow, well into late adulthood [ 8,9 ]. In contrast, fluid ability - the ability to abstract and perform efficient mental operations - shows consistent age-related decline beginning in the 20s [ 9 ], but nevertheless, performance is still relatively high in middle-aged adults [ 10 ]. Fluid ability is best measured by different types of inductive and deductive reasoning tasks, and is closely related to the construct of processing resources [ 11 ] as operationalized by working memory [ 12 ].

Previous studies have found fluid ability to be an important predictor of everyday problem solving in healthy older adults [ 6,7,13,14,15,16,17 ]. Gross et al. [ 15 ] found that although memory, reasoning, and processing speed were all significant predictors of everyday functioning and everyday problem solving, inductive reasoning (measured by Letter Series, Word Series, and Letter Sets tasks) independently accounted for the most variance in everyday functioning (measured by the Everyday Problems Test [EPT], the Observed Tasks of Daily Living [OTDL], and the Timed Instrumental Activities of Daily Living test). Willis et al. [ 18 ] also showed that older adults who underwent reasoning training showed less functional decline in IADLs than an untrained control group, indicating the importance of reasoning for everyday problem solving.

Everyday problem solving is also related to other aspects of fluid ability that decline with age, especially working memory and processing speed. Importantly, age-related decreases in working memory, using traditional measures that include Reading Span, Computation Span, and Operation Span tasks, have been strongly associated with lower performance on everyday problem solving tasks [ 13,19 ]. Age-related slowing in processing speed has also been associated with decreased everyday problem solving [ 7,20 ]. Rebok et al. [ 21 ] reported evidence that older adults who had extensive training on processing speed in the ACTIVE trial reported less difficulty in performing IADLs 10 years after training, suggesting that such an intervention confers protection in later life. In sum, there is little doubt that fluid ability plays an important role in everyday problem solving.

What is less certain is the role that crystallized ability and knowledge play in everyday problem solving. There have been a few studies that examined the joint contributions of both fluid ability and crystallized ability to everyday problem solving, and all suggest an important role of fluid ability [ 6,13,14,16,20,22,23 ]. However, the importance of crystallized ability in everyday problem solving seems to be different depending on the age range of the sample included in the study. Three studies in older adults all found that both fluid and crystallized ability played very important roles in everyday problem solving. Diehl et al. [ 14 ] used structural equation modeling and found that both fluid and crystallized abilities had significant paths to everyday problem solving, measured by OTDL. In addition, the effects of memory and speed on OTDL were mediated by crystallized ability, indexed by vocabulary. Burton et al. [ 20 ] used hierarchical regression and found that verbal ability, measured by verbal fluency and vocabulary tasks, predicted performance in EPT beyond the effect of fluid ability and demographic variables (e.g., age, education). Allaire and Marsiske [ 13 ] also found a relationship between vocabulary and some domains of everyday problem solving, measured by Everyday Cognition Battery. However, studies including middle-aged adults yielded somewhat different conclusions on the role of crystallized ability in the relation to everyday problem solving. Kimbler [ 6 ] studied healthy middle-aged and older adults (age 50-92 years) and found no relationship between performance in vocabulary and EPT. Thornton et al. [ 23 ] reported that, although in a sample of healthy adults and chronic disease patients, crystallized ability (measured by Educational Testing Service [ETS] vocabulary) mediated age effects on performance in EPT, the relationship was not significant when the analysis was limited only to healthy adults.

These findings suggest that there is a discrepancy in the role of crystallized ability in predicting everyday problem solving across the adulthood lifespan. A potential explanation is that there may be an age-related shift in the contribution of fluid versus crystallized abilities in solving everyday problems. This shift can only be detected by using a lifespan sample with a broader age range. We are aware of only 2 adult lifespan studies on the cognitive predictors of performance in everyday problem solving [ 5,22 ]. In both studies, the correlation of fluid and crystallized cognitive predictors to everyday problem solving (practical problem solving in [ 5 ]) was significant. However, when the effects of age and education on everyday problem solving were controlled, neither predictor was significant [ 5 ], limiting the understanding of their respective contribution. Moreover, we were unable to find any studies that include young, middle-age and older adults that examined how age affects the contribution of cognitive predictors to everyday problem solving. Therefore, the present study focuses on 2 important and unresolved issues. First, what is the strength of the contributions of fluid and crystallized abilities to everyday problem solving? And second, do these contributions shift in importance as a function of age?

Park [ 24 ] has argued that older adults maintain performance on many cognitive tasks by increasingly relying on knowledge and experience to compensate for declines in fluid abilities. Congruent with this perspective, Baltes et al. [ 25 ] also suggested that crystallized ability can compensate, to some extent, for age-related declines in processing efficiency with advanced age. In support of this theorizing, Hedden et al. [ 26 ] reported that performance on a verbal memory task was mediated by fluid abilities in young and middle-aged adults, but that older adults relied more on vocabulary (an index of crystallized ability) for optimal performance. In the present study, we determine whether such an age-related shift occurs for everyday problem solving in an adult lifespan sample. We predicted that young adults who are rich in cognitive resources such as speed, working memory and reasoning would rely on fluid processing for success; however, as age increased, crystallized ability would play an increasingly important role in everyday problem solving.

Participants

A total of 221 healthy adults from the Dallas Lifespan Brain Study (DLBS; 148 women, 73 men, age range: 24-93 years, Mini-Mental Status Examination scores ≥26, mean = 28.37) were recruited locally from the community. All participants were right-handed with normal or corrected to normal vision. Participants with any of following conditions were excluded: history of major psychiatric or neurological disorder, history of prescription drug abuse/illegal drug use, and/or any head trauma. Participants were compensated USD 15.00 per hour for their participation. They completed two 2.5-h sessions that are described below.

Each participant completed a battery of cognitive tests as well as the EPT [ 27 ]. This comprehensive battery included both paper-and-pencil and computerized tasks. The cognitive constructs assessed and the tasks associated with each construct included the following:

Processing speed was measured by Digit Comparison [ 28 ], WAIS-III Digit Symbol [ 29 ], and Pattern Comparison task taken from NIH Toolbox Cognition Battery (NIHTB-CB) [ 30 ].

Working memory was measured by the Spatial Working Memory (SWM) task of Cambridge Neuropsychological Test Automated Battery (CANTAB) [ 31 ], WAIS-III Letter-Number Sequencing [ 29 ], and NIHTB-CB List Sorting [ 30 ].

Inductive reasoning was measured by ETS Letter Sets [ 32 ], Raven's Progressive Matrices [ 33 ], and Stockings of Cambridge (SOC) of CANTAB [ 31 ].

Crystallized ability was measured by NIHTB-CB Picture Vocabulary [ 30 ], NIHTB-CB Oral Reading Recognition [ 30 ], and the ETS Advanced Vocabulary Scale [ 32 ]. Although the ETS Vocabulary task was timed, we made sure that no participants failed to complete the task because of the time limit, so the performance on this task was not affected by their speed.

Everyday problem solving ability was measured by EPT [ 27 ]. It is a paper-pencil task that has 42 questions, which assesses the ability to solve tasks that are important to live independently in our society. The EPT is comprised of 7 scales that include problems from the domains of Health/Medications, Meal Preparation/Nutrition, Phone Usage, Consumer/Shopping, Financial Management, Household Management, and Transportation. For each of these 7 scales, participants are presented with 3 sample stimuli (e.g., prescription drug label, bus schedule, catalog order form) and 2 questions about each stimulus. Figure 1 is an example of one EPT stimulus with 2 questions based on the stimulus. The performance on this task is measured as the total number of correct answers to the 42 questions. Compared to other neuropsychological tasks that assessed traditional problem solving ability, EPT was designed to be a better indicator of problem solving performance in real life. Schmitter-Edgecombe et al. [ 34 ] found that EPT was strongly associated with directly observed everyday functioning performance in real world, and therefore considered to be a valid and useful measure for assessing everyday functioning in cognitively healthy older population.

Example questions of the Everyday Problems Test.

Data Analyses

Altogether, there were 13 tasks subjected to analyses: 3 measures each for processing speed, working memory, inductive reasoning, crystallized ability, and a single measure of everyday problem solving. We created standard scores for the 12 cognitive measures that were used for further analyses. A confirmatory factor analysis validated the expected factor structure of cognitive measures (χ 2 (60) = 147.941, p < 0.001, CFI = 0.953, RMSEA = 0.081, SRMR = 0.076; Fig. 2 ). The standardized scores for each crystallized and fluid test were averaged to produce composite crystallized and fluid scores in a standard score (z-score) metric in the aggregate cross-sectional sample.

Confirmatory factor analysis of cognitive tasks, after controlling for age. χ 2 (60) = 147.941, p < 0.001, CFI = 0.953, RMSEA = 0.081, SRMR = 0.076.

To test the hypothesis that crystallized intelligence would be a more potent predictor of everyday problem solving for older adults, relative to earlier ages, we conducted a hierarchical moderated regression analysis with age, fluid ability, and crystallized ability as predictors using product variables to capture interactions. Prior to evaluating the interaction effects, we introduced quadratic age effects to test for possible curvilinearity in the relation of age and the variables to the EPT score. This approach was taken because curvilinear age relations were expected in abilities [ 35 ] and everyday problem solving performance, and because methodological studies have shown that failing to account for curvilinear relations of predictors to dependent variables in the context of moderated regression can create spurious product variable effects that are an artifact of curvilinear relations of both predictors to the dependent variable [ 36 ]. To foreshadow our results, we did detect curvilinear relations of age and abilities to EPS tests, requiring that moderated regression tests for age × ability interaction effects include quadratic terms for each predictor variable.

Linear predictor terms were first centered at the sample mean, and then squared predictors were computed to reduce collinearity issues in the multiple regression. Significant moderated regression effects were decomposed by computing simple slopes at the mean and at ±1 SD of the predictor variables.

To further understand age differences in the predictive utility of fluid and crystallized abilities for everyday problem solving, we used bootstrapping to examine the regression coefficients for each of the 3 age groups (young, middle-aged, older). Finally, to assess the stability of the observed effects across individual problem solving domains, we examined the contributions of fluid and crystallized abilities for each of the 7 domains in everyday problem solving for young, middle-aged, and older adults.

Demographic Data and Age-Related Differences

Demographic data are presented in Table 1 , broken down by 3 age groups (young: 24-49 years old; middle: 50-69 years old; old: 70-93 years old). The 3 age groups differed on years of formal education ( F (2, 218) = 6.16, p = 0.002), with young adults having somewhat higher levels than the other 2 age groups. Means and standard deviations of cognitive measures and EPT scores are also presented in Table 1 . For descriptive purposes, we presented age effects associated with fluid ability, crystallized ability and everyday problem solving score in scatter plots (Fig. 3 ). Figure 3 a portrays a significant linear age-related decrease in fluid ability ( R 2 = 0.626, R 2 adjusted = 0.625, F (1, 212) = 355.312, p < 0.001), and the quadratic relationship was also statistically significant ( R 2 = 0.64, R 2 adjusted = 0.637, F (2, 211) = 187.72, p < 0.001). In contrast, crystallized ability (Fig. 3 b) did not have a significant linear relationship with age ( p = 0.628). However, there was a significant quadratic relationship between crystallized ability and age ( R 2 = 0.038, R 2 adjusted = 0.029, F (2, 217) = 4.258, p = 0.015), with increasing performance until about age 59. We also examined both linear and quadratic relationships between everyday problem solving ability and age. While the simple linear relationship showed significance ( R 2 = 0.237, R 2 adjusted = 0.234, F (1, 219) = 68.091, p < 0.001), adding age 2 significantly improved the model (Δ R 2 = 0.105, Δ F = 34.810, p < 0.001), suggesting a quadratic relation with age was a better fit for everyday problem solving ability (Fig. 3 c) ( R 2 = 0.342, R 2 adjusted = 0.336, F (2, 218) = 56.707, p < 0.001), with the peak performance at 47.2 years of age.

Demographic and descriptive data ( n = 221)

a Age-related differences in fluid ability. Fluid ability is comprised of the measures on processing speed, working memory, and inductive reasoning. b Age-related differences in crystallized ability. Crystallized ability is comprised of ETS Advanced Vocabulary Scale, NIHTB-CB Picture Vocabulary, and NIHTB-CB Oral Reading Recognition. c Age-related differences in everyday problem solving. Everyday problem solving is measured by number of correct answers on the Everyday Problems Test (EPT).

Cognitive Predictors across the Lifespan

We used hierarchical multiple regression to examine the role that fluid and crystallized abilities play in solving everyday problems. In the first model, we included years of education and linear and quadratic components for age. Then in the second model, we added fluid ability and crystallized ability as cognitive predictors. In the third model, we included quadratic components (crystallized 2 and fluid 2 ) to examine if there was a curvilinear relationship between cognitive predictors and everyday problem solving. In the fourth model, we added interactions among fluid ability, crystallized ability and age. Each of the aforementioned steps improved the fit of the overall model significantly (Table 2 ). We also examined a further model that included interactions between cognitive ability and age 2 , and found that it did not improve the model significantly. Therefore, the fourth model was chosen as the final model depicting the relationship between cognitive predictors and everyday problem solving across the lifespan.

Hierarchical multiple regression

As shown in Table 2 , model 4 explained a substantial amount of variance in everyday problem solving ( R 2 = 0.683, R 2 adjusted = 0.666). There was a main effect of age, age 2 , fluid ability, and crystallized ability on everyday problem solving. Although the quadratic terms of fluid ability and crystallized ability were not each statistically significant in the final model, adding quadratic terms of these predictors significantly improved the fit of the model. The partial residual plots of crystallized ability (Fig. 4 a) and fluid ability (Fig. 4 b) showed that these 2 predictors both evidenced a similar curvilinear pattern to everyday problem solving. Curvilinearity occurred because for lower-ability participants compared to those of higher ability, cognitive ability had a stronger relationship to everyday problem solving.

a Partial residual plot of crystallized ability. b Partial residual plot of fluid ability. For both cognitive predictors, the effect of crystallized and fluid ability follows a similar curvilinear pattern regardless of age and the other cognitive level: for people who have lower cognitive ability, the level of cognitive ability has a strong effect on everyday problem solving, while for people who have high cognitive ability, higher cognitive ability does not affect everyday problem solving as much.

Critically, we also found a significant Age × Crystallized ability interaction ( b = 0.046, SEb = 0.016, t (201) = 2.943, β = 0.152, p = 0.004, 95% confidence interval [CI] = 0.015, 0.076), indicating the relationship between crystallized ability and everyday problem solving differed across the lifespan. In order to better interpret the significant interaction, simple slopes (displayed in Fig. 5 ) for the relationship between crystallized ability and everyday problem solving were tested for younger age (-1 SD below the mean), middle age (mean), and older age (+1 SD above the mean). Simple slope tests showed that the relationship of crystallized ability to everyday problem solving at a younger age was not significant ( b = 0.708, SEb = 0.433, t (201) = 1.636, β = 0.125, p = 0.103, 95% CI = -0.146, 1.562). However, both the middle age model ( b = 1.576, SEb = 0.292, t (201) = 5.391, β = 0.279, p < 0.001, 95% CI = 0.999, 2.152) and the older age model ( b = 2.44, SEb = 0.397, t (201) = 6.141, β = 0.432, p < 0.001, 95% CI = 1.656, 3.223) revealed a significant positive association between crystallized ability and everyday problem solving. We then tested the difference between regression coefficients across models, and found that the effect of crystallized ability was stronger for both old ( z = -3.027, p = 0.001) and middle age ( z = -1.719, p = 0.043) compared to young, and that the effect was even stronger for the old age compared to middle age ( z = -1.753, p = 0.04), suggesting that crystallized ability played a continuously increasingly important role in solving everyday problems as age increased. Note that the interaction between fluid and crystallized ability was not significant ( p = 0.351), suggesting that the contribution of crystallized ability did not change across people with different fluid ability, after taking age-related effects into account.

Simple slopes of Age × Crystallized ability. Simple slope was not significantly different from 0 at age = 40 (1 SD below mean), but was significant at age = 59 (mean age) and age = 78 (1 SD above mean). Based on a comparison using z -tests, the effect of crystallized ability was stronger at older age ( z = -3.027, p = 0.001) and middle age ( z = -1.719, p = 0.043) than at a younger age, and the effect was even stronger at an older age than middle age ( z = -1.753, p = 0.04). EPT, Everyday Problems Test.

Comparing Cognitive Predictors in Three Age Groups

To further examine which cognitive predictor - fluid or crystallized ability - was more important for everyday problem solving at different stages of the lifespan, we generated bootstrapped standard errors for regression coefficients in 3 age subgroups: younger adults (24-49 years old), middle-aged adults (50-69 years old), and older adults (70-93 years old). In each multiple regression, the predictor variables were age, fluid ability, crystallized ability, fluid 2 , crystallized 2 , and the Fluid × Crystallized interaction. This model was derived from model 4 used for the whole sample with first-order age-related effects removed since this analysis was on each age group. We generated 95% CI using bias-corrected and accelerated (BCa) bootstrap (with 1,000 iterations in each group) as presented in Table 3 . We then compared the BCa CI using a conservative rule by examining the overlap of CI [ 37 ]. Put simply, the rule assesses whether the 95% CI have less than 50% proportion overlap, expressed as a proportion of average margin of error. If the result is affirmative, the 2 estimates are significantly different ( p < 0.05). As shown in Figure 6 , for the young group, the lower end of 95% CI of the crystallized ability parameter was below zero, confirming its nonsignificance and that only the fluid ability value was predictive, as we found in simple slope analysis. For the middle age, the 95% CIs of fluid and crystallized abilities overlapped more than 50%, suggesting that both were predictive but not significantly different in middle-aged adults. Finally, for the older group, the predictive utility of crystallized ability was significantly larger than fluid ability, with the proportion overlap = 42.8%, p < 0.05. Hence, in middle-aged and older adults, everyday problem solving was associated with both fluid and crystallized abilities. Importantly for older adults, crystallized ability was a significantly stronger predictor compared to fluid ability (Fig. 6 ).

Regression coefficient estimates and 95% BCa CI in 3 age groups

95% BCa CI for fluid and crystallized regression coefficients. In older adults, everyday problem solving was predicted more by crystallized ability than fluid ability, proportion overlap = 42.8%, p < 0.05.

We also note that we found no evidence for a Fluid × Crystallized interaction within any age group. The absence of the interaction suggests that fluid and crystallized ability made independent contributions to everyday problem solving, regardless of level of performance on either ability.

In a final analysis, we assessed the stability of the effects of fluid and crystallized ability for each of the 7 problem-solving domains within each age group using the same bootstrapping approach. The main finding was that for older adults, crystallized ability played an important role for all EPT domains except meal preparation , which was marginally significant. In addition, fluid ability was significant for shopping , finance , and meal preparation in older adults (Table 4 ). Table 4 also shows that for young adults, fluid ability was significant for finance , household and transportation , and for finance , medication and transportation in middle-aged adults. Crystallized ability played no significant role for young adults, and significantly predicted only shopping in middle age.

Regression coefficient estimates and 95% BCa CI for 7 EPT domains

The main goal of this study was to understand how fluid and crystallized ability differ across the lifespan in predicting everyday problem solving. We hypothesized that due to diminished fluid resources with age, crystallized knowledge would become increasingly important in predicting everyday problem solving as a function of age. Congruent with this hypothesis, crystallized ability (measured by verbal knowledge in this study) played a more important role in predicting everyday problem solving as age increased. In contrast, fluid ability (measured by speed, working memory, and inductive reasoning) consistently explained variance for all age groups. This pattern of findings suggests that older adults are relying more on crystallized knowledge to solve everyday problems, whereas young adults rely more heavily on the efficiency of basic cognitive mechanisms (e.g., processing speed, working memory, inductive reasoning) that comprise fluid ability.

Past studies have been inconclusive about the relative roles of crystallized versus fluid abilities in everyday problem solving at different ages because none that have examined this issue have included a lifespan sample. The inclusion of the entire adult lifespan was an important feature of the present study, as it allowed us to begin to clarify when in the lifespan crystallized knowledge assumes importance in everyday problem solving. We began to observe a small contribution of crystallized ability to everyday problem solving in middle age, with a large contribution at older ages. The present findings provide clear evidence for the importance of including middle-aged samples in studies.

We also note that the present findings replicate a pattern reported by Hedden et al. [ 26 ] for a very different task - a verbal cued recall task that required participants to memorize associations between paired cues and target words. Hedden et al. [ 26 ] used crystallized and fluid ability to predict performance on the verbal recall task. Just as reported in the present study, they found that crystallized ability (vocabulary scores) explained more variance for older compared to middle-aged and young adults. The similarity of the findings for these 2 very different tasks suggests that increasing reliance on crystallized ability may be a general characteristic of aging. Buttressing this conclusion was the finding that crystallized ability accounted for significant variance in older adults in 6 of the 7 EPT domains, suggesting that the breadth of the effect was reliable across domains. Moreover, the crystallized ability effect was nearly absent in the young and middle-aged adults, with only 1 significant effect for shopping in the middle-aged.

The notion that age differentially affects the type of cognitive ability drawn upon to perform everyday cognitive tasks has not received much attention in the literature. The present findings suggest that crystallized knowledge may help older adults maintain cognitive function in the face of declining fluid ability. Other studies of problem-solving support this interpretation. For example, older adults actually showed better problem-solving abilities than young and middle-aged adults when they were presented with problems associated with social conflict and interpersonal conflict. The solution to these types of problems rely more on wisdom and a broad range of social experiences rather than fluid ability [ 38 ]. Similarly, there is evidence that older adults develop adaptive, experience-based heuristics for solving everyday problems and make decisions that minimize the need to rely on fluid reasoning [ 39 ]. Conversely, there are also domains where crystallized ability makes a scant contribution, even for older adults. We suggest that these would be domains that require extensive on-line processing, such as constantly switching and updating information of different ingredients and procedures when cooking.

It is also important to recognize that everyday problem solving ability is a crucial skill that greatly affects older adults' life quality, but few studies have examined the predictive utility of respondent-based, laboratory problem solving tasks (such as the EPT) in the real world. In support of the use of such laboratory measures, there is a small body of evidence suggesting that the EPT explains substantial variance in every day functioning [ 16,34,40 ]; but much more research is needed. Moreover, the EPT consists of sets of questions that address well-defined, but relatively narrow everyday problems. Real-world problems are typically more complex, are more open-ended (ill-defined), and are comprised of many smaller interrelated problems that require different aspects of knowledge, skills, and abilities. Thus, the EPT may not adequately mirror the complexity of real world problems. Additional investigation of ability predictors of everyday problem solving tasks would help to address this concern.

A limitation of this study is that crystallized ability was measured by vocabulary tasks, which have been traditionally considered as a proxy of knowledge and experience in cognitive psychology studies and everyday problem solving research. However, we acknowledge that a broader assessment of crystallized ability would incorporate experience and other types of world knowledge. Future research with more comprehensive assessment of knowledge and experience beyond measures of vocabulary may help to understand the individual differences in people's utilization of cognition in solving everyday problems. One option might be to assess expertise and familiarity participants have in each problem solving domain in an effort to understand how life experiences asset problem solving. Similar strategies could be adapted to different problem solving paradigms.

We also recognize that it would be ideal to have longitudinal data on both cognitive and everyday problem solving so that the changing relationship between cognitive measures and everyday performance could be assessed as people grow and age. Cross-sectional designs are vulnerable to cohort differences and age × selection confounds. Finally, the compensatory role of crystallized ability may be maximized in high-functioning samples of older adults. Participants in this study were well educated (mean years of education = 16.6); individuals with lower levels of educational attainment may not show the same degree of compensatory benefit (although we found no evidence of Fluid × Crystallized interactions in predicting EPS performance). It would therefore be useful to evaluate these relationships in a more representative sample of the population that included low-education individuals.

In conclusion, the present study suggests that young adults may solve everyday problems based on cognitive resources and mechanisms that are traditionally associated with effective problem solving. However, crystallized knowledge becomes a more predominant influence on everyday problem solving in older adults.

This work was supported by National Institute on Aging at the National Institutes of Health (grant number 5R37AG006265-29 to D.C.P.).

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• Published: 14 January 2021

The developmental trajectories of executive function from adolescence to old age

• Heather J. Ferguson 1   nAff3 ,
• Victoria E. A. Brunsdon 1 &
• Elisabeth E. F. Bradford 2  

Scientific Reports volume  11 , Article number:  1382 ( 2021 ) Cite this article

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• Cognitive ageing
• Learning and memory

Executive functions demonstrate variable developmental and aging profiles, with protracted development into early adulthood and declines in older age. However, relatively few studies have specifically included middle-aged adults in investigations of age-related differences in executive functions. This study explored the age-related differences in executive function from late childhood through to old age, allowing a more informed understanding of executive functions across the lifespan. Three hundred and fifty participants aged 10 to 86 years-old completed a battery of tasks assessing the specific roles of inhibitory control, working memory, cognitive flexibility, and planning. Results highlighted continued improvement in working memory capacity across adolescence and into young adulthood, followed by declines in both working memory and inhibitory control, beginning from as early as 30–40 years old and continuing into older age. Analyses of planning abilities showed continued improvement across adolescence and into young adulthood, followed by a decline in abilities across adulthood, with a small (positive) change in older age. Interestingly, a dissociation was found for cognitive flexibility; switch costs decreased, yet mixing costs increased across the lifespan. The results provide a description of the developmental differences in inhibitory control, working memory, cognitive flexibility and planning, above any effects of IQ or SES, and highlight the importance of including middle-aged adults in studies seeking to establish a more comprehensive picture of age-related differences in executive function.

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Introduction

Executive functions (EF) are high-level cognitive processes that include planning, initiation, shifting, monitoring, and inhibition of behaviours 1 . EFs play an important role in our everyday life, allowing us to focus attention on specific tasks, to engage in successful problem solving, and to plan for the future. EFs demonstrate variable developmental and aging profiles (e.g., 2 , 3 ), with protracted development into early adulthood and a decline into older age that is associated with structural and functional changes in the prefrontal cortex 4 , 5 , 6 , 7 , 8 , 9 , 10 . The majority of these studies have compared dichotomous young/old adult age groups, and few studies include middle-aged adults or adolescents in investigations of age-related changes in EF (c.f. 11 , 12 , 13 who included middle-aged adults). Therefore, many open questions remain about how development changes across the lifespan, and whether these effects are consistent across multiple components of EF. We address this by exploring how different components of EF develop and change across the lifespan, from late childhood through to old age. Specifically, we tested whether four key components of EF (inhibition, working memory, cognitive flexibility and planning) show parallel or distinct developmental trajectories, and aimed to describe any age-related changes in multiple EFs.

EFs begin to emerge early in infancy, with basic skills needed for EFs emerging before three years of age, and more specific skills developing into early childhood 14 . It has been suggested that each component of EF develops at its own rate across childhood and adolescence, reaching maturity at different ages (see 1 ). For instance, cognitive flexibility has been shown to emerge between the ages of 3 and 4 years old, becoming more complex between the ages of 7 and 9 years old, and reaching adult-like levels by age ~ 12 15 , 16 , 17 ; in contrast, Zelazo et al. 18 found that cognitive flexibility abilities continue to improve between the ages of 20 and 29 years old, suggesting prolonged development of these abilities into young adulthood, and highlighting the importance of using different approaches and tasks to assess EF abilities, providing further insight into when these abilities reach maturity. Working memory, inhibition, and planning have been shown to continue to develop throughout childhood and adolescence, and in some circumstances (e.g., task dependent), have also been shown to continue to develop into young adulthood (e.g., 19 , 20 , 21 , 22 , 23 ). The protracted development of EFs across childhood and adolescence is associated with neurological changes, particularly the development of the prefrontal cortex (e.g., 4 , 24 , 25 ). Given this, adolescence is a critical period to study, allowing further examination of the continued development of EFs beyond childhood and into early adulthood to establish when these components of EF reach maturity.

Cognitive performance peaks in young adulthood (e.g., 26 ), with declines emerging as early as 20 or 30 years old, including declines across adulthood in speed of processing 27 , 28 , 29 , 30 , reasoning 29 , 30 , face processing 31 , fluid intelligence 26 , 27 , crystallized intelligence 26 , 27 , working memory 26 , 28 , 32 , 33 , verbal and visuospatial memory 34 , and long-term memory 27 , 28 . There is a vast amount of heterogeneity in regards to when cognitive abilities peak and decline. For example, aspects of short-term memory decline from 18 years of age, working memory declines in the 30 s, and vocabulary peaks in the 40 s or even later 26 . In contrast, other aspects of cognition, such as autobiographical memory and semantic knowledge, remain relatively stable across adulthood 35 , 36 .

These findings raise the question of whether different components of EFs, specifically inhibition, working memory, cognitive flexibility, and planning, are stable across adulthood and decline in older age, or whether age-related declines in EFs begin soon after maturity in early adulthood. Studies have largely established that working memory reaches a peak at 30 years old and declines thereafter 26 , 32 , 33 , 37 . In addition, inhibitory control is poorest in younger children, improves in adulthood, and declines in older age 38 ; however middle-aged adults were omitted from this study, so it is not clear when these declines started to emerge. Overall, there is a paucity of research specifically focussing on multiple components of EF across the lifespan, with studies into aging often limited in their focus due to comparing dichotomous ‘young’ versus ‘old’ adult groups (i.e. few studies include adolescents or middle-age adults in their lifespan sample). This approach means that important evidence is scarce to draw conclusions on the extended developmental trajectory of EF or earlier signs of decline. A notable exception to this is the Cognitive Battery of assessments developed as part of the National Institutes Health Toolbox in the U.S.A (NIHTB-CB; 18 , 39 , 40 ). The NIHTB-CB sought to establish a series of tasks that could be used to assess cognitive function abilities across different populations of individuals, suitable for use in individuals aged from three to 85 years old, and includes measures of inhibitory control, cognitive flexibility, and working memory. Results from the NIHTB-CB support suggestions of an inverted-U-shaped curve in development of a number of EF abilities, including inhibition, cognitive flexibility, and working memory, with abilities first rising across childhood, and falling in later adulthood 18 , 41 , 42 . Ferriera et al. 43 also investigated EF abilities in a specific cohort of healthy middle-aged adults, with results highlighting very early declines in EF before the age of 50; other studies that have included middle-aged adults in a broader adult sample have reported a linear decline across adulthood which is steeper among participants aged 65 + (e.g., 13 ).

Further to these behavioural studies, neuroimaging has revealed changes in both the structure and function of brain regions that underlie EFs in middle-age and older adulthood 44 , 45 , which is highly likely to impact EF performance in these age ranges. The studies cited above have provided important insights into the developmental trajectories of EF capacities across the lifespan, including highlighting the limited studies that have included middle-aged adults in investigations of EFs and, importantly, included analysing age as a continuous measure to track development throughout adulthood (c.f. 11 , 12 , 13 , 46 ). More often, even when studies have included middle-age adults, they have analysed effects of age between groups rather than as a continuous predictor (e.g., 47 , 48 ), or rely on correlation or regression analyses to model only linear trends (e.g., 46 ). As illustrated, studies with middle-aged adults are essential to gain a comprehensive picture of the development of EFs throughout adulthood, to allow pinpointing of when declines in EFs first emerge, and whether the patterns of decline in early adulthood, as shown in other cognitive abilities, are also evident across the different components of EF across different paradigms, or whether they are limited to specific components. Conducting studies with a continuous age sample also provides vital insights to inform theories of healthy and abnormal aging, as, for example, the first pathophysiological changes can commence up to 20 years before a diagnosis of dementia 49 .

Older age is associated with significant declines in EF, including working memory (e.g., 50 ), inhibition (e.g., 51 ), planning (e.g., 52 ), and cognitive flexibility (e.g., 53 ). Additionally, different aspects of cognitive flexibility show distinct age-related effects. Mixing costs are greater in older adults (e.g., 54 , 55 , 56 , 57 , 58 ); however, there are mixed results in regard to switch costs, with some studies reporting an age-related increase (e.g., 59 ), a U-shaped trajectory 53 , or no age-related differences (e.g., 58 , 59 ), most likely due to differences in paradigms. Age-related effects in EFs are thought to be relatively robust, and have been associated with changes in the frontal lobes, specifically age-related volume reduction in the prefrontal cortex 60 . There are some conflicting findings in the literature regarding age-related declines in EF, perhaps because many studies do not account for general slowing in response latencies (see 61 , for a discussion). When accounting for this general slowing, Verhaeghen 61 failed to find evidence for specific age-related declines in inhibition and local task-shifting costs (termed switch costs herein), but found evidence for age-related declines in global task-shifting costs (termed mixing costs herein). Verhaeghen suggested that mixing costs reflect a dual-task cost, with dual-tasks affecting older adults more 62 . Thus, it is important for studies examining effects of cognitive decline in older age to account for age-related changes in response speed, to be sure that effects reflect true changes in executive capacities rather than more general slowing in response latencies.

In addition to age, several factors have been linked to cognitive decline, including genetics, health status, physical activity, socio-economic status (SES), IQ, and physical fitness (e.g., 63 , 64 , 65 , 66 , 67 , 68 ). Childhood SES has been consistently associated with EF 56 , 69 , 70 , 71 , with lower SES predicting poorer performance on tasks of EF in childhood 72 . Less is known about the link between adult SES and EF 73 . IQ is another factor that has been associated with EF, particularly with working memory 74 . IQ and EFs are dissociable yet related in childhood 75 , with evidence that inhibitory control and cognitive flexibility are related to IQ during childhood 76 . In adolescence, working memory is highly correlated with IQ, but inhibition and cognitive flexibility are not 54 . In older adults, IQ has been shown to be related to working memory, verbal fluency, inhibition, and cognitive flexibility 77 . Given that IQ and SES are related to EF abilities, the current study controlled for these factors in analysis, allowing us to assess the role of age in predicting differences in EFs, beyond effects of IQ and SES.

EFs play a critical role in everyday life, allowing individuals to plan ahead, focus their attention, and switch between different tasks. They play a key role in allowing individuals to maintain effective levels of independent functioning, and better EF abilities have been associated with improved self-reported quality of life in older age 1 , 78 . Further, deficits in EF abilities have been associated with issues with obesity 79 , social problems 80 , 81 , and lower levels of productivity 82 . It is therefore important to further our understanding of how these EF abilities continue to change and differ across the lifespan—contributing to our understanding of age-related cognitive changes—which ultimately may be able to provide insight into the optimum age at which cognitive training interventions could be utilized to help maintain real-world functioning across individuals.

The current study investigated how multiple components of EF differ across the lifespan, in a large, community-based sample of 350 10- to 86-year-olds, allowing differences across adolescence, early adulthood, middle adulthood, and older adulthood to be examined within one study. The study focussed on four components of EF: planning, inhibition (also termed inhibitory control and response inhibition), working memory, and cognitive flexibility (also called set shifting or mental flexibility). It is largely accepted that inhibitory control, working memory, and cognitive flexibility form the core components of EF abilities, reflecting largely (but not entirely) separable processes 83 . In the current study we also included a more complex aspect of EF, planning abilities. The ability to plan is a complex executive skill 94 , 102 that plays an important role in daily living, such as the ability to identify a goal and subsequently planning and executing the steps needed to attain that goal 72 , 83 . It is noted that planning abilities themselves, whilst considered an aspect of EF, may require activation of other EFs, including inhibitory control and working memory in order to produce successful outcomes 72 , 83 . Given this, the inclusion of a measure of planning abilities in the current study allowed further insight into how planning capacities may change across the lifespan, and whether we are able to establish a relationship between ‘core’ EF abilities and planning capacities within this lifespan sample.

The aim of this study was to explore the developmental trajectories of these four components of EF, to identify when age-related differences emerge. A cross-sectional design was utilized, to provide insight into differences that can be established across different age cohorts in task performance; importantly, to address our research question, we selected tasks that were appropriate for all participants from 10 to 86 years of age, allowing direct comparisons in task performance to be made across different ages. We used curvilinear regression modelling to establish the shape and trajectory of change across ages for each EF. Due to research suggesting that some components of EF may be related to IQ and SES, we also controlled for the effects of IQ and socio-economic status.

We predicted, firstly, that these components of EF would continue to develop throughout adolescence, indicated by an improvement in performance across tasks up to ~ 30 years of age. Second, we predicted that there would be age-related declines in EF from ~ 50 years of age onwards 43 . Third, we explored whether this decline in EFs would start earlier in adulthood (i.e. between 30 and 50 years of age). We did not stipulate specific predictions in this middle age range due to the dearth of research in adulthood. Instead, we modelled and tested the fit of linear, quadratic and cubic age relationships for each component of EF. Note that each statistical model can represent multiple patterns/directions of effects, however we define our predictions for the linear, quadratic and cubic fit models used here based on existing research on cognitive development and decline with age. We posited that a predicted linear age relationship would indicate either an improvement or decline in EF from adolescence to older age. We predicted that a quadratic age relationship would indicate a developmental improvement in EF in adolescence through to young adulthood, and a decline in EF throughout adulthood. A predicted cubic age relationship would indicate a developmental improvement in EF in adolescence through to young adulthood, a decline in EF across adulthood, and a further steeper decline in EF in older age. Finally, in line with previous research (e.g., 84 ) we predicted that the different aspects of cognitive flexibility would should show distinct effects: we predicted that switching costs (i.e., changing task sets) would not show any age-related changes, but mixing costs (i.e., maintaining multiple task sets) would show an increase across adulthood (e.g., 84 , 85 ).

Materials and method

Participants.

The sample consisted of 354 participants who were recruited from the community, via newspaper/radio adverts, social media, and an institutional research participation database, as part of the CogSoCoAGE project. Two participants were excluded due to low IQ (< 70), one participant was excluded due to being a non-native English speaker, and one participant’s data was lost due to computer failure. The final sample consisted of 350 participants (10–86 years-old; 232 females, 118 males). Table 1 provides a summary of the sample and Table 2 details the demographic characteristics of the CogSoCoAGE sample, each divided into five age groups for illustrative purposes. All participants were native English-speakers, had normal or corrected-to-normal vision, had no known neurological disorders, and had no mental health or autism spectrum disorder diagnoses. The Ethical Committee of the School of Psychology, University of Kent, approved the study, and all methods were carried out in accordance with EU guidelines and regulations. Informed consent was obtained from all participants; for participants under 18 years of age, consent was additionally sought from a parent or legal guardian.

Socio-economic status

Participants (if aged over 18) and parents of participants (if aged under 18) reported on their level of education, the household income, and their occupation (job title and industry). Occupational class was coded using the derivation tables provided by the Office for National Statistics 116 using the simplified National Statistics Socio-Economic Classification (NS-SEC) based on Standard Occupational Classification 2010 (SOC2010). To calculate an SES index, education level was coded on a scale 1–6, and household income and occupational class were coded on a scale 1–7. These three scores were summed to derive an SES index between 3 and 20 86 , with lower scores indicating lower SES. In our sample, scores ranged from 5 to 20.

Intellectual ability was assessed using the Wechsler Abbreviated Scale of Intelligence-Second Edition (WASI; 87 ). The WASI-II comprises of four subtests as a measure of intelligence for individuals aged 6–90 years old. The Vocabulary and Similarities subtests estimated a verbal IQ score. The Block Design and Matrix Reasoning subtests estimated a performance IQ score. Full-scale IQ comprised of both verbal and performance IQ.

Stroop colour-word task

A modified version of a standard Stroop Colour-Word task 88 was used as a measure of inhibition. The words were printed in red, green, blue, or yellow for all trials and were printed on a grey background. The words used in both congruent and incongruent trials were “RED”, “GREEN”, “BLUE”, and “YELLOW”. For congruent trials, the colour word matched the printed colour (i.e., “RED” printed in red). For incongruent trials, the colour word did not match the printed colour (i.e., “RED” printed in green). For filler trials, the non-colour words were matched for length and frequency to the colour words. The filler words used were “TAX”, “CHIEF”, “MEET”, and “PLENTY”. The word stimuli were presented in the middle of the screen in font type Courier New and font size 28. See Fig.  1 for example stimuli.

Illustrations of the stimuli and procedure employed in each of the four EF tasks: ( A ) Stroop colour-word task; ( B ) operation span; ( C ) task switching; ( D ) Tower of Hanoi.

Participants first completed 20 practice trials, which consisted of ten filler and ten congruent trials in a pseudo-randomised order. Participants were told that they would see a word and they were instructed to identify the colour of the word as fast as possible using a button-box (i.e., RED printed in green; participants press ‘green’ button). The experimental trials consisted of 50 congruent trials, 50 incongruent trials, and 50 filler trials presented in a pseudo-randomised order, in which the same colour word, the same printed colour, or the same colour word/printed colour could not appear on two consecutive trials to avoid priming effects. A blank screen appeared for 1000 ms at the start of the experimental trials. After the participant made a response, the next trial was started immediately.

Response times for filler, congruent and incongruent trials were calculated for accurate responses that were made 200 ms after stimuli onset and were within 2.5 SDs of each participant’s overall trial mean. The dependent variable was the Stroop congruency effect (incongruent trial mean RT minus congruent trial mean RT). In addition, we accounted for age-related slowing and declines in information processing speed, which led to positive skew and high kurtosis in reaction times, by log-transforming reaction times for each trial before calculating the Stroop congruency effect. The log-transformation of the Stroop congruency effect reduced skew and kurtosis (untransformed skew = 1.84, kurtosis = 8.84; log-transformed skew = 0.69, kurtosis = 3.44). The log-transformed Stroop congruency effects were reverse scored so that a higher score indicated better performance to aid interpretation of results alongside other measures. Internal consistency was excellent (Cronbach’s alpha = 0.99) and the average inter-item correlation was ideal (r = 0.53).

Operation span

This task was adapted from Unsworth et al.’s 89 automated operation span task (OSpan) as a measure of working memory, which was based on the original OSpan task by Turner and Engle 90 . Participants were required to solve maths equations while remembering a sequence of letters. The letters used were F, H, J, K, L, N, P, Q, R, S, T, and Y. See Fig.  1 for example stimuli.

There were three practice blocks. The first practice block was a simple letter span. A single letter appeared in the middle of the screen for 800 ms. A two-letter span was used for two trials, and a three-letter span was used for a further two trials. At recall, participants were required to recall the letter sequence in the correct order by clicking a box next to the appropriate letter presented in a 4 × 3 matrix. After clicking a box, a number appeared that represented the position of the letter in the sequence. A ‘blank’ box was also presented and participants were told to click this box if they could not remember the letter in the sequence. Participants could also click a ‘clear’ box to clear responses. The letters clicked also appeared at the bottom of the screen. To finish the letter recall stage, participants clicked a box labelled ‘enter’. This recall phase was untimed. After the recall phase, participants were given feedback about how many letters they recalled correctly.

The second practice block introduced the maths equations. A maths equation was presented on screen (e.g., (2 × 1) + 1 = 3) along with a ‘correct’ box and an ‘incorrect’ box. Participants were required to identify whether the maths equation was correct or incorrect by clicking the appropriate box. Accuracy feedback was given. There were three trials in this second practice block.

In the last practice block, participants completed both the maths section and letter recall section together. The maths equation was presented first, and once participants had responded to the problem, a letter to be recalled appeared in the middle of the screen for 800 ms. This equation-letter sequence was repeated twice to create a two-letter span in this final practice block. The letter recall screen with the 4 × 3 letter matrix was then presented. Participants completed three full practice trials, and were given feedback on how many letters they recalled correctly and how many errors they made on the maths problems.

The experimental trials consisted of three trials for each of 2 to 7 letter spans (randomised). This made a total of 18 trials with 81 maths problems and 81 letters. Participants were encouraged to keep their maths accuracy at or above 85% at all times. During recall, a percentage in red was presented in the upper right-hand corner of the screen, indicating the percentage accuracy for the maths problems.

An absolute OSpan score was calculated as the sum of all perfectly recalled sets. A partial OSpan score was also calculated as the total number of letters recalled in the correct position. The absolute and partial OSpan scores were highly correlated (r = 0.92, p  < 0.001) and due to the recommendations of Unsworth et al. 89 , the partial OSpan score was used as the dependent variable. Internal consistency was good (Cronbach’s alpha = 0.85) and the average inter-item correlation was ideal (r = 0.25).

Task switching

The task was adapted 91 , 92 as a measure of cognitive flexibility. Participants were presented with a 2 × 2 matrix on a computer screen. Stimuli were presented one-by-one in the four quadrants of the screen, beginning in the upper-left quadrant and rotating in a clockwise manner. The stimuli were coloured-shapes (circle/triangle, in blue/yellow) that appeared in the quadrant. See Fig.  1 for example stimuli. The same shape/colour combination did not appear on consecutive trials (i.e., a blue triangle could not appear in consecutive trials). Participants’ task was to decide whether the shape was a circle or a triangle, and whether the colour was blue or yellow, dependent on trial-type (see descriptions below). Participants used a button box to respond, pressing the left-hand button for circle/blue and the right-hand button for triangle/yellow. Participants were instructed to respond as fast and as accurately as possible. The next stimulus was presented 150 ms after a key press or after a timeout of 5000 ms. Participants received feedback about their accuracy after practice trials and repeated the practice block if their accuracy was less than 80%.

In the single-task, there were 16 practice trials and 32 experimental trials per block. Participants had to identify whether the shape was a circle or a triangle in one block, and whether the colour was blue or yellow in a second block (single-task trials).

In the mixed-task, there were 16 practice trials, and four blocks of 32 experimental trials. Participants had to indicate whether the shape was a circle or a triangle when the coloured-shape appeared in the top two quadrants, and whether the colour was blue or yellow if the coloured-shape appeared in the bottom two quadrants. Categorising the coloured-shape in the upper left to upper right quadrant, or in the lower right to lower left quadrant did not require switching to a new category (i.e., non-switch trials). However, categorising the coloured-shape in the upper right to lower right quadrant, or in the lower left to upper left quadrant required switching to a new category (from shape to colours, and vice versa, i.e., switch trials). Switch and non-switch trials alternated predictably within these blocks.

Response times were calculated for accurate responses that were made 200 ms after stimuli onset, and were within 2.5 SDs of each participant’s overall trial mean. A switch cost of task-set switching was calculated by subtracting the mean response time for non-switch trials from the mean response time for switch trials in the mixed-task. A mixing cost (indicating maintenance of two task-sets) was calculated by subtracting the mean single-task trial response time from the mean non-switch response time in the mixed-task. To account for age-related slowing and declines in information processing speed, trial level response times were log-transformed before calculating a switch cost and mixing cost. The log-transformation reduced skew and kurtosis for switch cost (untransformed skew = 0.47, kurtosis = 3.25; log-transformed skew = 0.17, kurtosis = 2.54) and mixing cost (untransformed skew = 0.91, kurtosis = 3.39; log-transformed skew = 0.41, kurtosis = 2.83). The log-transformed switch and mixing costs were reverse scored so that a higher score indicated better performance. Internal consistency was excellent for both the single and mixed-task (both Cronbach’s alpha = 0.98). The average inter-item correlation for the single-task (r = 0.49) and for the mixed-task (r = 0.34) was ideal.

Tower of Hanoi

The Tower of Hanoi was used as a measure of planning (based on script obtained from: https://step.talkbank.org/scripts-plus/TOHx.zip ). The Tower of Hanoi required the mouse-controlled movement of different-sized disks across three pegs from an initial state to a target state in a pre-defined number of steps. Participants were presented with three pegs (left, centre, right) and four disks; pink, yellow, blue and green, in increasing size. The target state was shown on the top-centre of the screen and was smaller than the initial state configuration. The initial state was presented on the bottom-centre of the screen. The number of steps remaining was shown in the centre of the screen. Participants were told that they needed to move the disks from their current positions on the bottom of the screen to match the target state in the given number of steps without placing larger disks on top of smaller disks. See Fig.  1 for example stimuli.

Participants first completed three practice trials: one one-step and two two-step problems. Participants continued to 16 experimental trials, which took three- to ten-steps to complete, with two trials at each step. Before the start of each trial, participants were told how many steps were required to complete each trial. During the trials, participants clicked on the disk that they wanted to move and this disk then turned red. The participant then clicked on the rod that they wanted to move the disk to. If the incorrect rod was selected, then an error message was shown and the participant restarted that trial. If the participant made five incorrect movements in a row then the task automatically ended. If the correct disk and rod were selected, then the selected disk moved to the selected rod and the participant moved on to the next step.

The dependent variable was an overall Tower of Hanoi score that used the traditional absolute scoring method, and was the sum of all perfectly completed trials (i.e., score of 5 for a trial with 5 steps completed perfectly with no errors). Internal consistency was acceptable (Cronbach’s alpha = 0.80) and the average inter-item correlation was ideal (r = 0.20).

Participants attended one or two visits to the university to complete the 5 h testing session, which included questionnaires on behaviour and demographic information, computer-based testing to assess cognitive and social skills, and an IQ assessment. The order of tasks was counterbalanced over 12 different lists to ensure that order effects were minimised. All tasks reported here were programmed using E-Prime software.

Analyses were conducted in R version 3.6.0. The datasets and code are available on the Open Science Framework ( https://osf.io/qzrwu ). Descriptive data on the EF measures are summarised in Table 3 , alongside the total number of participants retained per task. For the Stroop task, two participants did not complete the task due to equipment failure and one participant was colour-blind. For the Operation Span, one participant did not complete the task due to equipment failure, 3 participants did not return for their second testing session to complete the task, and 12 participants declined to complete the task or withdrew. For Task Switching, two participants did not complete the task due to equipment failure, 3 participants did not return for their second testing session to complete the task, 10 participants declined to complete the task or withdrew, and two participants’ data was lost due to computer error. For the Tower of Hanoi, two participants did not return for their second testing session to complete the task.

Age-related effects on executive function

A series of regression models were conducted to investigate the relationship between the measures of EF and age, over and above any potential effects of IQ and SES. The models specified the outcome variable as the dependent measure for the specific EF measure, the first predictor variable was age using linear, quadratic or cubic orthogonal polynomial coefficients, and IQ and SES index were included as the second and third predictor variables. Note that quadratic models included both linear and quadratic age coefficients, and cubic models included linear, quadratic and cubic age coefficients.

The best fitting model for each EF measure was deduced by comparing several goodness-of-fit indices shown in Table 4 . Established goodness-of-fit measures were used to evaluate model fit. The ANOVA test and likelihood test contrasted the simpler model against the more complex model (e.g., the model with linear vs. quadratic age coefficients). If the p value was greater than 0.05, then the simpler model was selected as the best fitting model. If the p value was less than 0.05, then the more complex model was selected as the best fitting model. Model comparison also used Akaike’s Information Criterion (AIC) and Bayesian Information Criteria (BIC), with increasingly negative values corresponding to increasingly better fitting models. Model selection evaluated these goodness-of-fit indices and the model (linear, quadratic, or cubic model) with the greatest number of goodness-of-fit indices was selected as the overall best fitting model (see Table 4 ). The model predictions for the overall best fitting models for each EF are plotted in Fig.  2 with the observed data. Analyses for the untransformed variables are reported in Supplementary Materials ( S1 , S2 ).

Relationship between age and executive function measures, adjusted for IQ and SES index. ( A ) log-transformed Stroop congruency effect, ( B ) OSpan partial score, ( C ) Tower of Hanoi score, ( D ) log-transformed Task Switching switch cost; and ( E ) log-transformed Task Switching mixing cost. The bold line indicates the best-fitting regression line and the dashed line indicates the 95% confidence intervals (CIs). Stroop congruency effect and Task Switching switch and mixing costs are reversed scored so that a higher value indicates better performance and all variables are z-scored for ease of comparison. Note : All measures were adjusted for IQ and SES to be comparable to the described regression models. To adjust for IQ and SES, the residuals were obtained from the regression line fit when fitting each executive function measure as a dependent variable in a linear model and IQ and SES index as predictor variables.

The best fitting model for the log-transformed congruency effect in the Stroop task included linear and quadratic age coefficients. The results of the model indicated that there was a significant association between the Stroop congruency effect and age, IQ, and SES (R 2  = 0.14, F (4, 335) = 13.46, p  < 0.001). Age was significantly associated with the Stroop congruency effect (linear β  = − 0.27, p  < 0.001; quadratic β  = − 0.28, p  < 0.001). To interpret the curvilinear relationship between the Stroop congruency effect and age, we consider the model predictions displayed in Fig.  2 A. Figure  2 A indicates that there is some increase in the Stroop congruency effect between 10 and 35 years of age (i.e., an improvement in inhibitory control) and a decrease in the Stroop congruency effect from 36 to 86 years of age (i.e., a decline in inhibitory control). IQ was also significantly associated with the Stroop congruency effect ( β  = 0.20, p  < 0.001), but SES was not ( β  = 0.09, p  = 0.106). The model remained significant when IQ and SES covariates were removed (quadratic R 2  = 0.09, F (2, 344) = 17.27, p  < 0.001), showing that age was significantly associated with the Stroop congruency effect in our sample (linear β  = − 0.19, p  < 0.001; quadratic β  = − 0.27, p  < 0.001).

The best fitting model for the OSpan partial score included linear and quadratic age coefficients. The results of the model indicated that there was a significant association between the OSpan partial score and age, IQ, and SES (R 2  = 0.28, F (4, 322) = 32.59, p  < 0.001). Age was significantly associated with the OSpan partial score (linear β  = − 0.48, p  < 0.001; quadratic β  = − 0.30, p  < 0.001). To interpret the curvilinear relationship between the OSpan partial score and age, we consider the model predictions displayed in Fig.  2 B. Figure  2 B indicates that there is some increase in the OSpan scores from 10 to 30 years of age (i.e., an improvement in working memory capacity), and a decrease from 30 onwards (i.e., a decline in working memory capacity). IQ was also significantly associated with the OSpan partial score ( β  = 0.44, p  < 0.001), but SES was not ( β  = 0.004, p  = 0.930). The model remained significant when IQ and SES covariates were removed (quadratic R 2  = 0.11, F (2, 331) = 20.78, p  < 0.001), showing that age was significantly associated with the OSpan partial score in our sample (linear β  = − 0.27, p  < 0.001; quadratic β  = − 0.22, p  < 0.001).

The best fitting model for the Task Switching switch cost included the linear age coefficient. The results of the model indicated that there was a significant association between the Task Switching switch cost and age, IQ, and SES (R 2  = 0.05, F (3, 323) = 5.19, p  = 0.002). Age was significantly associated with the log-transformed switch cost (linear β  = 0.20, p  < 0.001), indicating a decrease in switch cost from 10 to 86 years old (i.e., an improvement in cognitive flexibility in terms of ‘switch cost’; Fig.  2 D). IQ and SES were not significantly associated with switch cost (both p s > 0.134). The model remained significant when IQ and SES covariates were removed (linear R 2  = 0.04, F (1, 331) = 14.81, p  < 0.001), showing that age was significantly associated with the Task Switching switch cost in our sample (linear β  = 0.21, p  < 0.001).

The best fitting model for the Task Switching mixing cost included the linear age coefficient. The results of the model indicated that there was a significant association between the Task Switching mixing cost and age, IQ, and SES (R 2  = 0.07, F (3, 323) = 8.24, p  < 0.001). Age was significantly associated with the log-transformed switch cost (linear β  = − 0.26, p  < 0.001), indicating an increase in mixing cost from 10 to 86 years old (i.e., a decline in cognitive flexibility in terms of ‘mixing cost’; Fig.  2 E). IQ and SES were not significantly associated with switch cost (both p s > 0.103). The model remained significant when IQ and SES covariates were removed (linear R 2  = 0.07, F (1, 331) = 23.86, p  < 0.001), showing that age was significantly associated with the Task Switching mixing cost in our sample (linear ( β  = − 0.26, p  < 0.001).

The best fitting model for the Tower of Hanoi absolute score included linear, quadratic, and cubic age coefficients. The results of the model indicated that there was a significant association between the Tower of Hanoi absolute score and age, IQ, and SES (R 2  = 0.20, F (5, 336) = 17.00, p  < 0.001). Age was significantly associated with Tower of Hanoi absolute score (linear β  = − 0.15, p  = 0.004; quadratic β  = − 0.25, p  < 0.001; cubic β  = 0.21, p  < 0.001). To interpret the curvilinear relationship between the Tower of Hanoi absolute score and age, we consider the model predictions displayed in Fig.  2 C. Figure  2 C indicates that there is an initial increase in Tower of Hanoi absolute scores from 10 to 30 years of age (i.e., an increase in planning ability), a decrease from 30 to 70 years of age (i.e., a decrease in planning ability), and a small, but variable, increase from 70 years of age onwards. IQ was also significantly associated with the Tower of Hanoi absolute score ( β  = 0.43, p  < 0.001), but SES was not ( β  = 0.005, p  = 0.921). The model remained significant when IQ and SES covariates were removed (cubic R 2  = 0.05, F (3, 344) = 6.65, p  < 0.001), showing that age was significantly associated with the Tower of Hanoi absolute score in our sample (linear β  = − 0.41, p  = 0.002; quadratic β  = − 0.25, p  < 0.001; cubic β  = 0.26, p  < 0.001).

Relationships between measures of executive functions

A series of Pearson’s correlations were conducted between the four EF tasks to investigate the relationship between the measures of EF (Table 5 ). Partial correlations were also conducted to control for the effects of age. These effects of age for each EF measure were determined from the previously described regression models, i.e., the EF measures were adjusted for the linear, quadratic or cubic age effects. To adjust for age, the residuals were obtained from the regression line fit when fitting each EF measure as a dependent variable in a linear model and age coefficients (linear, quadratic, or cubic age coefficients) as predictor variables.

The OSpan partial score showed a positive correlation with both the Stroop congruency effect and the Tower of Hanoi score, with only a relationship with the Tower of Hanoi score remaining once accounting for the effects of age. These findings suggest that individuals with a higher working memory capacity also possess better planning ability, and these relationships are present irrespective of any age effects. Finally, Task Switching switch and mixing costs showed a negative correlation, reflecting that individuals with a greater switch cost also had a smaller mixing cost, and vice versa, and this pattern remained when accounting for the effect of age.

Comparing developmental trajectories of executive function

To examine whether each component of EF followed comparable or distinct developmental trajectories, we conducted across model comparisons for the age-related effects in the different EFs. This statistical method allows us to compare EF regression models with the same number of predictor variables, allowing direct comparisons between trajectories across these tasks. In our data, Task Switching switch and mixing cost models have three predictors (i.e., linear age coefficient, plus IQ and SES), Stroop congruency effect and OSpan partial score models have four predictors (i.e., linear and quadratic age coefficients, plus IQ and SES), and the Tower of Hanoi absolute score model has five predictors (i.e., linear, quadratic, and cubic age coefficients, plus IQ and SES). Therefore, Task Switching switch cost and mixing cost and Tower of Hanoi absolute score revealed different age-related effects (i.e., linear-only age effects vs. cubic age effect) and so were not directly compared with any other component of EF; analysis focused on the Stroop congruency effect versus OSpan partial scores.

Stroop congruency effect and OSpan partial score revealed similar curvature in the previous regression models (i.e., a quadratic effect of age), and so were directly compared. Two regression models were conducted and compared to statistically assess whether the age-related effects in the Stroop task and OSpan were significantly different. In the first step, a model was conducted that specified the outcome variable as the z-scores for the Stroop congruency effect and the OSpan partial score, with the predictor variable as the linear and quadratic age coefficients. In the second step, the same model was specified with the addition of an interaction term that included a grouping variable (i.e., a dummy variable) for the Stroop congruency effect (coded as 1) and the OSpan partial score (coded as 2). In the final step, these two models were compared using an ANOVA. If the p value was less than 0.05, then the regression slopes for the relationship between Stroop congruency effect and age versus OSpan partial score and age could be considered significantly different. If the p value was more than 0.05, then the regression slopes could be considered not statistically different.

The results indicated that the regression slopes for the Stroop congruency effect and OSpan partial score were not significantly different (RSSΔ = 2.96, F Δ = 1.11, p  = 0.344), suggesting that inhibitory control and working memory show similar developmental trajectories. As illustrated in Fig.  2 , the regression slopes for the other components of EF follow different patterns over age, indicating that only inhibitory control and working memory have similar developmental trajectories and all other components of EF show distinct developmental trajectories.

The current study explored age-related differences in EF from late childhood through to old age in a large, community-based sample. Three-hundred and fifty individuals aged 10 to 86-years-old completed tasks to measure inhibitory control, working memory, cognitive flexibility, and planning, to identify when age-related changes in these EFs first become apparent. After controlling for any potential effects of IQ and SES, analyses revealed that inhibitory control and working memory capacity was higher in young adulthood compared to adolescence, with inhibitory control showing a decline in participants from ~ 35-years-old, and working memory capacity showing a decline in participants from ~ 30-years-old. Planning ability was also higher in young adulthood compared to adolescence, but then declined across adulthood, with a small positive change in older age. In line with our hypothesis, a dissociation was found for the measures of cognitive flexibility: interestingly, however, this reflected that switch costs decreased across the lifespan, yet mixing costs increased across the lifespan.

These findings provide insight into the developmental trajectories of inhibitory control, working memory, cognitive flexibility, and planning ability across the lifespan, providing a more comprehensive picture of the age-related changes in EF than has previously been established. Many of the existing studies that have examined aging and EFs have compared a dichotomous sample of younger versus older adults (e.g., 51 , 93 , 94 , 95 ), have combined individuals into smaller age groups during analysis (e.g., 53 , 55 ), or have focused on single aspects of EF, such as inhibitory control (e.g., 19 , 23 ). Instead, in the current study, we used a continuous age sample to model curvilinear age relationships to show the development of EFs from adolescence through to older adulthood, and to highlight changes in EFs that emerge throughout adulthood and not specifically at the onset of old age (typically considered 65 years old plus). Studies have largely overlooked adulthood as a period of change, with many studies omitting middle-aged adults in their samples examining lifespan changes. Moreover, cognitive performance among adolescents has rarely been compared to middle- or older-aged adults. The current study therefore makes a unique contribution to the literature by demonstrating developmental changes in different EFs, using the same set of tasks for all participants, with evidence that declines emerge in inhibitory control, working memory, and planning as early as the third decade of life. In addition, inhibitory control and working memory follow comparable developmental trajectories, with distinct developmental trajectories apparent for the other measures of EF.

In line with our predictions, and supporting previous studies 61 , the current study highlighted that different aspects of cognitive flexibility showed distinct age effects. As expected, there was an increase in mixing costs across adulthood, but switch costs decreased across adulthood. Mixing costs have generally been found to be greater in older adults (e.g., 54 , 55 , 56 , 57 , 58 ) there are mixed results in regard to switch costs, with some studies reporting an age-related increase (e.g., 59 ), a U-shaped trajectory 53 or no age-related differences (e.g., 58 , 59 ), most likely due to differences in the task switching paradigms. We note that the current study used an alternating-runs paradigm without a preparatory cue-stimulus interval, which is analogous to Huff et al.’s 84 task-switching paradigm with comparable aging results. In addition, switch and mixing costs showed a negative correlation, reflecting that individuals with a greater switch cost also had a reduced mixing cost, and vice versa, and this pattern also remained when accounting for the effect of age. This finding replicates that seen in Huff et al. 84 in which a dissociation was found between switch and mixing costs across age groups. Huff et al. 84 suggested that this dissociation is due to differences in the attentional systems in younger versus older adults. They suggest that younger and middle-aged adults experience a larger switch cost as their attentional systems become tuned to the task set in the single-task, and this inertia to executing the same rule in the single-task slows the reconfiguration to respond to the switch trials in the mixed-task. Older adults experience a reduced switch cost as their attentional systems are less well tuned to the task set in the single-task, and so do not experience the same slow down to respond to switch trials in the mixed-task. Moreover, older adults experience a larger mixing cost due to the additional attentional demands of maintaining two task sets in the mixed-task as compared to a single task set in the single-task. In summary, results of the task switching paradigm demonstrate dissociations between switch and mixing costs across the lifespan, indicating that adolescents and younger adults have more difficulty switching between task sets, and middle-aged and older adults have more difficulty maintaining task sets.

In the current study, we utilized four widely used tasks to measure inhibitory control, working memory, cognitive flexibility, and planning as components of EF. We investigated the relationship between these measures and found that individuals with a higher working memory capacity also had better planning ability, and these relationships remained when accounting for the effects of age. This finding suggests a link between working memory capacity and planning ability, or alternatively it could suggest that some EF tasks purported to measure singular aspects of EF may also require other EF processes to complete. This is supported by prior literature which has suggested that ‘planning’ may be indicative of a more complex executive skill, requiring activation of other aspects of EF to be successful (e.g., 26 , 96 , 97 ). For instance, working memory may be required when utilizing planning abilities to allow thinking ahead and execution of steps to achieve a set goal 26 , 97 ; Hill and Bird 98 also suggest that the traditional tower tasks (as used here to assess planning abilities) may require working memory, the inhibition of prepotent responses, and the generation of problem-solving ideas.

Interestingly, there were no other relationships between the measures of EF. Other research has documented very weak relationships between EF tests and EF factors, leading to the conclusion that these are dissociable components of EF and providing support for the fractionated EF theory (e.g., 83 , 97 , 99 ). Studies that do report relationships between components of EF tend to use several different EF tasks to assess each component and use an SEM approach to fit and compare models. For instance, Miyake et al. 83 report in their study that, following completion of nine tasks used to assess shifting, updating, and inhibition, a three-factor model fitted the data best, highlighting distinguishable factors of: cognitive flexibility (shifting), updating, and inhibition. In the current study, we did not aim to assess whether EF is a unitary or diverging construct, and as such data is not optimised to investigate this specifically. However, the lack of correlations between tasks in the current data suggest that the tasks are tapping into distinguishable capacities rather than ‘umbrella’ EFs. Furthermore, EFs differentiate from middle to late childhood 100 . Our study is unique in exploring four separate measures of EFs (as opposed to an aggregated measure of cognitive performance), allowing across model comparisons which revealed that inhibitory control and working memory follow similar developmental trajectories, and all other measures of EF show distinct developmental trajectories.

In general, there is no single task or task battery that can exhaustively measure all aspects of EF, and tests of individual EF are rarely “process pure” 97 , 101 . Furthermore, there is some debate about whether tasks measure the underlying concept that they are purported to measure. For example, it has been suggested that participants may solve the Tower of Hanoi problems in a step-by-step manner instead of in a multi-step, planful manner 102 . It is also likely that the specific processes involved in each task differ across individuals and cohorts. For example, the method of administration used in the OSpan task here (i.e. requiring participants to select their answer from letters in a 4 × 3 grid) is likely to have differentially affected performance among the older participants since they are less familiar with computers and are known to experience age-related difficulties in visual search tasks and motor control (e.g., 103 , 104 ). In addition, it is noted that we used a single task to measure each component of EF. There may be specific aspects of each EF that may follow different developmental trajectories—for example, inhibitory control could be divided into automatic and effortful inhibition 105 . However, the aim of the current study was to examine how four separable EFs (inhibitory control, working memory, cognitive flexibility, and planning) may continue to change and differ across the lifespan, to further our understanding of age-related cognitive changes that may be present; to do this, we selected four well-established tasks that were suitable for use across the participant sample age-range, 10–86 years. This allowed direct comparison of task performance across different participant ages. It is noted, as previously recommended 106 , that in future studies it would be beneficial to include multiple measures of each component of EF to elucidate whether these age-related changes reflect the underlying EF or whether the age-related effects are task- or paradigm-specific. Furthermore, dual-tasks of EF may reveal greater age-related declines as multiple EFs are loaded in a single task; for example, loading working memory in younger adults has been found to reduce both inhibitory control and switching ability 107 . Tasks need to be sensitive enough to detect age-related declines 108 and should account for general cognitive processing 61 . The four EF tasks in the current study were found to be age-sensitive after adjusting for general cognitive declines in response latencies and for IQ and SES, and therefore suitably provide an overall lifespan description of EF. We note that our analyses did not factor in the influence of gender on EFs, though gender was unequally distributed across the age groups in our sample (e.g., 47% females among adolescents but 82% females among adults). Previous research has provided mixed evidence for gender or sex differences in executive functioning (e.g., 109 ), and these analyses were beyond the scope of the current paper, however it would be beneficial for future studies to systematically explore this influence further. Gender details in our sample are available alongside task data on the OSF repository.

Here, we describe the overall developmental trajectories of EFs. To increase confidence in findings relating to this main aim, we controlled for any effects of IQ and SES when exploring age effects on EFs, due to evidence suggesting that some components of EF may be related to IQ and SES 54 , 56 , 67 . For IQ measures, our results highlighted a relationship with inhibitory control, working memory, and planning ability, above the effects of age. This may also explain why, in our measure of planning ability, a small, variable, improvement in abilities is seen from 70 years old onwards. Notably, the older age participants who took part in this study had higher IQ scores (full scale, verbal, and performance) than any other age group included in analysis; participants in this study were community-based, and this higher IQ may reflect that those experiencing the optimum ‘healthy’ aging experience are more likely to agree to take part in research studies such as these. This provides insight into healthy aging processes and may indicate that IQ holds a protective role against age-related declines in EF, although further research aimed at directly examining this suggestion, particularly its role in predicting planning abilities, is required. It is interesting to note that in this study, SES was not related to any component of EF, above the effects of IQ and age. So far, no other study has examined current SES on adult EF; literature has instead used a longitudinal approach to examine how SES during a distinct period (typically childhood) predicts later EF (e.g., 110 ). The current findings therefore suggest that an individual’s SES can change over the lifespan, which may have an additional effect on cognition 111 , and that SES may be less critical for EF after childhood.

It is interesting to note that not every individual demonstrated the same developmental profile of EF; for example, some older adults show equivalent performance in tasks to younger adults. The current study used a cross-sectional design to identify when age-related differences emerge when examining performance on four key measures of EF abilities. Given the scope of this design, results can only assess group-level age-related changes. Cross-sectional studies are potentially confounded by cohort effects and might therefore overestimate age-related changes, potentially failing to accurately explore age-related changes in task performance at an individual-level (i.e., how an individual’s EF capacities change over time; see 112 ). For instance, prior studies using longitudinal analysis have highlighted that during middle age (i.e., 20–60 years), cognitive abilities such as speed of processing decline, but at a smaller rate than may be indicated in cross-sectional analyses (e.g., 113 ). The current study provides insight into the presence of age-related differences in EF abilities across the lifespan using a cross-sectional approach; it would be of interest in future to further this research by utilizing longitudinal designs to furthering our understanding of how EFs change with age, and individual differences that may influence these changes. It is also noted that the current sample consisted of a community sample of healthy adult volunteers functioning at high levels and may therefore, as discussed above, represent ‘successful’ aging within this particular population. There may be other factors that influence an individual’s performance on the EF tasks over and above age-related effects, which would be of interest to examine in future research; for example, there may be protective factors that offset declines in EFs, such as increased cardiovascular fitness in older age relating to better inhibitory control 114 .

As previously stated, EFs play an important role in daily life. Poor EFs can lead to social problems 80 , 81 , obesity and overeating 79 , 115 , lower productivity and difficulty keeping a job 82 , and people with better EF abilities have been shown to enjoy an improved quality of life 78 . Diamond 1 highlights the importance of EFs for maintenance of mental and physical health. Given this, it is important to further our understanding of how EF abilities continue to change and evolve across the lifespan, examining not only childhood/adolescence and older adulthood, but observing differences across all of adulthood. Furthering prior research that has sought to establish changes in EFs across the lifespan (e.g., 40 , 42 , 48 ; see also 41 ), the current study used four tasks to assess key EF abilities, including inhibitory control, working memory, cognitive flexibility, and planning abilities, providing further insight into cross-sectional changes seen in EF abilities across the lifespan. EF is a ‘functional construct’, involved in helping individuals conduct deliberate, goal-directed thoughts and actions 48 ; by examining which aspects of EF do or do not change across the lifespan, and which tasks are able to sensitively assess differences in EF abilities across different ages, we are able to gain information about the overall EF construct. The tasks used in the current study were shown to be suitable for use with individuals from ten to 86 years of age, sensitively detecting differences in EF abilities. Additionally, by identifying the ages at which changes in EFs are seen, we may be able to develop targeted interventions to help maintain efficient EF capacities, in turn assisting in increased success in real-world scenarios. By analysing the data in the current study as a continuous sample, allowing curvilinear relationships to be examined, results highlight changes in EF abilities can be observed from young adulthood, and emphasise the importance of looking at all ages when examining cognitive changes, rather than focussing on ‘younger’ versus ‘older’ age groups.

We explored developmental changes in inhibitory control, working memory, cognitive flexibility, and planning ability from 10 years old to 86 years old in a large, community-based sample of healthy individuals. We show that working memory capacity and planning ability continue to develop over adolescence and into early adulthood. Crucially, we show that declines emerge as early as the third decade of life in inhibitory control, working memory, and planning, which is much earlier than has previously been considered. In addition, we demonstrate a dissociation for measures of cognitive flexibility, with switch costs decreasing and mixing costs increasing up to older age, indicating that adolescents and young adults have difficulties switching tasks sets, whereas middle-aged and older adults have difficulties maintaining task sets. In general, studies have largely overlooked adulthood as a period of change in EFs, with studies focussing on their development in childhood, or comparing dichotomous groups of young versus older adults in studies of cognitive aging. The findings of the current study highlight the value of including adolescents and middle-aged adults to provide a comprehensive lifespan description of the distinct developmental trajectories of EFs.

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This work was carried out with the support of a European Research Council grant to HF (Ref: CogSoCoAGE; 636458). The datasets and code supporting this article are available on the Open Science Framework ( https://osf.io/qzrwu ).

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Heather J. Ferguson

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H.F. conceived of the study, designed the study, won the funding, data analysis and interpretation, and revised the manuscript; V.B. contributed to study design, data collection, data analysis and interpretation, and drafting the manuscript; E.B. contributed to study design, data collection, and revising the manuscript. All authors gave final approval for publication.

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Ferguson, H.J., Brunsdon, V.E.A. & Bradford, E.E.F. The developmental trajectories of executive function from adolescence to old age. Sci Rep 11 , 1382 (2021). https://doi.org/10.1038/s41598-020-80866-1

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DOI : https://doi.org/10.1038/s41598-020-80866-1

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Cognitive Predictors of Everyday Problem Solving across the Lifespan

Affiliation.

• 1 Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA.
• PMID: 28273664
• PMCID: PMC5471120
• DOI: 10.1159/000459622

Background: An important aspect of successful aging is maintaining the ability to solve everyday problems encountered in daily life. The limited evidence today suggests that everyday problem solving ability increases from young adulthood to middle age, but decreases in older age.

Objectives: The present study examined age differences in the relative contributions of fluid and crystallized abilities to solving problems on the Everyday Problems Test (EPT). We hypothesized that due to diminishing fluid resources available with advanced age, crystallized knowledge would become increasingly important in predicting everyday problem solving with greater age.

Method: Two hundred and twenty-one healthy adults from the Dallas Lifespan Brain Study, aged 24-93 years, completed a cognitive battery that included measures of fluid ability (i.e., processing speed, working memory, inductive reasoning) and crystallized ability (i.e., multiple measures of vocabulary). These measures were used to predict performance on EPT.

Results: Everyday problem solving showed an increase in performance from young to early middle age, with performance beginning to decrease at about age of 50 years. As hypothesized, fluid ability was the primary predictor of performance on everyday problem solving for young adults, but with increasing age, crystallized ability became the dominant predictor.

Conclusion: This study provides evidence that everyday problem solving ability differs with age, and, more importantly, that the processes underlying it differ with age as well. The findings indicate that older adults increasingly rely on knowledge to support everyday problem solving, whereas young adults rely almost exclusively on fluid intelligence.

Keywords: Age-related changes; Cognition; Everyday problem solving; Intelligence.

© 2017 S. Karger AG, Basel.

Publication types

• Research Support, N.I.H., Extramural
• Activities of Daily Living
• Aged, 80 and over
• Aging / psychology*
• Middle Aged
• Problem Solving*
• Psychometrics
• Young Adult

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• R37 AG006265/AG/NIA NIH HHS/United States
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Intellectual Development Through Adulthood and the Effects of Age on the Functions of Memory

Last updated 23 Sept 2022

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In this study note we highlight some key features of intellectual development through adulthood.

Early Adulthood

During early adulthood, individuals continue to develop logical thinking . This is now applied (alongside skills and knowledge) into the workplace, where they are tasked to problem solve and make decisions about more complex situations.

Middle Adulthood

As people move into middle adulthood, their ability to retrieve information may be more difficult. There is some dispute as to whether our memory is starting to decline (where did I put my phone) or whether our brain starts to focus on other information.

How Age Affects the Functions of Memory in Later Adulthood

• Memory loss that occurs during later adulthood can result in difficulty recalling and learning new information.
• In most cases this is not clinical memory loss, but what scientists refer to as lapses in memory function.
• Daily activities such as completing word and number puzzles help to keep the brain active and healthy.
• Severe issues with recall and remembering may indicate cognitive decline and types of dementia that would require further testing by specialists.
• Scientists have discovered that throughout adulthood new brain cells are produced within the hippocampus. This is the areas of the brain that is involved in learning, memory and emotions.
• Intellectual development
• Later adulthood
• Early adulthood
• Middle adulthood

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Physical development in early childhood (3-8 years), physical changes in early adulthood (19-45 years), perimenopause in early adulthood, physical changes in middle adulthood, physical changes in middle adulthood (46-65 years): menopause, physical changes in later adulthood (65+ years), intellectual development, chomsky: language acquisition in infancy and early childhood, our subjects.

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Chapter 9: Late Adulthood

Attention and problem solving.

Changes in Attention in Late Adulthood: Changes in sensory functioning and speed of processing information in late adulthood often translates into changes in attention (Jefferies et al., 2015). Research has shown that older adults are less able to selectively focus on information while ignoring distractors (Jefferies et al., 2015; Wascher, Schneider, Hoffman, Beste, & Sänger, 2012), although Jefferies and her colleagues found that when given double time, older adults could perform at young adult levels. Other studies have also found that older adults have greater difficulty shifting their attention between objects or locations (Tales, Muir, Bayer, & Snowden, 2002). Consider the implication of these attentional changes for older adults.

How do changes or maintenance of cognitive ability affect older adults’ everyday lives? Researchers have studied cognition in the context of several different everyday activities. One example is driving. Although older adults often have more years of driving experience, cognitive declines related to reaction time or attentional processes may pose limitations under certain circumstances (Park & Gutchess, 2000). In contrast, research on interpersonal problem solving suggested that older adults use more effective strategies than younger adults to navigate through social and emotional problems (Blanchard-Fields, 2007). In the context of work, researchers rarely find that older individuals perform poorer on the job (Park & Gutchess, 2000). Similar to everyday problem solving, older workers may develop more efficient strategies and rely on expertise to compensate for cognitive decline.

Problem Solving : Problem solving tasks that require processing non-meaningful information quickly (a kind of task that might be part of a laboratory experiment on mental processes) declines with age. However, many real-life challenges facing older adults do not rely on speed of processing or making choices on one’s own. Older adults resolve everyday problems by relying on input from others, such as family and friends. They are also less likely than younger adults to delay making decisions on important matters, such as medical care (Strough, Hicks, Swenson, Cheng & Barnes, 2003; Meegan & Berg, 2002).

What might explain these deficits as we age? The processing speed theory , proposed by Salthouse (1996, 2004), suggests that as the nervous system slows with advanced age our ability to process information declines . This slowing of processing speed may explain age differences on many different cognitive tasks. For instance, as we age, working memory becomes less efficient (Craik & Bialystok, 2006). Older adults also need longer time to complete mental tasks or make decisions. Yet, when given sufficient time older adults perform as competently as do young adults (Salthouse, 1996). Thus, when speed is not imperative to the task healthy older adults do not show cognitive declines.

In contrast, inhibition theory argues that older adults have difficulty with inhibitory functioning, or the ability to focus on certain information while suppressing attention to less pertinent information tasks (Hasher & Zacks, 1988). Evidence comes from directed forgetting research. In directed forgetting people are asked to forget or ignore some information, but not other information. For example, you might be asked to memorize a list of words, but are then told that the researcher made a mistake and gave you the wrong list, and asks you to “forget” this list. You are then given a second list to memorize. While most people do well at forgetting the first list, older adults are more likely to recall more words from the “forget-to-recall” list than are younger adults (Andrés, Van der Linden, & Parmentier, 2004).

Cognitive losses exaggerated: While there are information processing losses in late adulthood, overall loss has been exaggerated (Garrett, 2015). One explanation is that the type of tasks that people are tested on tend to be meaningless. For example, older individuals are not motivated to remember a random list of words in a study, but they are motivated for more meaningful material related to their life, and consequently perform better on those tests. Another reason is that the research is often cross-sectional. When age comparisons occur longitudinally, however, the amount of loss diminishes (Schaie, 1994). A third reason is that the loss may be due to a lack of opportunity in using various skills. When older adults practiced skills, they performed as well as they had previously. Although diminished performance speed is especially noteworthy in the elderly, Schaie (1994) found that statistically removing the effects of speed diminished the individual’s performance declines significantly. In fact, Salthouse and Babcock (1991) demonstrated that processing speed accounted for all but 1% of age-related differences in working memory when testing individuals from 18 to 82. Finally, it is well established that our hearing and vision decline as we age. Longitudinal research has proposed that deficits in sensory functioning explain age differences in a variety of cognitive abilities (Baltes & Lindenberger, 1997).

• Authored by : Martha Lally and Suzanne Valentine-French. Provided by : College of Lake County Foundation. Located at : http://dept.clcillinois.edu/psy/LifespanDevelopment.pdf . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Early Adulthood: Challenges You Are Likely to Face

We all know that life can be difficult sometimes, and each developmental stage has its own set of hardships. However, perhaps when we reach the early periods of adulthood, we are presented with the biggest challenges.

Early adulthood is the stage where we start developing our identity, which is often a make-or-break situation. During this time, we start to realize our individuality, and we begin to make choices for ourselves and our futures. We wrote this article to prepare you for that.

Early Adulthood Challenges

There are plenty of hurdles that you will face in early adulthood, and most of them are unavoidable. The inevitability of them is a call-to-action that we must be better versions of ourselves to stand strong and develop holistically from every possible learning curve. That being said, here are some of the most common early adulthood challenges.

Physical Peak

When we reach early adulthood, our bodies are at their best possible states. While that can present itself as a benefit, it becomes a disadvantage if we’re unable to utilize our physical abilities to the best of our interests and if we fail to maintain a healthy lifestyle. We tend to use our body the most during this stage, and it can be tempting to always push it to the limit. However, this can cause chronic illnesses if we don’t give our bodies proper and adequate rest.

Wisdom Teeth

Although this next challenge seems like a minor concern, it is actually quite a pressing issue. You will soon realize it as soon as you face it. The development of wisdom teeth is rather painful, almost to a paralyzing degree that prevents us from fully functional. It’s actually quite common for people to resort to the extraction of wisdom teeth to avoid their negative impacts.

There are plenty of  chronic illnesses linked to stress , and because it is during early adulthood that we face lots of challenges at once, it is also the point where we begin to develop these diseases. In fact, around 70 to 90% of adults seek professional help for stress-related concerns. That’s why it’s essential that we also develop healthy coping mechanisms to reduce stress to a manageable degree.

Separation from Parents

As we develop our individual selves, we begin to focus more on our careers to start a life of our own. Since at this point, we have lived most of our lives sheltered by our parents. Because of this, one challenge that we’re likely to face is separation from our providers. It is a learning opportunity for us to take control of ourselves and develop our autonomy. Although this can present itself with considerable concerns, gradually detaching ourselves from our parents is essential to the development of our independence.

Intimate Relationships

Early adulthood is also the stage where we start to develop our abilities to share intimacy. This is the time when we begin to explore and experiment on finding a relationship built on love. While this is also something that can be beneficial, it can become a problem because we have to share a huge part of ourselves with someone else, and it can be distressing when it’s not reciprocated. We need to ensure that our identities, beliefs, and principles are already established at this point for us not to suffer from the consequences of failed intimate relationships.

At this point in our lives, we have already established strong friendships with people from our past, but it’s also the period where we form new ones. It can be difficult to juggle all of these relationships, especially if we’re also trying to focus on our careers and family. Be that as it may, forming friendships is vital in our growth because we need other people’s perspectives on some of our problems.

It’s difficult to find the  balance between practicality and passion . Some of our skills lead to high-paying jobs, while other skills don’t pay out. That’s why plenty of people often have to let go of their passion for advancing in their careers. It’s important that we still find outlets for what we love doing because this will save us from facing this conflict.

Early adulthood is a critical stage of our lives, and we must guarantee that we’re making the right choices to develop our individualities. We will inevitably commit mistakes along the way, but seeing these as learning opportunities is essential to our growth. That seems to be the only possible solution for us to create progress at this point in our lives.

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Cognitive Development in Late Adulthood

Diana Lang; Nick Cone; Sonja Ann Miller; Martha Lally; and Suzanne Valentine-French

There are numerous stereotypes regarding older adults as being forgetful and confused, but what does the research on memory and cognition in late adulthood actually reveal? In this section, we wil l focus upon t he impact of aging on memory, how a ge impacts cognitive functioning, and a bnormal memory loss due to Alzheimer’s disease, deliriu m, and dementia. [1]

How does aging affect memory?

The Sensory Register

Aging may create small decrements in the sensitivity of the senses.  And, to the extent that a person has a more difficult time hearing or seeing,   that information will not be stored in memory. This is an important point, because many older people assume that if they cannot remember something, it is because their memory is poor. In fact, it may be that the information was never seen or heard.

The Working Memory

Older people have more difficulty using memory strategies to recall details. [2] Working memory is a cognitive system with a limited capacity responsible for temporarily holding information available for processing . As we age, the working memory loses some of its capacity. This makes it more difficult to concentrate on more than one thing at a time or to remember details of an event.  However, people often compensate for this by writing down information and avoiding situations where there is too much going on at once to focus on a particular cognitive task.

When an elderly person demonstrates difficulty with multi-step verbal information presented quickly, the person is exhibiting problems with working memory. Working memory is among the cognitive functions most sensitive to decline in old age. Several explanations have been offered for this decline in memory functioning; one is the processing speed theory of cognitive aging by Tim Salthouse. Drawing on the findings of general slowing of cognitive processes as people grow older, Salthouse argues that slower processing causes working-memory contents to decay, thus reducing effective capacity. [3] For example, if an elderly person is watching a complicated action movie, they may not process the events quickly enough before the scene changes, or they may processing the events of the second scene, which causes them to forget the first scene. The decline of working-memory capacity cannot be entirely attributed to cognitive slowing, however, because capacity declines more in old age than speed.

Another proposal is the inhibition hypothesis advanced by Lynn Hasher and Rose Zacks [4] . This theory assumes a general deficit in old age in the ability to inhibit irrelevant, or no-longer relevant, information. Therefore, working memory tends to be cluttered with irrelevant contents which reduce the effective capacity for relevant content. The assumption of an inhibition deficit in old age has received much empirical support but, so far, it is not clear whether the decline in inhibitory ability fully explains the decline of working-memory capacity.

An explanation on the neural level of the decline of working memory and other cognitive functions in old age was been proposed by Robert West. He argued that working memory depends to a large degree on the pre-frontal cortex, which deteriorates more than other brain regions as we grow old. [5] Age related decline in working memory can be briefly reversed using low intensity transcranial stimulation, synchronizing rhythms in bilateral frontal and left temporal lobe areas.

The Long-Term Memory

Long-term memory involves the storage of information for long periods of time. Retrieving such information depends on how well it was learned in the first place rather than how long it has been stored. If information is stored effectively, an older person may remember facts, events, names and other types of information stored in long-term memory throughout life. The memory of adults of all ages seems to be similar when they are asked to recall names of teachers or classmates. And older adults remember more about their early adulthood and adolescence than about middle adulthood. [6] Older adults retain semantic memory or the ability to remember vocabulary.

Younger adults rely more on mental rehearsal strategies to store and retrieve information. Older adults focus rely more on external cues such as familiarity and context to recall information. [7] And they are more likely to report the main idea of a story rather than all of the details. [8]

A positive attitude about being able to learn and remember plays an important role in memory. When people are under stress (perhaps feeling stressed about memory loss), they have a more difficult time taking in information because they are preoccupied with anxieties. Many of the laboratory memory tests require comparing the performance of older and younger adults on timed memory tests in which older adults do not perform as well. However, few real life situations require speedy responses to memory tasks. Older adults rely on more meaningful cues to remember facts and events without any impairment to everyday living.

New Research on Aging and Cognition

Can the brain be trained in order to build cognitive reserve to reduce the effects of normal aging? ACTIVE (Advanced Cognitive Training for Independent and Vital Elderly), a study conducted between 1999 and 2001 in which 2,802 individuals age 65 to 94, suggests that the answer is “yes.” These participants received 10 group training sessions and 4 follow up sessions to work on tasks of memory, reasoning, and speed of processing. These mental workouts improved cognitive functioning even 5 years later. Many of the participants believed that this improvement could be seen in everyday tasks as well. [9] Learning new things, engaging in activities that are considered challenging, and being physically active at any age may build a reserve to minimize the effects of primary aging of the brain.

Watch this video from SciShow Psych to learn about ways to keep the mind young and active.

You can view the transcript for “The Best Ways to Keep Your Mind Young” here (opens in new window) .

Changes in Attention in Late Adulthood

Divided attention has usually been associated with significant age-related declines in performing complex tasks. For example, older adults show significant impairments on attentional tasks such as looking at a visual cue at the same time as listening to an auditory cue because it requires dividing or switching of attention among multiple inputs. Deficits found in many tasks, such as the Stroop task which measures selective attention, can be largely attributed to a general slowing of information processing in older adults rather than to selective attention deficits per se. They also are able to maintain concentration for an extended period of time. In general, older adults are not impaired on tasks that test sustained attention, such as watching a screen for an infrequent beep or symbol.

The tasks on which older adults show impairments tend to be those that require flexible control of attention, a cognitive function associated with the frontal lobes. Importantly, these types of tasks appear to improve with training and can be strengthened. [10]

An important conclusion from research on changes in cognitive function as we age is that attentional deficits can have a significant impact on an older person’s ability to function adequately and independently in everyday life. One important aspect of daily functioning impacted by attentional problems is driving. This is an activity that, for many older people, is essential to independence. Driving requires a constant switching of attention in response to environmental contingencies. Attention must be divided between driving, monitoring the environment, and sorting out relevant from irrelevant stimuli in a cluttered visual array. Research has shown that divided attention impairments are significantly associated with increased automobile accidents in older adults [11]   Therefore, practice and extended training on driving simulators under divided attention conditions may be an important remedial activity for older people. [12]

Problem Solving

Problem solving tasks that require processing non-meaningful information quickly (a kind of task which might be part of a laboratory experiment on mental processes) declines with age. However, real life challenges facing older adults do not rely on speed of processing or making choices on one’s own. Older adults are able to resolve everyday problems by relying on input from others such as family and friends. They are also less likely than younger adults to delay making decisions on important matters such as medical care. [13] [14]

Brain Functioning

Research has demonstrated that the brain loses 5% to 10% of its weight between 20 and 90 years of age. [15] This decrease in brain volume appears to be due to the shrinkage of neurons, lower number of synapses, and shorter length of axons. According to Garrett, [16] the normal decline in cognitive ability throughout the lifespan has been associated with brain changes, including reduced activity of genes involved in memory storage, synaptic pruning, plasticity, and glutamate and GABA (neurotransmitters) receptors. There is also a loss in white matter connections between brain areas. Without myelin, neurons demonstrate slower conduction and impede each other’s actions. A loss of synapses occurs in specific brain areas, including the hippocampus (involved in memory) and the basal forebrain region. Older individuals also activate larger regions of their attentional and executive networks, located in the parietal and prefrontal cortex, when they perform complex tasks. This increased activation correlates with a reduced performance on both executive tasks and tests of working memory when compared to those younger. [17]

Despite these changes the brain exhibits considerable plasticity, and through practice and training, the brain can be modified to compensate for age-related changes. [18] Park and Reuter-Lorenz [19] proposed the Scaffolding Theory of Aging and Cognition which states that the brain adapts to neural atrophy (dying of brain cells) by building alternative connections, referred to as scaffolding. This scaffolding allows older brains to retain high levels of performance. Brain compensation is especially noted in the additional neural effort demonstrated by those individuals who are aging well. For example, older adults who performed just as well as younger adults on a memory task used both prefrontal areas, while only the right prefrontal cortex was used in younger participants. [20] Consequently, this decrease in brain lateralization appears to assist older adults with their cognitive skills.

Can we improve brain functioning? Many training programs have been created to improve brain functioning. ACTIVE (Advanced Cognitive Training for Independent and Vital Elderly), a study conducted between 1999 and 2001 in which 2,802 individuals age 65 to 94, suggests that the answer is “yes”. These racially diverse participants received 10 group training sessions and 4 follow up sessions to work on tasks of memory, reasoning, and speed of processing. These mental workouts improved cognitive functioning even 5 years later. Many of the participants believed that this improvement could be seen in everyday tasks as well. [21] However, programs for the elderly on memory, reading, and processing speed training demonstrate that there is improvement on the specific tasks trained, but there is no generalization to other abilities. [22] Further, these programs have not been shown to delay or slow the progression of Alzheimer’s disease. Although these programs are not harmful, “physical exercise, learning new skills, and socializing remain the most effective ways to train your brain” (p. 207). These activities appear to build a reserve to minimize the effects of primary aging of the brain.

Parkinson’s disease

Parkinson’s disease is characterized by motor tremors, loss of balance, poor coordination, rigidity, and difficulty moving . [23] Parkinson’s affects approximately 1% of those over the age of 60, and it appears more frequently in family members in a little less than 10% of cases. Twenty-eight chromosomal areas have been implicated in Parkinson’s disease, but environmental factors have also been identified and include brain injury. Being knocked unconscious once increases the risk by 32%, and being knocked out several times increases the risk by 174%. [24] Other environmental influences include toxins, industrial chemicals, carbon monoxide, herbicides and pesticides. [25] The symptoms are due to the deterioration of the substantia nigra, an area in the midbrain whose neurons send dopamine-releasing axons to the basal ganglia which affects motor activity. Treatment typically includes the medication levodopa (L-dopa), which crosses the blood-brain barrier and is converted into dopamine in the brain. Deep brain stimulation, which involves inserting an electrode into the brain that provides electrical stimulation, has resulted in improved motor functioning. [26]

Similar to other adults, older adults need between 7 to 9 hours of sleep per night, but they tend to go to sleep earlier and get up earlier than those younger. This pattern is called advanced sleep phase syndrome and is based on changes in circadian rhythms. [27] There are sleep problems in older adults, and insomnia is the most common problem in those 60 and older. [28] People with insomnia have trouble falling asleep and staying asleep . There are many reasons why older people may have insomnia, including certain medications, being in pain, having a medical or psychiatric condition, and even worrying before bedtime about not being able to sleep. Using over the counter sleep aids or medication may only work when used for a short time. Consequently, sleep problems should be discussed with a health care professional.

Also, common in older adults are sleep disorders, including sleep apnea, restless legs syndrome, periodic limb movement disorder, and rapid eye movement sleep behavior disorder. [29] Sleep apnea refers to repeated short pauses in breathing, while an individual sleeps, that can lead to reduced oxygen in the blood . Snoring is a common symptom of sleep apnea and it often worsens with age. Untreated sleep apnea can lead to impaired daytime functioning, high blood pressure, headaches, stroke, and memory loss. Restless legs syndrome feels like there is tingling, crawling, or pins and needles in one or both legs, and this feeling is worse at night.  Periodic limb movement disorder causes people to jerk and kick their legs every 20 to 40 seconds during sleep. Rapid eye movement sleep behavior disorder occurs when one’s muscles can move during REM sleep and sleep is disrupted. 

According to the National Sleep Foundation, [30] there are many medical conditions that affect sleep and include gastroesophageal reflux disease, diabetes mellitus, renal failure, respiratory diseases such as asthma, and immune disorders. Diseases such as Parkinson’s disease and multiple sclerosis also commonly cause problems sleeping. Lastly, Alzheimer’s disease can interfere with sleeping patterns. Individuals may wake up many times during the night, wander when up, and yell which can alter the amount of time they sleep. Both minor and significant sleep problems in older adults can lead to increased risk of accidents, falls, chronic fatigue, decreased quality of life, cognitive decline, reduced immune function, and depression. [31]

Because of sleep problems experienced by those in late adulthood, research has looked into whether exercise can improve their quality of sleep. Results show that 150 minutes per week of exercise can improve sleep quality. [32] This amount of exercise is also recommended to improve other health areas including lowering the risk for heart disease, diabetes, and some cancers. Aerobic activity, weight training, and balance programs are all recommended. For those who live in assisted living facilities even light exercise, such as stretching and short walks, can improve sleep. High intensity activity is not necessary to see improvements. Overall, the effects of exercise on sleep may actually be even larger for older adults since their sleep quality may not be ideal to start.

Intelligence and Wisdom

When looking at scores on traditional intelligence tests, tasks measuring verbal skills show minimal or no age-related declines, while scores on performance tests, which measure solving problems quickly decline with age. [33] This profile mirrors crystalized and fluid intelligence. As you recall from last chapter, crystallized intelligence encompasses abilities that draw upon experience and knowledge. Measures of crystallized intelligence include vocabulary tests, solving number problems, and understanding texts. Fluid intelligence refers to information processing abilities, such as logical reasoning, remembering lists, spatial ability, and reaction time. Baltes [34] introduced two additional types of intelligence to reflect cognitive changes in aging. Pragmatics of intelligence are cultural exposure to facts and procedures that are maintained as one ages and are similar to crystalized intelligence . Mechanics of intelligence are dependent on brain functioning and decline with age, similar to fluid intelligence. Baltes indicated that pragmatics of intelligence show little decline and typically increase with age. Additionally, pragmatics of intelligence may compensate for the declines that occur with mechanics of intelligence. In summary, global cognitive declines are not typical as one ages, and individuals compensate for some cognitive declines, especially processing speed.

Wisdom is the ability to use the accumulated knowledge about practical matters that allows for sound judgment and decision making . A wise person is insightful and has knowledge that can be used to overcome obstacles in living. Does aging bring wisdom? While living longer brings experience, it does not always bring wisdom. Paul Baltes and his colleagues [35] [36]   suggest that wisdom is rare. In addition, the emergence of wisdom can be seen in late adolescence and young adulthood, with there being few gains in wisdom over the course of adulthood. [37] This would suggest that factors other than age are stronger determinants of wisdom. Occupations and experiences that emphasize others rather than self, along with personality characteristics, such as openness to experience and generativity, are more likely to provide the building blocks of wisdom. [38] Age combined with a certain types of experience and/or personality brings wisdom.

Attention and Problem Solving

Changes in sensory functioning and speed of processing information in late adulthood often translates into changes in attention. [39] Research has shown that older adults are less able to selectively focus on information while ignoring distractors, [40] [41] although Jefferies and her colleagues found that when given double time, older adults could perform at young adult levels. Other studies have also found that older adults have greater difficulty shifting their attention between objects or locations. [42] Consider the implication of these attentional changes for older adults.

How do changes or maintenance of cognitive ability affect older adults’ everyday lives? Researchers have studied cognition in the context of several different everyday activities. One example is driving. Although older adults often have more years of driving experience, cognitive declines related to reaction time or attentional processes may pose limitations under certain circumstances. [43] In contrast, research on interpersonal problem solving suggested that older adults use more effective strategies than younger adults to navigate through social and emotional problems. [44] In the context of work, researchers rarely find that older individuals perform poorer on the job. [45] Similar to everyday problem solving, older workers may develop more efficient strategies and rely on expertise to compensate for cognitive decline.

Problem solving tasks that require processing non-meaningful information quickly (a kind of task that might be part of a laboratory experiment on mental processes) declines with age. However, many real-life challenges facing older adults do not rely on speed of processing or making choices on one’s own. Older adults resolve everyday problems by relying on input from others, such as family and friends. They are also less likely than younger adults to delay making decisions on important matters, such as medical care. [46] [47]

Deficit theories

The processing speed theory , proposed by Salthouse, [48] [49] suggests that as the nervous system slows with advanced age our ability to process information declines . This slowing of processing speed may explain age differences on many different cognitive tasks. For instance, as we age, working memory becomes less efficient. [50] Older adults also need longer time to complete mental tasks or make decisions. Yet, when given sufficient time older adults perform as competently as do young adults. [51] Thus, when speed is not imperative to the task healthy older adults do not show cognitive declines.

In contrast, inhibition theory argues that older adults have difficulty with inhibitory functioning, or the ability to focus on certain information while suppressing attention to less pertinent information tasks . [52] Evidence comes from directed forgetting research. In directed forgetting people are asked to forget or ignore some information, but not other information. For example, you might be asked to memorize a list of words, but are then told that the researcher made a mistake and gave you the wrong list, and asks you to “forget” this list. You are then given a second list to memorize. While most people do well at forgetting the first list, older adults are more likely to recall more words from the “forget-to-recall” list than are younger adults. [53]

Cognitive losses exaggerated

While there are information processing losses in late adulthood, overall loss has been exaggerated. [54] One explanation is that the type of tasks that people are tested on tend to be meaningless. For example, older individuals are not motivated to remember a random list of words in a study, but they are motivated for more meaningful material related to their life, and consequently perform better on those tests. Another reason is that the research is often cross-sectional. When age comparisons occur longitudinally, however, the amount of loss diminishes. [55] A third reason is that the loss may be due to a lack of opportunity in using various skills. When older adults practiced skills, they performed as well as they had previously. Although diminished performance speed is especially noteworthy in the elderly, Schaie [56] found that statistically removing the effects of speed diminished the individual’s performance declines significantly. In fact, Salthouse and Babcock [57] demonstrated that processing speed accounted for all but 1% of age-related differences in working memory when testing individuals from 18 to 82. Finally, it is well established that our hearing and vision decline as we age. Longitudinal research has proposed that deficits in sensory functioning explain age differences in a variety of cognitive abilities. [58]

Abnormal Loss of Cognitive Functioning During Late Adulthood

Historically, the term dementia was used to refer to an individual experiencing difficulties with memory, language, abstract thinking, reasoning, decision making, and problem-solving. [59] While the term dementia is still in common use, in the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) [60] the term dementia has been replaced by neurocognitive disorder. A Major Neurocognitive Disorder is diagnosed as a significant cognitive decline from a previous level of performance in one or more cognitive domains and interferes with independent functioning, while a Minor Neurocognitive Disorder is diagnosed as a modest cognitive decline from a previous level of performance in one of more cognitive domains and does not interfere with independent functioning. There are several different neurocognitive disorders that are typically demonstrated in late adulthood, and determining the exact type can be difficult because the symptoms may overlap with each other. Diagnosis often includes a medical history, physical exam, laboratory tests, and changes noted in behavior.

Common symptoms of dementia include emotional problems, difficulties with language, and a decrease in motivation. A person’s consciousness is usually not affected. Globally, dementia affected about 46 million people in 2015. About 10% of people develop the disorder at some point in their lives, and it becomes more common with age. About 3% of people between the ages of 65–74 have dementia, 19% between 75 and 84, and nearly half of those over 85 years of age. In 2015, dementia resulted in about 1.9 million deaths, up from 0.8 million in 1990. As more people are living longer, dementia is becoming more common in the population as a whole.

Dementia generally refers to severely impaired judgment, memory or problem-solving ability. It can occur before old age and is not an inevitable development even among the very old. Dementia can be caused by numerous diseases and circumstances, all of which result in similar general symptoms of impaired judgment, etc. Alzheimer’s disease is the most common form of dementia and is incurable, but there are also nonorganic causes of dementia which can be prevented. Malnutrition, alcoholism, depression, and mixing medications can also result in symptoms of dementia. If these causes are properly identified, they can be treated. Cerebral vascular disease can also reduce cognitive functioning.

Delirium , also known as acute confusional state, is an organically caused decline from a previous baseline level of mental function that develops over a short period of time, typically hours to days. It is more common in older adults, but can easily be confused with a number of psychiatric disorders or chronic organic brain syndromes because of many overlapping signs and symptoms in common with dementia, depression, psychosis, etc. Delirium may manifest from a baseline of existing mental illness, baseline intellectual disability, or dementia, without being due to any of these problems.

Delirium is a syndrome encompassing disturbances in attention, consciousness, and cognition. It may also involve other neurological deficits, such as psychomotor disturbances (e.g. hyperactive, hypoactive, or mixed), impaired sleep-wake cycle, emotional disturbances, and perceptual disturbances (e.g. hallucinations and delusions), although these features are not required for diagnosis. Among older adults, delirium occurs in 15-53% of post-surgical patients, 70-87% of those in the ICU, and up to 60% of those in nursing homes or post-acute care settings. Among those requiring critical care, delirium is a risk for death within the next year.

Alzheimer’s Disease

Alzheimer’s disease (AD) , also referred to simply as Alzheimer’s, is the most common cause of dementia, accounting for 60-70% of its cases. Alzheimer’s   is a progressive disease causing problems with memory, thinking and behavior. Symptoms usually develop slowly and get worse over time, becoming severe enough to interfere with daily tasks. [61]

Alzheimer’s disease is probably the most well-known and most common neurocognitive disorder for older individuals. In 2016, an estimated 5.4 million Americans were diagnosed with Alzheimer’s disease, [62] which was approximately one in nine aged 65 and over. By 2050, the number of people age 65 and older with Alzheimer’s disease is projected to be 13.8 million if there are no medical breakthroughs to prevent or cure the disease. Alzheimer’s disease is the 6th leading cause of death in the United States, but the 5th leading cause for those 65 and older. Among the top 10 causes of death in America, Alzheimer’s disease is the only one that cannot be prevented, cured, or even slowed. Current estimates indicate that Alzheimer disease affects approximately 50% of those identified with a neurocognitive disorder. [63]

Alzheimer’s disease has a gradual onset with subtle personality changes and memory loss that differs from normal age-related memory problems occurring first. Confusion, difficulty with change, and deterioration in language, problem-solving skills, and personality become evident next. In the later stages, the individual loses physical coordination and is unable to complete everyday tasks, including self-care and personal hygiene. [64] Lastly, individuals lose the ability to respond to their environment, to carry on a conversation, and eventually to control movement (Alzheimer’s Association, 2016). The disease course often depends on the individual’s age and whether they have other health conditions.

Alzheimer’s is the sixth leading cause of death in the United States. On average, a person with Alzheimer’s lives four to eight years after diagnosis, but can live as long as 20 years, depending on other factors. Alzheimer’s is not a normal part of aging. The greatest known risk factor is increasing age, and the majority of people with Alzheimer’s are 65 and older. But Alzheimer’s is not just a disease of old age. Approximately 200,000 Americans under the age of 65 have younger-onset Alzheimer’s disease (also known as early-onset Alzheimer’s). [65]

The cause of Alzheimer’s disease is poorly understood. About 70% of the risk is believed to be inherited from a person’s parents with many genes usually involved. Other risk factors include a history of head injuries, depression, and hypertension. The disease process is associated with plaques and neurofibrillary tangles in the brain. A probable diagnosis is based on the history of the illness and cognitive testing with medical imaging and blood tests to rule out other possible causes. Initial symptoms are often mistaken for normal aging, but examination of brain tissue, specifically of structures called plaques and tangles, is needed for a definite diagnosis. Though qualified physicians can be up to 90% certain of a correct diagnosis of Alzheimer’s, currently, the only way to make a 100% definitive diagnosis is by performing an autopsy of the person and examining the brain tissue. In 2015, there were approximately 29.8 million people worldwide with AD. In developed countries, AD is one of the most financially costly diseases.

This Ted-Ed video explains some of the history and biological diagnosis of Alzheimer’s.

You can view the transcript for “What is Alzheimer’s disease? – Ivan Seah Yu Jun” here .

Samuel Cohen researches Alzheimer’s disease and other neurodegenerative disorders. Listen to Cohen’s TED Talk on Alzheimer’s disease to learn more.

• This chapter was adapted from select chapters in Lumen Learning's Lifespan Development , authored by Martha Lally and Suzanne Valentine-French available under a Creative Commons Attribution-NonCommercial-ShareAlike license , and Waymaker Lifespan Development , authored by Sonja Ann Miller for Lumen Learning and available under a Creative Commons Attribution license . Some selections from Lumen Learning were adapted from previously shared content from Laura Overstreet's Lifespan Psychology and Wikipedia. ↵
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How Wisdom Emerges from Intellectual Development: A Developmental/Historical Theory for Raising Mandelas

Andreas demetriou.

1 Department of Psychology, University of Nicosia, Nicosia 1700, Cyprus

2 Cyprus Academy of Science, Letters, and Arts, Nicosia 1700, Cyprus

Antonis Liakos

3 Faculty of History and Archaeology, National and Kapodistrian University of Athens, 15784 Athens, Greece; moc.liamg@sokailotna

Niyazi Kizilyürek

4 Department of Turkish and Middle Eastern Studies, University of Cyprus, Kallipoleos 75, Nicosia 1678, Cyprus; yc.ca.ycu@izayin

Associated Data

Not applicable.

This paper invokes cognitive developmental theory as a means for preparing citizens to deal with and resolve conflicts within or across nations. We take the centuries-old Greek–Turkish dispute as an example. We first outline a theory of intellectual development postulating that mental changes emerge in response to changing developmental priorities in successive life periods, namely, interaction control in infancy, attention control and representational awareness in preschool, inferential control and cognitive management in primary school, and advanced forms of reasoning and self-evaluation in adolescence. Based on this model, we outline a control theory of wisdom postulating that different aspects of wisdom emerge during development as different levels of control of relations with others: trust and care for others in infancy, taking the other’s perspective, reflectivity, and empathy in preschool, rationality and understanding the rules underlying individual and group interactions in primary school, and understanding the general principles of societal operation explaining the differences in approach and interest between groups in adolescence and early adulthood. We also outline the educational implications of this theory for the education of citizens by capitalizing on intellectual strengths at successive developmental periods to comprehensively understand the world and to act prudently when dealing with interpersonal and social or national conflict. Finally, the paper discusses the political constraints and implications of this theory. This is the first attempt to derive wisdom from the development of cognitive and personality processes from infancy through early adulthood and to connect it to serious world problems.

The modern world is a world of nation states and the foundation history of the nation states is a history of warfare. There is almost not a single nation state that did not emerge from war. Wars create a background of nationalism and uneven development which protract conflict and tension between or within countries for a long time after they end. Conflicts and tensions compromise life and well-being. One such conflict is the conflict between Greeks and Turks. The Greek nation state emerged from an independence war against the Ottoman Empire (1821–1828); modern Turkey was founded as a modern nation state after a war with Greece (1919–1921). Cyprus is a battlefield where the Greek–Turkish conflict is endlessly protracted. The national desire of Greek Cypriots to unite with Greece and counter the national desire of Turkish Cypriots for division of Cyprus between Greek Cypriots and Turkish Cypriots turned Cyprus into a hotspot of ethnic conflict, where the end of history ( Fukuyama 1992 ) is still far away.

The Republic of Cyprus emerged as an independent state from a war against British colonialism (1955–1959) and was established as a constitutional bi-communal state governed by its two dominant communities, Greeks, about 80% of the population, and Turks, about 18%. Obviously, historical forces were too strong for the small young state to overcome. Even more so, the division between the two communities was institutionalized in the constitution: although political bodies, such as the government, the parliament, and public administration, were proportionally shared by the two communities, education was separate and under the jurisdiction of an independent body in each community. This was enough to perpetuate separatist and conflictual national narratives, undermining the very functioning of political institutions. The state collapsed soon after independence, in 1963, because of strong disputes about distribution of power between the two communities and lack of unifying pressures coming from the people. Since then, inter-communal tensions and conflict never ended and peaked in a coup and civil war within the Greek Cypriot community in 1974; as a result, a Turkish invasion followed, causing displacement of about one-third of the population and occupation of one-third of the island by Turkish forces since then. Negotiations about a political settlement of the dispute have continued under the auspices of the United Nations since 1963. In the words of a UN envoy, negotiations in Cyprus go well as long as they go on!

Our position here is that the consequences of this dispute for the life of people are disproportionately large. The issues at stake changed drastically over historical time, so that division and conflict cause more harm than profit for any other than those perpetuating their power by capitalizing on division, such as political parties and politicians across the divide. In fact, none of the causes that ignited the Greek revolution against the Ottomans or the wars in the late 19th and early 20th century between Greece and Turkey is currently active. However, the leadership and the peoples in Greece, Turkey, and Cyprus refrain from making decisions that would bring the conflict to an end. Legalistic disputes about distribution of power and jurisdiction in a common federal state draw on the centuries-old Greek–Turkish conflict which fuels current political disputes and conflicts of interests with historical arguments within and across the two nations. It is well known that perpetuating negative narratives about the other in groups with conflictual relations facilitates further conflict, preventing search for means that would facilitate overcoming the conflict ( Psaltis et al. 2020 ). This background causes lack of trust and tolerance, ethnic and nationalist tension, and self-perpetuating hostility drawing on misdeeds against each other.

Some scholars proposed that the average level of intelligence in a population is related to the level of democracy, technological advancement, and prosperity and the ability to efficiently resolve tensions and disputes with other countries or between groups within a country ( Rindermann 2018 ). The higher the level, the better it is for peace and well-being. Rindermann suggested that wars in the modern world abound in regions where secular intelligence is considerably lower than the standard average. However, it is questionable whether current theory of intelligence would suffice to help nations to overcome disputes. Rindermann’s argument above may be cyclical: there may well be social and political factors other than individual intelligence causing national or social success, which subsequently result in provisions raising individual intelligence, such as generalized education. In fact, international history abounds with examples where advanced nations made fatally self-defeating decisions, including Germany, Rindermann’s country.

We argue that no theory of intelligence or intellectual development suffices to generate solutions for serious social or political problems. To be useful for this sake, any theory would have to be much broader, accounting for individual and social development on several fronts, such as the following:

• This theory would have to account for both (a) individual mental development and (b) the contribution of individual development to the functioning of the social groups and institutions at various levels, increasingly distancing from an individual’s own life, such as family, city, nation, and humanity. This requires understanding how cognitive, social, moral, and personality development interact. Currently, no comprehensive theory exists that would do justice to these interactions. Piaget’s theory might have been the closest approximation to it, delving into the common core of intellectual ( Piaget 1968 ), social ( Piaget 1951 ), and moral development ( Piaget 1932 ). However, Piaget’s theory, gearing every aspect of understanding on the development of logical reasoning, is not accepted anymore. Current theories are too fragmented to satisfy the requirements above (see Demetriou and Spanoudis 2018 ).

Finally, provisions are needed for the organization and functioning of social and political institutions, such as parliament and other decision-making bodies, to guide social and political goal setting and problem solving, enabling the handling of strong historical forces that compromise the present and future of people.

1. Historical and Epistemological Concerns: How Wisdom Emerges from Intellectual Development

The developmental model outlined here draws on two major sources: current theory and research on intellectual development and current theory and research on wisdom in dealing with important life problems and societal issues. We argue that intellectual development and the development of wisdom are more closely related than assumed in the literature. Our central position is that neither of these lines of research alone is sufficient for the attainment of the aims above. The theory of intellectual development focuses on cognitive processes in childhood and adolescence. The theory of wisdom focuses on a specific approach to problem-solving and decision-making in adulthood. Historically, the two fields operationalized constructs differently. However, thinking and problem-solving in actual life need not respect the boundaries of different research fields in psychological research.

The psychology of intelligence adopted Binet’s ( Binet and Simon [1908] 1948 ).) priorities for studying intelligence in the early 20th century. These focused on mental processes which are important for school learning, such as reasoning, language, and comprehension of concepts. The field was admirably successful in identifying individual differences in these processes and directing educational decision-making accordingly ( Anastasi 2005 ). The psychology of cognitive development, under the influence of Piaget ( 1968 ), focused on the development of reasoning ( Flavell 1985 ). Notably, Piaget’s career as a researcher started in Binet’s laboratory. Additionally, as an epistemologist interested in the nature and origins of knowledge, Piaget prioritized the study of logical reasoning and understanding philosophically defined categories of reason, such as number, space, and causality, rather than life-important decision-making. Piaget’s ideas caused important progress in our understanding of cognitive development. However, both the individual differences and the developmental approach to cognition did ill-justice to problem-solving and decision-making of broad social or societal interest, the focus of this article.

The study of wisdom has a different history. In classical Greek philosophy, wisdom involves nous (mind), discerning reality, and episteme , knowing and reasoning on universal truths. Phronesis involves the ability to make correct decisions and reflect on experience for important life matters. Current psychological theory and research on wisdom includes both ancient constructs ( Ferrari 2009 ). It is considered a complex state of mind and personality enabling adults to use personal life experiences and a broad knowledge basis to make prudent judgments about complex personal, interpersonal, and social problems, which are recognized as inclusive, balanced, moral, and beneficial for everyone involved ( Ardelt et al. 2019 ; Baltes and Smith 1990 ; Staudinger 2008 ; Sternberg and Karami 2021 ).

Wisdom so conceived is intelligence at its best. The road to it is intellectual development, gradually constructing a scaffold of thought enabling one to discern and evaluate reality, know universal and historical truths and reason on them, make practically beneficial decisions given the situation, and reflect on them to become better for the future. Wisdom has been an object of research on adult development but not of development in childhood, underestimating the fact that wise adults have been children . The study of cognitive development focused on changes in cognitive processes from infancy through early adulthood, but it did not examine how problems of everyday childhood or adolescence are resolved. The position of this paper is that all aspects of wisdom have precursors in early cognitive development. Thus, our central concern is to ensure that wisdom will gradually emerge from each phase of intellectual development, preparing citizens to deal intelligently and prudently with personal, national, and international issues involving conflict.

2. Empirical Concerns: How Wisdom and Intelligence Are Related

All theories assume that wisdom includes intelligence and much more. This is reflected in the moderate correlation, circa.3, between various processes addressed by intelligence research, such as processing speed, reasoning, and vocabulary, and processes addressed by wisdom research, such as integration of multiple points of view, ethical considerations, and self-transcendence ( Glück 2020a , 2020b ; Grossmann et al. 2020 ). In neo-Piagetian theory, wisdom was associated with attaining formal or postformal reasoning ( Kallio 1995 ). Therefore, there is wide agreement that “wisdom is not a form of intelligence, nor is intelligence a form of wisdom” Glück ( 2020a, p. 17 ). Wisdom requires the following processes: (1) cognitive processes underlying intelligence, such as reasoning, reflection, and metacognition; (2) creativity to conceive of new solutions and interpretations, if old solutions do not suffice; (3) knowledge of the persons and situations requiring solutions, “the pragmatics of life”; (4) products (ideas and solutions) balancing interests of the parties involved, which are recognized by all to advance common good; (5) the motivation and affectivity to act accordingly ( Sternberg and Karami 2021 ; Sternberg et al. 2019 ). Processes in points 1 and 2 become increasingly integrated and abstract with development, accounting for individual differences in cognitive ability. Points 3 and 4 draw on the processes in 1 and 2 to acquire knowledge and skill in different conceptual or activity domains. Mastering these domains requires drawing on the first two types of processes in the fashion required for mastering other complex domains, such as science. Processes in point 5 relate to motivation and dispositions to get involved in all other processes.

Thus, intelligence–wisdom relations appear as an investment cascade: fluid reasoning together with certain interests and personality dispositions allow the construction of a broad knowledge base underlying crystallized intelligence, which may be extended, by some people, into the “big questions” of human existence. Some of these people may capitalize on their own and others’ life challenges and develop wisdom, if some intellectual possibilities are present and if they have certain personality qualities, such as openness, empathy, and self-reflectivity. According to Glück ( 2020a ), wise people have high levels of intelligence, and they are open, reflective, empathic, and ethical; however, not all intelligent people are wise.

Some complex domains, such as science, are learned in systematic long studies. No such program exists for wisdom. Theories of wisdom assume that becoming wise requires long-term life planning, optimum life management, and life review, allowing to make meaning of past decisions and actions and optimize future ones ( Baltes and Smith 1990 ; Staudinger 2008 ). Thus, learning by doing is assumed. The present article suggests that wisdom might develop more broadly if wisdom-building mechanisms are properly guided to become invested with the knowledge required together with handling the personality dispositions required to provide the emotional context and motivation for wise thinking and decisions. In other words, we suggest that education for wisdom must be part of a program aiming to support intellectual development.

The model of wisdom-based reasoning provides the operationalization of wisdom that is necessary for the development of a program for educating wisdom that draws on intellectual development. This model postulates that wise thinking draws on four aspects of cognition ( Brienza et al. 2021 ; Grossmann 2017 ): (a) intellectual humility, emerging from recognition of the limits of one’s own knowledge; (b) recognition that there may be multiple points of view or perspectives or that a current issue of concern belongs to a broader context; (c) recognition that views or interests often change in social relations or in time; (d) systematic search for the integration of different opinions, which implies recognition of compromise as part of social decision-making. We show below that all four aspects of cognition required for wisdom-based reasoning are acquired in early and middle childhood as building blocks of cognitive development.

3. A Cognitive Developmental Theory for Intelligence and Wisdom

3.1. the mental architecture.

The theory of intellectual development we employ makes four fundamental assumptions ( Demetriou et al. 2018a ; Demetriou and Spanoudis 2018 ).

First, the human mind involves mental processes that carry out different tasks for understanding and problem-solving, namely, control functions, enabling focusing on information and action critical at a given moment and updating as required; integration functions, enabling grasping relations, generalizing, and validly filling in missing information; and cognizance, enabling awareness of the objects of mental activity and mental processes.

Second, these processes are functionally intertwined, always operating together as a unit at various levels, from perception to abstract thinking; meaning-making emerges from their integrated functioning. This unit, named noetron, after nous, the Greek term for mind ( Demetriou et al. 2021 ), operates as a “master-algorithm,” coordinating current goals and relational integration with awareness of mental processes, their objects, and contents. Noetron is constantly updated in reference to the results of ongoing activity, mental or actual, coordinating feedforward expectations with feedback from the results of activity; matching feedforward with feedback information enables subjective experiences ascribing intrinsic values to mental states and activities, allowing choices among them according to their value suggested by successes and failures. Any organism lacking this form of a unifying mental agency would not be capable of capitalizing on a balanced combination of past knowledge and experience for the sake of future-oriented understanding or action. Ideally, this combination optimizes choices by selecting the best fitting experience or concept to vary, based on how the future is conceived. This is a fundamental condition for the development of wisdom, which is an advanced aspect of comprehensive intelligent judgment.

Third, noetron expands with development, generating increasingly inclusive repertoires of action or thought choices. In cognitive science terms, this expansion gradually generates a Language of Thought including tokens of experience and action (representations), rules for their possible legitimate relations (reasoning) ( Fodor 1975 ), and exemplars from experience providing ready-made frames for rule implementation. In other words, noetron is embedded into an expanding system integrating perceptual experiences into representations by various forms of rules underlying forms of reasoning, such as inductive, analogical, and deductive reasoning, and problem-solving scripts. Increasing flexibility in choosing and using different forms of reasoning and scripts implies increasingly efficient levels of control, for instance, going from action control in infancy to representational control in preschool to inferential control in primary school to truth control in adolescence.

Fourth, with age, the profile of mental ability varies depending on the developmental needs for exercising control at successive developmental phases. Developmental priorities change with the functional state of noetron. When a process is highly demanded for efficient noetron functioning, mastering this process becomes a dominant developmental priority. Changes in mastering this process become highly helpful for learning and highly predictive of learning outcomes in various domains, including school learning ( Demetriou et al. 2019b , 2020a , 2020b , 2020c ). After a critical integration point in satisfying functional demands, the two may vary independently, because the formation of mental ability shifts to other priorities, showing dependence on other processes ( Demetriou et al. 2017 ; Demetriou and Spanoudis 2018 ). Developmental priorities and their relations with cognitive processes and wisdom are discussed below. Table 1 summarizes developmental priorities for cognitive and wisdom development and their educational implications.

Dominant cognitive, wisdom, and educational priorities as a function of age.

3.2. Developing Mind: From Cognition to Wisdom

Precursors of wisdom in infancy . Episodic representations dominate in infancy. These are mental states preserving the spatial and time properties of actions and experiences. Thus, interaction control, allowing efficiency of actions on objects, is the developmental priority of infancy ( Demetriou and Spanoudis 2018 ). Cognitively, mastering interaction control provides the background for understanding that one’s own actions may have implications for objects and persons and that taking control needs effort. However, strictly speaking, infants cannot be wise because they lack the representational resolution, integrative power, awareness, knowledge, and emotional stability required for wisdom. Mastering interaction control, the developmental priority of this cycle, sets the background for later attainment of wisdom. This background is set when interacting with objects and persons, exploring differences across them, engaging actively with them but staying calm when results violate expectations, seeking help to improve interactions, and enabling the infant to realize that some solutions are better than others and that attaining them requires trying out alternative solutions.

Individual differences in emotionality and affectivity appear early in infancy and these may interfere in mastering interaction control ( Soto et al. 2011 ; Roberts et al. 2006 ). Differences between infants in reactivity to persons and objects suggest that some infants are more likely than others to enter the road to wisdom. Infants high in activity, attraction to novelty, and inclination to affiliate, but low in intense emotional reactions predisposing for self-control are more likely to enter this road. Infants low in these attributes and high in emotionality need special care to take control of their interactions in a context of emotional security. Emotionally, mastering interaction control lays the ground for trust and security in dealing with oneself and others. Failing to protect infants from these weaknesses may channel them away from coherent, balanced, and beneficial interactions with others later in life.

Attaining intellectual humility, decentering, and recognition of uncertainty in early childhood . Realistic mental representations emerge from episodic representation at 2–3 years and are associated with symbols, such as words and mental images. These dominate in preschool, from 2 to 7 years. Hence, representational awareness and attention control are the major developmental priorities in this period. Mastering attention control enables the attainment of more complex cognitive tasks, such as organizing action according to represented goals, following ongoing verbal interactions, and exploring the behavior and interactions between other persons and objects ( Demetriou et al. 2018a ; Diamond 2013 ; Zelazo 2015 ). Mastering symbol systems, such as language, and subjecting action under the control of representation, renders awareness of representations and control of attention important. Awareness of representations enables individuals to become social partners and negotiate each other’s views or intentions; it also provides a representational insight that views of reality are often mirrored in each person’s representations. Preschool children’s strong interest in the imaginary worlds of fairy tales and movies reflects humans’ emergent realization that the world may be represented by alternative, often surprising, ways ( Hinchcliffe 2006 ) and that using them helps explore their possible differences and functionalities ( Dubourg and Baumard 2021 ). Education of tolerance and empathy may capitalize on the preschooler’s discovery of imaginary worlds.

Notably, three of the four aspects of wisdom-based reasoning emerge in this period. The precursor of intellectual humility is children’s awareness of their ignorance. Children talk explicitly about their own and others’ knowledge and they admit their own ignorance ( Harris et al. 2017 ). In addition, by the age of 4–5 years, children revise incorrect interpretations in the light of new information. By the age of 7–8 years, children are aware that visual or oral input can be ambiguous, and they differentiate the conditions of epistemic uncertainty from physical uncertainty ( Robinson et al. 2006 ). This understanding predates recognition of uncertainty and change. Grasp of Theory of Mind ( Wellman 2014 ) at 3–5 years strongly suggests that preschoolers understand that mental states and beliefs may differ between individuals, depending on the sources of information they have access to. These achievements are precursors of the recognition of others’ perspectives and relevance to context ( Hughes and Leekham 2004 ). They also predate the reflective stance about oneself and others, a pivotal component of wisdom.

Children high in awareness of others’ mental and emotional states are more likely than children who are low in these processes to engage in activities leading to wisdom. These children may understand that activities and objects may be shared and that goals may be better attained by persons working together than one person working alone. If sociable, helpful, and generous to others, organized, systematic, planful, and creative, children may realize that they may have a role in leading shared activities and gaining satisfaction from success and praise. However, the reasoning needed to grasp the underlying causal relations between events and realities or between motives and their effects is still weak in this cycle. Moreover, control of social interaction and openness to experience are not yet well refined and consolidated ( Demetriou et al. 2018a , 2018b ; Roberts et al. 2006 ). Together, these weaknesses hinder the recognition of problems of importance to a group and designing broadly beneficial problem-solving activities. Thus, synthesizing beyond one’s own experience and perspective is limited.

For the present concerns, it would be useful if children in this cycle are guided to reflect on how their activities may be benevolent or may cause pain or distress in others ( Weststrate et al. 2018 ). The keen representational interests of this cycle may be used to familiarize children with the experiences of children belonging to other groups and develop empathy for their agemates belonging to the other group. This requires ad hoc educational programs allowing children to listen to the stories of the others from the others , to remember the stories of others, and to hold group-specific narratives against each other. The aim is to build a conception of the world where self-centered attitudes and ethnocentric heroism are relativized vis-à-vis an overall human narrative where human life and general well-being dominate as standards for individual action ( Kizilyurek 2019 ).

Search for integration and compromise in primary school . With representational awareness and attention control established by 5–6 years, priorities change in primary school, from 7 to 11 years. The relations between representations need to be worked out and accurately represented. Hence, cognitive priorities are redirected from knowing the represented world and coupling representations with the environment to the relations between representations and concepts themselves. Holding representations active for as long as required to process relations and connect them by inference are the major priorities of this period. Inductive inference is the major tool for grasping the relations between objects and concepts because it enables transfer of meaning from experience to novel situations. Therefore, processes for handling memory and inductive inference are the major contributors to the formation of general cognitive ability in this period. Explicit deductive reasoning emerges at the end of this period, from 8–11 years, reflecting the integration of inferential rules into a system where one representation may be systematically viewed from the point of view of others. It is notable that from 7–9 years, children recognize that two viewers or listeners might make different interpretations, depending on what information they have access to and the inference used to connect them ( Kazi et al. 2019 ; Spanoudis et al. 2015 ).

Therefore, the fourth requirement for wisdom-based reasoning, the search for integration and compromise, emerges in this period of life. In rule-based thought, children have the mental capacity to employ reasoning to inter-relate activities and emotions with the requests or needs of others, such as parents, siblings, and school mates, when they diverge from intentions and wishes. This enables children to formulate concepts organizing one’s own experiences and action plans, understand the role of rules and prescriptions in one’s life, and use them to generate or negotiate solutions to problems. Rule-based thinkers may be aware of the underlying connections between concepts, events, and experiences, or actions, thoughts, and motives. They may also use previous knowledge or experience to anticipate consequences of actions or events. They can also be aware of different perspectives on the same event because they understand that information or knowledge causes differences in perceptions and attitudes, even if reasoning is the same. Therefore, rule-based thinkers may analyze problems rationally and can take a reflective stance toward others and problems, which is conducive to tolerance of alternative views and their possible synthesis into solutions going beyond one’s own preferences.

However, rule-based thought often fails to grasp higher-order relations highlighting links between seemingly unrelated rules or systems; in addition, rule-based thought lacks truth control tools protecting from fallacious reasoning. This often causes personal biases and perspectives to dominate, especially if promoted by authority, in individuals who are not socially oriented, emotionally stable, or open to novelty. For instance, lack of openness may hinder individuals from seeing problems from the perspective of others. These individuals may not avail themselves to opportunities conducive to wisdom. In short, many adults do not reach wisdom if they stay in rule-based thought.

To deal with the weaknesses of rule-based thought, children in this cycle must be induced to explicitly grasp the rules under which conflicting groups operate. They must reflect on how conflicting rules may cause conflicting actions, which may in turn cause pain and compromise the interests and well-being of all persons involved. They must also be induced to redefine each other’s rules so that commonly accepted and beneficial ones may be conceived and implemented. There is evidence that practicing distanced self-reflection in the third person about conflict situations increases wise judgments by widening one’s own often narrow self-focus ( Grossmann et al. 2020 ). In addition, teaching by example and historical role models may be the method of choice to enable rule-based thinkers to see how important figures approached complex problems and experience, from their point of view, the benefits obtained for themselves (becoming important figures in society) and others (improvement of their condition) (see Grossmann 2017 ).

Building wisdom-based knowledge in adolescence . Intellectual changes are consolidated in adolescence when controlled reasoning is fully established. Overarching principles integrating rules into systems according to truth and validity dominate in adolescence. Principles enable thinkers to grasp when it is and when it is not possible to use an inferential rule to infer a state of reality based on this rule. Thus, a critical approach to reality is possible. These processes strengthen until middle age. In addition, cognitive self-evaluation and self-representation become powerful factors in the formation of cognitive ability among university-educated persons in middle age ( Demetriou and Bakracevic 2009 ; Demetriou et al. 2017 ). In adulthood, persons must take control of their life, regardless of how far they have gone on each of the cognitive and personality development dimensions discussed above. Entering the worlds of work, family, and citizenship without the protective shields provided by parents and schools imposes strong cognitive, personality, and social requirements.

Therefore, principle-based thought is, by definition, an important tool for transcending one’s own subjectivity to view problems from the point of view of other persons or alternative contexts, a condition for wisdom ( Ardelt et al. 2017 , 2019 ; Demetriou n.d. ; Kallio 1995 ). Sound deductive reasoning is a truth control system enabling persons to evaluate solutions and perspectives according to their truth, value, and scope in concern with the persons and stakes involved. The epistemic stance enables persons to realize that even the best solutions may be relative and subject to revision. Thus, at the individual level, these types of thought may guide the development of long-term life plans, such as choosing a course of studies or a profession, balancing value judgments for one’s own weaknesses and strengths vis-à-vis a preferred lifestyle or social role. At the social level, they may enable individuals to grasp assumptions and prescriptions of multiple contexts in which they live and enable problem-solving, generating solutions, decisions, and courses of action that are optimal for the individual and other persons or institutions affected.

Scholars argue that higher levels of mental functioning, such as principle-based thought, openness, and higher levels of ego development, are complementary aspects of the same construct: the mature mind ( Costa and McCrae 1993 ; McCrae and Costa 1997 ) which creatively integrates cognitive, personality, and emotional trends and proclivities in dealing with problems ( Demetriou et al. 2018c ). It is notable that wisdom in later life is associated with openness to novelty, mindedness, and well-being coming from having purpose in life, satisfaction, positive relations with others, environmental mastery, and a general concern for the well-being of others ( Wink and Staudinger 2016 ). In this cycle, adolescents must be induced to consider the multiplicity of factors that may cause a conflict, such as historical, religious, political, and economic reasons and grasp their underlying principles. They must also be induced to consider historical events and decisions of political or historical figures from the point of view of all of the actors involved. This would enable them to understand that wise decisions are often those which may not appear right at the time they are taken. They must also be induced to understand that the modern narratives about these events may serve purposes other than the interests and well-being of the individuals or institutions involved.

4. A Developmental Model for Wise Conflict Management in Schools

In conclusion, we have suggested that wisdom is an emergent system of control integrating cognitive, emotional, and personality abilities and attributes for the sake of efficient, constructive, and self-enhancing activities and relations with others. This is a long process starting from infancy, building on attaining control of processes dominating in successive periods of life. The central idea is that childhood is important for integrating wise judgment into spontaneous cognitive functioning because satisfying the developmental priorities of each cycle causes the necessary build-up of the cognitive and personality characteristics required for wise judgment. Mastering interactions with persons and objects (infancy) provides the background for knowing that one’s actions have implications for objects and other persons. Mastering executive control and becoming aware of mental worlds (early childhood) are necessary for judgments acceptable by many. Mastering inference and using it to organize understanding and action (middle and late childhood) is necessary for understanding that views and decisions build up mentally and are built on rule systems that may differ between persons or groups. Mastering principles, imagining possible worlds, and understanding that inference and interpretation may not always be true or valid (adolescence) is necessary for adopting the critical stance, enabling to analyze and explore truths and search systematically for truth. Mastering the art of balanced and constructive choices embedding judgments and decisions in societal, cultural, and historical perspectives requires knowledge and experience drawn from autonomous life (early adulthood). All of these must be acquired in a positive context enabling persons to take responsibility for their life, develop trust for others, and become motivated to be constructive for themselves and others.

The theory above may guide enhancement of tolerance for social and political differences and capitalizing on them for efficient and productive functioning in a world of differences and diversities in such a way that it may enhance learning in more classic school domains. Specifically, there is research showing that school performance at successive educational levels is best predicted by the processes associated with the developmental priorities of each developmental cycle: command of attention control processes and representational awareness at preschool, management of working memory and inductive reasoning in primary school, and mastery of deductive reasoning, language, and accurate self-evaluation at secondary school ( Demetriou et al. 2019a , 2019b , 2020a , 2020b , 2021 ). Importantly, training of mental processes transfers to general cognitive ability only if aligned with developmental priorities: training on attention control and theory of mind in preschool ( Rueda et al. 2012 ), training on working memory in primary school ( Holmes and Gathercole 2014 ), and training on relational integration ( Klauer and Phye 2008 ; Papageorgiou et al. 2016 ) and deductive reasoning schemes in secondary school ( Christoforides et al. 2016 ).

5. Ending History Wars: Overcoming a Side-Effect of Democracy

National educational goals and priorities are shaped by the orientations and priorities of a society and the institutions implementing them ( Demetriou 2013 ; Nisbett 2003 ). In modern states, educational goals and priorities exist at several levels, often in conflict with each other. Education is addressed to everyone, aiming to enable the understanding and use of complex knowledge and technology in societies where differences must be accepted and honored. However benign these aims are, they are often understood differently, and they are not unconditionally accepted by different groups and stakeholders. Parties and organizations compete for priorities and orientations of society, including education, because it prepares citizens for the future, thereby affecting their own existence and role. Modern states are governed by several authorities with time-limited overlapping mandates and mutually balanced powers. Therefore, shaping the aims of education and educational practices is complicated and tricky business.

It is assumed that the level and quality of education depends on the quality of democracy and vice versa. It is also assumed that the level and quality of education and democracy in a country causes improvement in the relations of this country with other countries, facilitating intelligent and wise analysis and resolution of disputes. Inversely, it is assumed that wars stem more frequently from authoritarian regimes than democracies. However, unfortunately, democracy may have its share in starting a war. In the national rivalry between Greece and Turkey, strangely enough, things proved more peaceful under authoritarian regimes or conservatist governments (Metaxas dictatorship 1936–1941, post-war Greek authoritarian governments 1945–1963 and 1967–1973) than under democratic rule and populist governments (George Papandreou’s government 1963–1967 and Andreas Papandreou’s governments in the eighties). This may be ascribed to the major influence that well-preserved nationalist ideas have on the public sphere and public opinion. These are preserved in the national narrative by education. They are often part of identity-building policies that shape the orientations of education in both Greece ( Liakos 2008 , 2011 ) and Turkey and Cyprus ( Kizilyurek and Kizilyurek 2004 ). Individual identities and related attitudes and feelings are then exploited by politicians to increase their political appeal and access to power. In a sense, fossilized national narratives function as political traps channeling nations to wrong directions, because people’s rule and democracy are not always compatible with the relativization of national differences and the culture of rights of minorities and different groups within a nation or multinational entities, such as the European Union or multinational countries. National rivalry comes not from the elites, but from the peoples themselves.

History wars often reflect this situation and become a tool of democratic functioning, protracting real wars or social polarization within societies. Obviously, this interpretation does not imply that we favor authoritarian over democratic governance. On the contrary, it highlights the hurdles of democracy with the aim to remove them from political practices for the sake of its further development. It is the task of education to raise critical and wise citizens from infancy through adulthood. Hopefully, this would enable nations to properly understand and weigh social and international problems when dealing with conflict and rivalries. Even this benign aim may be disputed because it might be interpreted by some to endanger the continuation of a nation as a distinct entity in time and space. Thus, provisions are needed to ensure that wisdom-based education and upbringing are accepted and implemented by all nations involved. Perhaps, the European Union is the most interesting historical and political experiment designed to achieve these aims.

However, even this is disputed, especially in nations where national, social, and political orientations are not settled. In these nations, the ideal for a European citizen is often interpreted with caution, because no commonly accepted answer exists on how much a European identity may be integrated with national identity. In Greece, there is still insecurity toward Europe because many believe that it endangers national religion, values, and traditions ( Stavridi-Patrikiou 2007 ). In Cyprus, there is an ongoing discussion that the establishment and success of the state of Cyprus will eventually compromise Greek identity, among the Greek Cypriots, or Turkish identity, among the Turkish Cypriots, in favor of a Cypriot identity. Political parties or other institutions, such as the Church, object to the development of new curricula in several subjects, especially history, language, and religion, because they are concerned that this is ill-intentioned, aiming to increase distance between Cyprus and Greece (or Turkey, depending upon the community). A few years ago, a new curriculum, developed under the leadership of the first author as the then Minister of Education and Culture, with the aim to develop a culture of historical reconciliation and mutual tolerance between the Greek and the Turkish Cypriot ignited fierce history wars among the Greek Cypriots and was strongly opposed by many, with the Church in the lead. The Archbishop of Cyprus went as far as to state in public that he “will invite people to burn the new history books” because they supposedly endanger national history, as he himself understands it. In fact, a new history war started again while this revision, about the teaching of the role of Ataturk during the Greek–Turkish war in the early 20th century, was in progress. Many stars would have to align before the implementation of psychological models, such as the present one, would change society. Without this alignment, these models may appear as an interesting academic exercise, at best.

Mandela was a wise man who led his country to end apartheid, a long and very painful conflict between the white and black populations of South Africa. Four Mandelas may be needed to resolve the Greek–Turkish dispute: one in Athens, one in Ankara, and two in Nicosia (a Greek Cypriot and a Turkish Cypriot). We do not have them yet. Perhaps, we will not have them for as long as populist nationalist elites across the divide instrumentalize historical and political disputes about nation and national identity. After all, Mandela emerged from South Africa’s prisons, not its schools! However, it may be time for countries that shaped history for centuries, such as Greece and Turkey, to protect their citizens and future generations from war, letting their Mandelas to emerge from their schools rather than from battlefields or prisons.

These Mandelas would have to understand the history of both nations in the long term; they must understand the reasons which caused their conflict in the past and the reasons which caused changes in their relations over the centuries. They must also understand that relations between nations change with changes in the wider historical and cultural context. For instance, neither Greece nor Turkey or Cyprus operate in the context of the Byzantine or the Ottoman empire; they rather operate in a completely different context, including the European Union. The distribution of power and influence is not primarily dependent on military power but on other forms of soft power, such as science and cultural productions. In this regard, cooperation is a win-win multiplicative factor of power and influence; military competition is a loss-loss factor weakening all nations involved in many different respects. If education would raise a majority of Mandelas in both nations (in all nations for that matter), then naturally Mandelas would emerge in the leadership of both nations, leading them to a new chapter in their history.

This paper outlined a theory of individual-social development that may help raise them and call them to service. Raising Mantelas in this fashion will enable nations to overcome historical conflicts in which they are trapped. Times scales in the resolution of these problems are much larger than individual lives. Thus, raising many Mandelas in education may be the optimal management of the future by societies trapped in their past.

6. Conclusions

In short, a theory of intellectual development that may lead to a deeper and more comprehensive understanding of social and political problems and to collective wisdom was outlined. To our knowledge, this is the first theory attempting to derive wisdom from the development of cognitive and personality processes from infancy through early adulthood and connect it to serious world problems. This theory aims to (i) advance a deeper understanding of social and political problems since early childhood, (ii) advance individual wisdom for the sake of social well-being and long-term human interests, and (iii) guide education to capitalize on developmental priorities to develop knowledge and mental skills conducive to a wise decision-making ability when dealing with conflicts and disputes. Obviously, this model needs to be tested empirically. Ideally, longitudinal evidence would show that individuals performing high in childhood on tasks addressed to recognition of one’s own ignorance, understanding others’ perspectives, and integrating rules in overarching systems serving the interests of different individuals are more likely to demonstrate wisdom in real-world problems in adulthood. This type of research is time and resource demanding. Alternatively, training of these processes would have to generalize to the four aspects of wise reasoning and cause changes in inter-group attitudes in the fashion found by Brienza et al. ( 2021 ).

It was also argued that long-held national narratives and convictions may be incompatible with attaining these aims. The mechanisms for choosing individual leaders in modern democracies and their political functioning when chosen may be incompatible with international and supra-social goal setting and dealing with conflicts. Leaders are mostly elected by nations or different political and social groups to maintain and enhance political, social, and economic interests of their constituencies. Therefore, the success of the present model would be facilitated if political institutions are founded that would ensure integrative, wise, and future-oriented policies and practices rather than policies trapped into the past and motivated to sustain historical divisions and tensions. Probably, countries need a Wisdom Authority to watch and guide analysis and decision-making of problems. Only Mandelas would have to stuff a country’s Wisdom Authority.

Author Contributions

A.D. shaped the psychological part, A.L. shaped the historical part, and N.K. shaped political science part of the article. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

There is no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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10.9: Cognitive Development and Memory in Late Adulthood

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• Page ID 60540
• Lumen Learning

Learning Outcomes

• Discuss the impact of aging on memory
• Explain how age impacts cognitive functioning

How does aging affect memory?

The Sensory Register

Aging may create small decrements in the sensitivity of the senses. And, to the extent that a person has a more difficult time hearing or seeing, that information will not be stored in memory. This is an important point, because many older people assume that if they cannot remember something, it is because their memory is poor. In fact, it may be that the information was never seen or heard.

The Working Memory

Older people have more difficulty using memory strategies to recall details (Berk, 2007). Working memory is a cognitive system with a limited capacity responsible for temporarily holding information available for processing. As we age, the working memory loses some of its capacity. This makes it more difficult to concentrate on more than one thing at a time or to remember details of an event. However, people often compensate for this by writing down information and avoiding situations where there is too much going on at once to focus on a particular cognitive task.

When an elderly person demonstrates difficulty with multi-step verbal information presented quickly, the person is exhibiting problems with working memory. Working memory is among the cognitive functions most sensitive to decline in old age. Several explanations have been offered for this decline in memory functioning; one is the processing speed theory of cognitive aging by Tim Salthouse. Drawing on the findings of general slowing of cognitive processes as people grow older, Salthouse argues that slower processing causes working-memory contents to decay, thus reducing effective capacity. [1] For example, if an elderly person is watching a complicated action movie, they may not process the events quickly enough before the scene changes, or they may processing the events of the second scene, which causes them to forget the first scene. The decline of working-memory capacity cannot be entirely attributed to cognitive slowing, however, because capacity declines more in old age than speed.

Another proposal is the inhibition hypothesis advanced by Lynn Hasher and Rose Zacks. This theory assumes a general deficit in old age in the ability to inhibit irrelevant, or no-longer relevant, information. Therefore, working memory tends to be cluttered with irrelevant contents which reduce the effective capacity for relevant content. The assumption of an inhibition deficit in old age has received much empirical support but, so far, it is not clear whether the decline in inhibitory ability fully explains the decline of working-memory capacity.

An explanation on the neural level of the decline of working memory and other cognitive functions in old age was been proposed by Robert West. He argued that working memory depends to a large degree on the pre-frontal cortex, which deteriorates more than other brain regions as we grow old. [2] Age related decline in working memory can be briefly reversed using low intensity transcranial stimulation, synchronizing rhythms in bilateral frontal and left temporal lobe areas.

The Long-Term Memory

Long-term memory involves the storage of information for long periods of time. Retrieving such information depends on how well it was learned in the first place rather than how long it has been stored. If information is stored effectively, an older person may remember facts, events, names and other types of information stored in long-term memory throughout life. The memory of adults of all ages seems to be similar when they are asked to recall names of teachers or classmates. And older adults remember more about their early adulthood and adolescence than about middle adulthood (Berk, 2007). Older adults retain semantic memory or the ability to remember vocabulary.

Younger adults rely more on mental rehearsal strategies to store and retrieve information. Older adults focus rely more on external cues such as familiarity and context to recall information (Berk, 2007). And they are more likely to report the main idea of a story rather than all of the details (Jepson & Labouvie-Vief, in Berk, 2007).

A positive attitude about being able to learn and remember plays an important role in memory. When people are under stress (perhaps feeling stressed about memory loss), they have a more difficult time taking in information because they are preoccupied with anxieties. Many of the laboratory memory tests require comparing the performance of older and younger adults on timed memory tests in which older adults do not perform as well. However, few real life situations require speedy responses to memory tasks. Older adults rely on more meaningful cues to remember facts and events without any impairment to everyday living.

https://assessments.lumenlearning.co...essments/16657

New Research on Aging and Cognition

Can the brain be trained in order to build cognitive reserve to reduce the effects of normal aging? ACTIVE (Advanced Cognitive Training for Independent and Vital Elderly), a study conducted between 1999 and 2001 in which 2,802 individuals age 65 to 94, suggests that the answer is “yes.” These participants received 10 group training sessions and 4 follow up sessions to work on tasks of memory, reasoning, and speed of processing. These mental workouts improved cognitive functioning even 5 years later. Many of the participants believed that this improvement could be seen in everyday tasks as well (Tennstedt, Morris, et al, 2006). Learning new things, engaging in activities that are considered challenging, and being physically active at any age may build a reserve to minimize the effects of primary aging of the brain.

Watch this video from SciShow Psych to learn about ways to keep the mind young and active.

A YouTube element has been excluded from this version of the text. You can view it online here: http://pb.libretexts.org/lsdm/?p=390

You can view the transcript for “The Best Ways to Keep Your Mind Young” here (opens in new window) .

Wisdom is the ability to use common sense and good judgment in making decisions. A wise person is insightful and has knowledge that can be used to overcome obstacles they encounter in their daily lives. Does aging bring wisdom? While living longer brings experience, it does not always bring wisdom. Those who have had experience helping others resolve problems in living and those who have served in leadership positions seem to have more wisdom. So it is age combined with a certain type of experience that brings wisdom. However, older adults generally have greater emotional wisdom or the ability to empathize with and understand others.

Changes in Attention in Late Adulthood

Divided attention has usually been associated with significant age-related declines in performing complex tasks. For example, older adults show significant impairments on attentional tasks such as looking at a visual cue at the same time as listening to an auditory cue because it requires dividing or switching of attention among multiple inputs. Deficits found in many tasks, such as the Stroop task which measures selective attention, can be largely attributed to a general slowing of information processing in older adults rather than to selective attention deficits per se. They also are able to maintain concentration for an extended period of time. In general, older adults are not impaired on tasks that test sustained attention, such as watching a screen for an infrequent beep or symbol.

The tasks on which older adults show impairments tend to be those that require flexible control of attention, a cognitive function associated with the frontal lobes. Importantly, these types of tasks appear to improve with training and can be strengthened. [3]

An important conclusion from research on changes in cognitive function as we age is that attentional deficits can have a significant impact on an older person’s ability to function adequately and independently in everyday life. One important aspect of daily functioning impacted by attentional problems is driving. This is an activity that, for many older people, is essential to independence. Driving requires a constant switching of attention in response to environmental contingencies. Attention must be divided between driving, monitoring the environment, and sorting out relevant from irrelevant stimuli in a cluttered visual array. Research has shown that divided attention impairments are significantly associated with increased automobile accidents in older adults [4] Therefore, practice and extended training on driving simulators under divided attention conditions may be an important remedial activity for older people. [5]

Problem Solving

Problem solving tasks that require processing non-meaningful information quickly (a kind of task which might be part of a laboratory experiment on mental processes) declines with age. However, real life challenges facing older adults do not rely on speed of processing or making choices on one’s own. Older adults are able to resolve everyday problems by relying on input from others such as family and friends. They are also less likely than younger adults to delay making decisions on important matters such as medical care (Strough et al., 2003; Meegan & Berg, 2002).

https://assessments.lumenlearning.co...essments/16658

[glossary-page] [glossary-term]long-term memory:[/glossary-term] [glossary-definition]the storage of information over an extended period[/glossary-definition]

[glossary-term]working memory:[/glossary-term] [glossary-definition]a cognitive system with a limited capacity that is responsible for temporarily holding information available for processing[/glossary-definition] [/glossary-page]

• Salthouse, TA (1996). The processing-speed theory of adult age differences in cognition. Psychology Review. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/8759042 . ↵
• West, Robert (1996). An application of prefrontal cortex function theory to cognitive aging. Psychological Bulletin. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/8831298 . ↵
• Glisky EL. Changes in Cognitive Function in Human Aging. In: Riddle DR, editor. Brain Aging: Models, Methods, and Mechanisms. Boca Raton (FL): CRC Press/Taylor & Francis; 2007. Chapter 1. Available from: https://www.ncbi.nlm.nih.gov/books/NBK3885/ ↵
• McDowd JM, Shaw RJ. Attention and aging: a functional perspective. In: Craik FIM, Salthouse TA, editors. The Handbook of Aging and Cognition. 2. ↵
• Erlbaum; Mahwah, NJ: 2000. p. 221., FN Park DC, Gutchess AH. Cognitive aging and everyday life. In: Park D, Schwarz N, editors. Cognitive Aging: A Primer. Psychology Press; Philadelphia, PA: 2000. p. 217. ↵

Contributors and Attributions

• Modification, adaptation, and original content. Authored by : Sonja Ann Miller for Lumen Learning. Provided by : Lumen Learning. License : CC BY: Attribution
• Psyc 200 Lifespan Psychology. Authored by : Laura Overstreet. Located at : http://opencourselibrary.org/econ-201/ . License : CC BY: Attribution
• Image of old couple. Authored by : Ian MacKenzie. Provided by : Flickr. Located at : https://en.Wikipedia.org/wiki/Remarriage#/media/File:Old_couple_in_love.jpg . License : CC BY: Attribution
• The Best Ways to Keep Your Mind Young. Provided by : SciShow Psych. Located at : https://www.youtube.com/watch?v=5DH9lAqNTG0 . License : Other . License Terms : Standard YouTube License