Interrupted Learning across the Lifespan

Continued learning opportunities provide the foundation for growth and adaptation in a new or changing environment (see Wu et al., 2021). For example, if someone moves to a new country, they need to figure out new banking, medical, and transportation systems. If a particular technological device or platform becomes obsolete (e.g., 3G flip phones), the user has to learn how to use a new compatible device to stay connected with others (e.g., a smartphone). Many learning opportunities for children originate from experiences with formal education, whereas for older adults, learning opportunities tend to be more informal and self-directed or work-related (e.g., Beier et al., 2017).

Interrupted learning (due to catastrophe or planned breaks) is a known, critical issue for childhood education but has yet to be studied in relation to older adulthood. During the COVID-19 pandemic, for example, many students missed an estimated 7 to 10 weeks of learning, due to remote or reduced schooling (Goldhaber et al., 2022). During summer break, there is an intentional period of interrupted learning, where learning loss (also referred to as “summer slide”) has been observed. However, summer slide occurs unevenly across students of different ages, abilities, and resources (Cooper, 2003; Kuhfeld, 2019). The backsliding or stagnation observed in academic abilities during periods of learning interruption, particularly in those of lower socioeconomic status, might account for some of the persistent academic gaps observed between more and less economically disadvantaged students (Cooper, 2003; Cooper et al., 1996). These disparities were clarified and exacerbated during the COVID-19 pandemic, affecting, for example, students’ reading, math, and civics knowledge as assessed in the USA in 2022 (US Department of Education, 2022).

The present perspective piece considers how post-education adulthood could be considered a period in the lifespan of prolonged interrupted learning. We first introduce interrupted learning in childhood and then draw links to adulthood when learning opportunities are often reduced as careers tend to begin while formal education ends. Importantly, we note how cross-talk between cognitive development research on interrupted learning and cognitive aging research could improve lifespan development by focusing on (1) the notion and definition of loss, (2) building skills and function (e.g., online banking) versus training general cognitive abilities, (3) how individual and contextual factors lead to different outcomes, and (4) potential neural underpinnings of interrupted learning across the lifespan.

Academic underachievement in the USA impacts not only a student’s educational trajectory, but also multiple, critical aspects of health and well-being across the lifespan (Hartog & Oosterbeek, 1998; Maguin & Loeber, 1996; Molla et al. 2004; Topitzes et al. 2009). One source of enormous variation between students is how time is spent during periods of interrupted learning and in their academic performance after interruptions. Summer learning loss (also popularized as “summer slide”) from the substantial summer break in schooling in the USA is observed in math and reading skills across upper elementary and middle school grades (Kuhfeld, 2019). Summer learning loss is understudied, and whether summer breaks result in loss has come under question because of measurement approaches (e.g., von Hippel, 2019; von Hippel & Hamrock, 2019). However, other recent work has found that, during summer break, the average student loses 17–34% of the prior year’s learning gains (Atteberry & McEachin, 2021). Furthermore, slippage over summer tends to persist, such that those who lose skill over one summer tend to do so over subsequent summers (Atteberry & McEachin, 2021). As a direct consequence of summer learning loss, numerous programs administer summer interventions to try to stem the slide (Cooper et al., 2000; Kim & Quinn, 2013). Recently, the COVID-19 pandemic caused global learning interruptions for students in the spring of 2020. The pandemic interruption has been related to substantial loss in reading and math achievement across all grades in the USA and thus far an uneven recovery (Fahle et al. 2023; Mazrekaj & De Witte, 2023).

A significant source of variability in outcomes from any learning interruption is also one of the strongest sources of variability in academic achievement: socioeconomic disadvantage. Socioeconomic advantages include multiple related factors (e.g., social status, educational attainment and opportunity, wealth and stability, food security, and accessibility), occur at multiple levels (e.g., family, neighborhood, school, community), and may interact with the developing brain and behavior differently over time (e.g., childhood socioeconomic advantage vs. adulthood socioeconomic advantage; e.g., Hackman and Farah, 2009). Income-related academic achievement gaps start early, before kindergarten, and persist or even widen as a student progresses through school (e.g., Reardon, 2011; Sirin, 2005). One prominent hypothesis about summer learning loss is that socioeconomic differences account for much individual variability in loss and that summer loss directly relates to the persistence of the achievement gap over middle childhood and adolescence (Cooper et al., 1996; Quinn et al., 2016). Some students actually gain knowledge over the summer, presumably related to summer camps and other learning opportunities, and the “resource faucet” not being turned off in summer for wealthier children (Cooper et al., 1996; Entwisle et al., 2000; Quinn et al., 2016). In the USA, wealth is intimately tied to childhood educational opportunities, and there is also relatively little mobility among classes, meaning that children from lower socioeconomic backgrounds in the USA are unlikely to significantly change socioeconomic status over their lifetime (e.g., Isaacs, 2007).

Other important sources of variability in outcomes after learning interruptions may be how children spend their time during academic breaks (e.g., books, screen time, travel, camps, tutoring), the mindsets of parents and children regarding academic breaks (e.g., their views of the purpose of summer break and motivation to engage in different activities), student academic ability prior to interruption (e.g., stronger vs. weaker initial understanding of material), and their general cognitive abilities (e.g., executive function, memory). For example, executive functions form the building blocks that support goal-oriented behaviors and are strongly tied to academic and life success (Best et al., 2011). Better executive functioning has been linked to better reading comprehension, math skills, and overall academic achievement (e.g., Berninger et al. 2017; Wilkey et al., 2020). The slow developmental trajectory of executive functions may make them, along with academic skills, susceptible to learning interruptions, socioeconomic disadvantages, and other school-related effects (Church et al., 2023; Hackman et al., 2010; Tervo-Clemmens et al. 2023).

There is no extant work on which brain systems are related to summer learning change, and limited work on how summer activities or executive functions impact academic skill retention over time or interruption. It is also unknown whether academic changes from learning interruptions relate more to domain-general or task-specific brain processes. Reading and math task performance result in engagement of a few task-specific brain regions but also overlap in engagement of multiple domain-general brain systems (e.g., control, default brain networks, Peters & De Smedt, 2018; Nugiel et al., 2024; Yeo et al., 2017; Roe et al., 2018). Despite the current lack of understanding, predictions can be made about how multiple brain networks important for reading and/or math performance could be impacted, given a strong body of literature tying academic skills to brain engagement more broadly. Those who struggle with reading, for example, tend to under-recruit multiple brain regions important for reading, while also having relatively higher engagement in frontal control-related regions (Hoeft et al., 2011; Roe et al., 2018). Regardless of socioeconomic status, children raised in cognitively stimulating homes were found to have thicker cortex and higher BOLD signals in executive function-related regions, which related to higher academic achievement (Rosen et al., 2018). Cognitive stimulation in this study included books, out-of-school activities, and home math practice, all of which could relate to variability in achievement gain over learning interruptions as well. Thus, while we might predict that interruption-related academic changes are predicted by different initial engagement or changes in engagement of academic and control-related brain regions, much work remains to be done to understand the effects of learning interruptions on the developing brain.

As we leave formal education in adolescence or young adulthood, perhaps the rest of adulthood could be considered a period in the lifespan of prolonged interrupted learning. For many, adulthood contains years, or even decades, of reduced or non-existent learning opportunities, as opposed to the weeks or months of a break from school, or 3 disrupted pandemic years in childhood. For example, retired older adults do not have a return to the workforce and routine in the same way that children seasonally return to school. Perhaps this extended “break” may lead to accelerated cognitive deficits and neural degradation in older adults (e.g., Rohwedder & Willis, 2010). The findings on interrupted learning in childhood may be useful for drawing links to the decline in learning opportunities and possible loss in skills in adulthood, especially older adulthood.

After the last year of formal education, novel learning opportunities are generally limited to informal, self-directed learning, such as with leisure activities and/or formal or informal workplace trainings (e.g., Beier et al., 2017). Learning in adulthood often requires the learner to garner resources (e.g., time, money, motivation) and create learning opportunities for themselves, similar to summer periods during childhood. Workplace trainings may offer more formal learning opportunities but often focus only on workers learning a specific task required for a particular job and aim to accomplish this in the fastest way possible to maximize a worker’s productivity. Workers can find learning opportunities outside of work, but these may not align with salary or other work-related incentives. Employers agree that training employees on new skills is a top 10 or even top 5 priority (Illanes et al., 2018), but many workplaces do not have the time or resources to provide such training and, as a result, could hire new already-trained employees. Some professions require extended schooling/training, such as medical and academic professions, and therefore may reduce the total period of prolonged interrupted learning experienced by adults in those fields (e.g., Boom-Saad et al., 2008; Christensen & Henderson, 1991). These professions are typically tied to higher education level, higher pay, better healthcare, and higher occupational complexity, all of which also relate to better cognitive functioning in old age (e.g., Luo and Waite, 2005; Smart et al., 2014).

The overall decreased learning opportunities in adulthood may be at least partially responsible for observed cognitive trajectories in adulthood that show a downturn about a decade or so after the last years of formal education for some cognitive domains, such as processing speed (Hartshorne & Germaine, 2015; Park & Reuter-Lorenz, 2009; Salthouse, 2009). Cross-sectional and longitudinal data differ in terms of pinpointing when cognitive abilities start declining, with cross-sectional data pointing to an earlier start and a linear decline but longitudinal data showing a later start with a quadratic decay function. Considering cross-sectional and longitudinal studies together, the earliest downward trends have been observed around 20–30 years of age for some cognitive domains and around 40–50 years of age for other domains (Hartshorne & Germaine, 2015; Salthouse, 2009).

Similar to variation in academic ability in childhood, downward trends in cognitive abilities in adulthood also depend on many factors, including occupation complexity, healthcare access and quality, socioeconomic status, race/ethnicity, among others. A sizable literature documents a variety of factors that impact the start and the shape of decline trajectories (e.g., Finkel et al., 2009; Han et al., 2023; Reuter-Lorenz & Park, 2014; Wu et al., 2020). Although we only focus on educational cessation as a source of decline in this article, we acknowledge the literature showing that individuals who follow unhealthy lifestyles (e.g., lack physical exercise, smoke; Dhana et al., 2020; Kelly et al., 2014; Yu et al., 2020; Wajman et al., 2018), have poor health conditions (e.g., diabetes, hypertension, obesity; Ganguli et al., 2020; Leritz el at., 2011; Wysocki et al., 2012), and carry the APOE ε4 allele (Chen et al., 2021) may experience a faster decline in cognitive functioning during older adulthood than those who do not. There are also pronounced differences in cognitive aging based on race/ethnicity as studies have demonstrated that Black and Hispanic/Latino older adult groups perform worse in cognitive assessments and face steeper declines as they age in contrast to White individuals (Lee et al., 2012; Quiñones et al., 2022; Sachs-Ericsson & Blazer, 2005; Zahodne et al., 2021). Overall, Black and Hispanic/Latino individuals suffer from higher rates of Alzheimer’s disease and related dementias (Alzheimer’s Association, 2020). Worse cognitive outcomes among these racial/ethnic groups are fueled by a build-up of a lifetime of adversities/risk factors (e.g., discrimination, socioeconomic disadvantage, financial stress, poor access to healthcare; Forrester et al., 2019) that are more commonly present in these populations as well as a lack of protective resources, one of which is higher educational attainment (e.g., Sachs-Ericsson & Blazer, 2005).

Educational attainment in particular has been given a lot of attention in the literature as it is a known contributing factor that shapes cognitive outcomes in later life. In general, low levels of education have been linked to greater cognitive decline in older adulthood (Bosma et al., 2003; Schmand et al., 1997). Educational attainment seems to be a salient factor in protecting individuals against cognitive decline and impairment, potentially as a result of building one’s cognitive reserve earlier in life (Arenaza-Urquijo et al., 2017; Chen et al., 2019; Clouston et al., 2020; Fletcher et al., 2021; Lövdén et al., 2020; Schneeweis et al., 2014; Walsemann & Ailshire, 2020, although see Berggren et al., 2018). Perhaps cognitive decline observed in many older adults is partially attributed to the long-term disengagement of learning opportunities. Formal education is primarily focused on and encouraged for younger individuals. Traditionally, college campuses tend to be populated by younger adults (ages 18–24) across the USA (National Center for Education Statistics, 2023; Hanson, 2024). There is great emphasis on going to school during the earlier years of life and opportunities for formal education tend to decrease or stop altogether after one has achieved the degree desired or required for employment. Overall, there are discrepancies in formal learning opportunities between young and older individuals, due to the societal belief that there are specific life stages for obtaining formal education and learning new skills (e.g., see Sheffler et al., 2022). Therefore, it is difficult to understand how many adults, especially older adults, can benefit from the intense learning environment that younger learners from childhood to young adulthood experience (Lövdén et al., 2020; Wu et al., 2017).

It is important to note that we are not positing that formal education is the most important way for cognitive enrichment and learning, but rather highlighting that the rich learning opportunities afforded to, and prioritized for, younger learners decrease at the cessation of formal education. The impact of lower education levels on cognitive abilities has been well documented. The cognitive performance of students who stay in high school through senior year is better than that of those who are high school dropouts (Alexander et al., 1985). Moreover, a study showed that adolescents who drop out of school have lower verbal cognitive ability in contrast to high school graduates, and this effect upholds long term into adulthood (Vaughn et al., 2011). Even a few months of reduced learning opportunities over the summer triggers a decrease in some abilities and function in children as noted earlier. Evidently, when children and adolescents are provided with decreased learning opportunities, they also decline until learning opportunities are increased. Thus, we propose that decreased learning opportunities may be an important contributor to age-related cognitive decline starting in mid-life.

On the flip side, providing rich learning environments to older adults, especially older adults with few resources, may encourage new skill learning, thereby increasing both function and cognitive abilities over the long term (Ferguson et al., 2023; Leanos et al., 2023; Park et al., 2014). It has been proposed that these environments should include six aspects beneficial to learners across the lifespan (Wu et al., 2017): (1) Open-minded learning (adaptive, input-driven learning rather than relying on habit), (2) individualized scaffolding (optimal tailored learning instruction rather than no or suboptimal instruction), (3) growth mindset (believing in one’s ability to increase abilities with effort as opposed to believing that one does not have inborn talent to learn something), (4) a forgiving environment (ability to make mistakes and avoid negative learning/cognitive stereotypes rather than requiring perfection at the risk of ridicule), (5) a serious commitment to learning (persisting with new learning for a long period rather than quitting after a short period), and (6) learning multiple skills simultaneously. Leanos et al. (2023) provided such an environment in a university classroom setting to older adults for 3 months as they learned at least three new skills simultaneously (e.g., photography, Spanish, how to use an iPad). As a result, older adults’ cognitive abilities increased on average to baseline levels of middle-aged adults (30 years younger) by the end of the 3-month intervention and continued to increase to baseline levels of younger adults (50 years younger) 1 year after the end of the intervention (Ferguson et al., 2023).

Even when learning is interrupted as a result of completing formal education, lacking higher educational opportunities, or not learning new skills through one’s occupation, there are other ways in which learning can be reintegrated in later life that benefit adults. Many organizations provide in-person or online learning programs for older adults. For example, California State University (East Bay) provides an opportunity for older adults past the age of 60 to pursue any degree of interest with practically all fees waived (Institute on Aging, 2016). Additionally, SeniorNet offers older adults online learning through a network of community centers that encourage access to technology and continued learning in a variety of courses (i.e., genealogy, Greek, computer skills; Githens, 2007). Overall, lifelong learning also provides societal benefits beyond the individual, such as better employment opportunities, preserved social cohesion, enhancement in productivity, all of which can help bridge disparities in educational outcomes, occupational attainment, and overall health (Zmas & Sipitanou, 2009).

Increasing novel learning opportunities extends current efforts that promote cognitive engagement and cognitive training more generally. However, the extant literature on cognitive engagement and cognitive training interventions often focuses on engaging in cognitively challenging activities in order to “stay active” to maintain or improve cognitive abilities. The approach we take focuses on learning novel skills to adapt to personal and environmental changes and needs (e.g., Nguyen et al., 2020), with benefits in cognitive abilities as secondary. In the next section, we note important considerations for linking interrupted learning research with children and cognitive aging research with older adults, which align with our approach of highlighting the importance of focusing on skills and function.

Research findings focused on understanding and testing how to address interrupted learning from childhood to young adulthood could be applied to the long spans of interrupted learning during middle-aged and older adulthood, especially after retirement. In particular, there are several key considerations of interrupted learning: assumptions behind measuring loss; a focus on functions and skills versus general cognitive abilities; the importance of understanding the multitude of individual and contextual factors that may relate to differential effects of learning interruptions; potential neural underpinnings of interrupted learning across the lifespan.

The literature on interrupted learning during childhood does not often promote the term “learning loss.” Using the term “loss” focuses on a deficit perspective that marginalizes underserved students and prompts questions regarding what is actually lost and how learning is measured (e.g., Diaz & Muñoz, 2021). Avoidance of this term is related to the central premise that children and adolescents should be on a growth trajectory of cognition and academic abilities toward adulthood that ideally centers equity in education and opportunities for all students. Indeed, prior research has criticized the use of the term “loss” in relation to summer break as the academic gaps between advantaged and disadvantaged students may begin much earlier than when formal education begins, and disadvantaged students may merely be maintaining instead of declining compared to peers during learning interruptions (e.g., von Hippel, 2019). Pandemic-related school interruptions led to broad declines in reading and math test scores across the USA (The White House, 2023). These declines are now improving, but improvement has been uneven across districts, often replicating or enhancing the pre-pandemic wealth-achievement gap (Dewey et al., 2024).

Compared with children, older adults are more consistently thought to be on a declining cognitive trajectory that can only at best be delayed (e.g., a “use it or lose it,” Hultsch et al., 1999; Shors et al., 2012). Studies that investigate neural changes in aging have warranted evidence of “use it or lose it” based on data demonstrating neuronal loss (e.g., Shors et al., 2012), similar to observed neural changes in children (e.g., deaf children who do and do not receive cochlear implants, Gordon et al., 2011). However, studies that focus on engaging in cognitively stimulating activities to maintain or improve cognitive abilities also have used the term “use it or lose it” (e.g., Hultsch et al., 1999). The use of the term implies that not engaging in such activities leads to declines in cognitive abilities. These studies observe faster and larger rates of cognitive decline for older adults who do not engage in as many stimulating activities as other older adults. Going beyond this deficit perspective of “use it or lose it” (Stine-Morrow & Manavbasi, 2022) allows cognitive aging research to adapt more of a growth mindset focused on thriving, while also accounting for the inherent heterogeneity in older adults, especially healthy older adults who have not declined in many areas. Unfortunately, the deficit perspective of “use it or lose it” is still standard for many older adults and promoted by community stakeholders (e.g., AARP). Adopting assumptions of growth and thriving for older adults may enhance or even fundamentally change some important theoretical frameworks and approaches. For example, stagnation (“maintenance”) or absence of learning in the face of others’ growth during childhood can be a cause for concern. However, cognitive maintenance and brain maintenance is considered a positive goal in older adulthood (Cabeza et al., 2018; Nyberg et al., 2012), as if loss is assumed to be the norm. Perhaps assuming loss with age and focusing on maintenance has limited much of cognitive aging research to focus on stagnation rather than on a potential for later-life growth and thriving.

When members of the general public think about the cognitive changes seen in childhood, they typically focus on growth of specific functions and gains in particular skills (e.g., math, reading, motor). General cognitive abilities (e.g., processing speed, memory) are often considered contributing factors to the rate or ease with which these new skills are learned but not necessarily as factors that can change. Instructional focus is similarly on growth in skills (e.g., phonics, fluency, vocabulary for reading) rather than general cognitive abilities. Researchers in child development have focused substantially on what drives the improvement of academic or other skills but have also studied the significant growth in some aspects of general cognitive abilities (e.g., executive functioning, memory, emotional regulation) over development (Bevandić et al., 2024; Elsayed et al., 2023; Porter et al., 2023).

By contrast, cognitive aging research has often focused on how to maintain or improve more general abilities related to attention and memory via cognitive engagement, cognitive training, leisure activities, new strategies, and exercise (e.g., Hertzog et al., 2008). Compared to the handful of studies conducted on novel real-world skill learning with healthy older adults, there are hundreds of studies conducted on training general cognitive abilities (Lampit et al., 2014; Simons et al., 2016). Moreover, novel skill learning studies with older adults tend to have cognitive abilities as the primary outcome measure, suggesting that the purpose of novel skill learning in older adulthood is to increase, or at least maintain, cognitive abilities. Older adult research only tends to focus on skills once particular functions start declining, such as research on independent activities of daily living (Millán-Calenti et al., 2010) in clinical populations (e.g., patients with mild cognitive impairment, dementia, Alzheimer’s disease, or schizophrenia). In fact, the decline of basic functional skills is often an important, or even first, measure for clinical populations.

For healthy older adults with limited time and/or resources, encouraging continuous learning of novel skills that facilitate daily activities (e.g., learning to use a new smartphone to access rideshare services, learning to speak a new language after moving to a new country) may be more advantageous over the long term compared with improving domain-general cognitive abilities but lacking skills to function in daily activities. With personal changes (e.g., moving out of state to be closer to other family members, learning how to walk again after breaking a hip) and societal changes (e.g., physical distancing due to a pandemic, increased adoption of online platforms to cut costs), it is necessary to adapt continuously to the changes in daily life. For example, focusing on technological skills in a technologically oriented world could be very useful for adults (e.g., learning new online banking or telehealth platforms during the pandemic, Charness & Boot, 2022). This idea aligns with current calls to focus on functional abilities in older adults (e.g., World Health Organization, 2021), which highlights the importance of maintaining functional abilities so that older adults can continue to be and do what they value. Maintaining functional abilities in new and changing circumstances and environments often requires adaptation via learning novel skills, such as learning to use a new smartphone to order new medication, and accessing rideshare services after losing a license to pick up new medication.

One important implication of shifting the focus in cognitive aging research from general cognitive abilities to functional skills is that cognitive interventions for older adults would then have long-term, real-world functional gains as the primary outcome measures. Consistent with our proposal, giving older adults opportunities to learn and train has shown to be productive. Prior cognitive interventions with older adults have shown not only that it is possible to increase cognitive functioning (e.g., Kueider et al., 2012; Lampit, 2014), but also that training multiple aspects and/or domains simultaneously (e.g., physical and cognitive training, or working memory, attention, and processing speed) can produce greater and better maintained effects (Cheng et al., 2012; Gavelin et al., 2021; Schmiedek et al., 2010), especially in terms of far transfer (i.e., increasing untrained abilities, Gajewski et al., 2020; Tagliabue et al., 2018). Booster training sessions (i.e., short training sessions after the end of the intervention) also can help sustain the cognitive benefits from interventions (e.g., Rebok et al. 2014). Overall, prior cognitive interventions have shown that learning in a multi-domain training context not only can improve cognitive abilities in older adulthood, but also effects can be maximized with booster sessions. Future research can build on the success of these prior cognitive training interventions, which have shown that cognitive improvements are possible, by focusing on functional skills as a multi-domain way to address interrupted learning in adulthood. In light of the research indicating narrow to non-existing transfer of trained cognitive abilities to untrained cognitive abilities or to real-world skills, future interventions could investigate whether focusing on functional skills, while training targeted cognitive abilities could tailor and optimize cognitive and functional outcomes for older adults.

Focusing on functional skills in healthy older adults has several advantages for the literature, as well as society. First, more studies can address current real-world issues, such as the need to learn new skills related to work to remain employed or addressing the digital divide (Charness & Boot, 2022), especially for underserved communities. Second, theoretical models of cognitive aging can be extended or even amended to address how a suboptimal learning environment may lead to aspects of cognitive decline over time and explain aspects of heterogeneity in aging based on differential learning environments and resources available. This shift would support more inclusive theories of aging, while promoting the development of more effective and useful interventions in the real world.

If novel skill learning is promoted in adulthood, especially older adulthood, what might upward learning trajectories look like beyond formal education? What community and societal structures could help prevent stagnation? Future research could test potential growth trajectories during middle-aged and older adulthood via novel skill learning in supportive environments that do not interrupt or deprioritize new learning.

The backsliding or stagnation observed in academic abilities during summer breaks, particularly in those of lower socioeconomic status, might account for some of the widening academic gap observed between more and less economically disadvantaged students (Kuhfeld & McEachin, 2021; Reardon, 2011). But as discussed earlier, many potential factors influence the impact of and outcomes from learning interruptions in childhood. In a similar vein, we need to know more about who is affected by interrupted learning in adulthood, especially older adulthood.

As previously mentioned, educational attainment is associated with cognitive outcomes in later life, such those with higher education have better cognitive performance than those with lower education. However, inequalities in access to educational opportunities lead to some groups in the USA experiencing earlier cessations of formal education than other groups and, thus, are deprived of reaping the benefits of higher education. For example, on average, Hispanic and non-Hispanic Black individuals have lower levels of education compared with non-Hispanic Whites (Duffin, 2021). The US Census Bureau of 2021 reported that 41.9% of non-Hispanic White individuals had a college degree at the time, compared to 28.1% of Black individuals and 20.6% of Hispanic individuals (US Census Bureau, 2022). These educational attainment differences negatively affecting Hispanics and Blacks have been largely tied to structural inequities ranging from disparities in wealth and housing to many segregational practices (Fuligni, 2006; Goldsmith, 2009; Mayer, 2002). Racial and ethnic disparities in health remain prominent as a reflection of broader socioeconomic disadvantages (Williams et al., 2016). The impact of early disparities in educational opportunities due to socioeconomic standing reflects the later observed disparities in cognitive abilities based on educational attainment.

The inequalities in learning opportunities experienced by racial/ethnic minorities and those with socioeconomic disadvantage are not limited to formal education. Indeed, while some adults have opportunities to engage in learning or performing skills that are cognitively stimulating via their jobs, racial/ethnic minorities are less likely to hold occupational positions that are deemed as cognitively complex (e.g., Sonnega & Andrasfay, 2023). The types of jobs performed by racial and ethnic minorities tend to be highly unionized, requiring lower skills, routine-based work, with less than optimal workplace environments (Alonso-Villar et al., 2012). For example, Black individuals work at higher rates in jobs such as machine operators in laundry rooms and garbage collectors while their White counterparts are disproportionately registered as nurses and pilots (Kaufman, 2010). Moreover, ethnic and racial minorities tend to work more physically demanding jobs in comparison to White older workers (Bucknor & Baker, 2016). Therefore, in combination with other aforementioned factors, lower educational and occupational learning opportunities among racial/ethnic minorities across their lifespan may play an important role in cognitive disparities observed in the USA (Sheftel et al., 2023; Sisco et al., 2015). Their higher risk for developing Alzheimer’s disease and related dementias than majority groups could be a consequence in part due to this higher level of interrupted learning that they experience.

Beyond formal education, opportunities for continued learning in adulthood, especially older adulthood, are beneficial, as noted earlier. However, there are important barriers to learning engagement that some individuals face, especially those who also had limited educational opportunities earlier in life. For example, participation in lifelong learning programs is largely limited by work obligations, family responsibilities, limited free time, and lack of proper information to engage in such activities (Zmas & Sipitanou, 2009). Racial/ethnic minorities often do not get the privilege to retire as they face the need to maintain financial support for their family for greater years than their White counterparts (Francis & Weller, 2021a, 2021b). A qualitative study conducted with low-income Latino older adults also found that lacking good health, financial resources, awareness of opportunities, proficiency in the English language, transportation, and motivation are all barriers to learning engagement (Rodriguez & Wu, 2024). Additionally, older adults that have lower literacy skills and educational qualifications, have a low income or are unemployed, and present low skill levels have fewer opportunities of participating in lifelong learning programs (Zmas & Sipitanou, 2009). By contrast, the individuals that reap the most benefits from such opportunities are highly educated, skilled, younger, and employed by established businesses. Adult learning opportunities can be dependent on socioeconomic background as well as previously acquired education, making access to lifelong learning programs inequitable. As a variety of learning barriers could be present in adulthood, existing and future learning programs should tailor their services to address the barriers experienced by individuals, with an emphasis on marginalized groups (Rodriguez et al., 2023). Possible areas of focus when designing learning opportunities include underserved populations who have specific barriers and needs, such as learning new skills to remain in the workforce, even as an older adult because they do not have the luxury to retire.

It is important to note that the ideas presented in this article draw heavily from research conducted with children and adults in the USA across the world, populations in different nations have different beliefs, resources, and regulations regarding learning opportunities in older adulthood. For some, there could be nationwide efforts to promote lifelong learning via reduced or free opportunities (e.g., free college tuition for adult learners of all ages), while others may restrict federally sponsored learning opportunities to younger learners. Countries also have different mandatory retirement ages (e.g., 65 years in Germany), while some countries do not have mandatory retirement (e.g., USA). Across countries, there are also different regulations for children in terms of length of summer breaks (e.g., 4–13 weeks) and resources available during these breaks (NCEE, 2018). Future research can explore how facilitated or interrupted learning opportunities across the lifespan, especially in adulthood, in diverse populations may promote or hinder adaptation and overall cognitive health (see Wu et al., 2021).

It is now clear that neural plasticity (e.g., neuronal growth and reorganization) occurs throughout the lifespan, much of which is driven by environmental influences, such as the experience of everyday activities, and the learning of new skills and information (e.g., Gutchess, 2014; Kramer et al., 2004; Pauwels et al., 2018). Although there is overlap in brain structures that are critical to skill learning in both children and adults, little research has been done on how these areas may be affected by interrupted learning across the lifespan. The proposed extended period of interrupted learning during adulthood may be supported by these brain measures in addition to behavioral factors. Correlations between brain factors and learning outcomes demonstrated in children may be present when investigated in middle-age and older adulthood. For example, white matter microstructure and functional connectivity have been shown to be associated with learning in children, such as predicting future reading ability and performance on math skills (Hoeft et al., 2011; van Eimeren et al., 2008). Similar correlations have been made for various skill learning in adulthood (Antonenko et al., 2012; Schlegel et al., 2012). Exploring these connections through interventions across the lifespan will provide valuable insights into the mechanisms of interrupted learning.

Further connections can be established between children and adults based on the level of cognitive stimulation in different environments and how this subsequently influences brain health, particularly cortical thickness. Cortical thickness in older adults has been demonstrated to correlate with skill learning and executive function (Burzynska et al., 2012; Engvig et al., 2010; Worschech et al., 2022), but a link to the degree of cognitive stimulation in current or past environments (i.e., workplace and home environments) has yet to be thoroughly investigated. We can theorize that, given the prevalence of evidence concerning this correlation in children and adolescents, a similar association may exist for older adults. Within the framework of interrupted learning during adulthood, it is important to consider the degree of cognitive stimulation in the workplace, or lack thereof, as a significant factor. Although many adults are employed in cognitively demanding jobs, learning experiences in these settings often target specific areas of interest rather than broader skill sets. Exploring measures of cortical thickness, both globally and regionally, may deliver helpful insights into this phenomenon.

Connecting research on interrupted learning early in the lifespan with research on cognitive aging later in the lifespan may be useful for developing a better understanding of the impact of interrupted learning across the lifespan. Doing so may shift the focus of cognitive aging research from mere maintenance or small, short-term increases, to long-term thriving via more continuous adaptation in new and changing environments. Considering interrupted learning as a lens for assessing access to learning across the lifespan may be important for formalizing and supporting efforts to mitigate or even prevent premature cognitive and functional decline in healthy older adults. Addressing interrupted learning for all learners may allow us to achieve much more than current expectations across the lifespan.

No data were included in this article.

The authors have no conflicts to report.

Preparation of this article was funded in part by an NSF CAREER Award to RW (BCS-1848026) and by the Johns Hopkins Alzheimer’s Disease Resource Center for Minority Aging Research (1P30AG059298).

R.W. and J.A.C. came up with the idea, and R.W., I.F.L.Q., T.M.R., B.P.T., and J.A.C. contributed to writing and editing the manuscript.

All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.