The exploration of factors influencing human behavior often revolves around two contrasting scientific perspectives. On the one hand, some approaches underscore the pivotal role of genetic predispositions, suggesting that innate biological traits predominantly govern behavior. On the other hand, opposing views emphasize the primacy of environmental factors, positing that individual behavior is primarily shaped by external conditions and life experiences (Georgiades et al., 2017; Rangaswami, 2021). Rather than adhering to these polarized perspectives, the SMART COMPASS model establishes this dynamic, reciprocal interplay as its foundational pillar (see Diamond, 2009). This perspective emphasizes the interaction between genetic traits and experiences in shaping human behavior (Diamond, 2009; Lange and Schwarz, 2015).

Thus, integrating nature and nurture offers a comprehensive framework for understanding behavior across the lifespan (see McAuley et al., 2021, discussing the impact of genetic and environmental factors on athlete development; Zi, 2025, regarding genetic and environmental influences on motor milestones). While some of these factors are genetically determined [e.g., apolipoprotein E (APOE)], others, such as physical activity, nutrition, or intellectual engagement, can be modified to promote brain health and mitigate cognitive decline across the lifespan (Reuter-Lorenz and Park, 2014; Phillips, 2017). For example, the ε4 variant of apolipoprotein E (APOE) has been identified as a significant genetic risk factor for Alzheimer’s disease (AD) and a wide range of other neurodegenerative conditions (Windham and Cohen, 2024). Even in such cases of genetic predisposition, early identification of risk factors like APOE ε4 can inform timely and targeted preventive interventions to preserve cognitive performance. The model and the following description place particular emphasis on the role of environmental factors within this interplay, as these factors can be actively shaped. It highlights how life course experiences accumulated over time have the potential to either enhance or diminish neural resources, thus impacting cognitive and motor development. Life course experiences refer to the age-related sequence of roles, opportunities, and constraints that is an individual’s experience across the lifespan; for example, research examines how young people choose personal experiences, interpersonal relationships, and social settings in ways that reflect their past and contribute to their future (Johnson et al., 2011). This foundational understanding sets the stage for subsequent components of the SMART COMPASS model, offering a holistic view of the intricate connections between genetics, environment, neural mechanisms, cognition, and motor behavior.

Various factors throughout the human lifespan shape the enrichment and decline of neural resources (Clemenson et al., 2015). Enrichment occurs through influences that enhance brain structure and function, particularly during childhood and adolescence when the brain exhibits high plasticity (Miguel et al., 2019). During this phase, stimulating environments rich in sensory inputs and opportunities for exploration play a crucial role in optimizing cognitive development. Positive early experiences, such as social interactions, access to educational opportunities, sleep, nutrition, and exercise, are vital for promoting neural development and preparing the brain for future challenges (Schoentgen et al., 2020; Tooley et al., 2021; Schott et al., 2023; Bosseler et al., 2024). Although the brain’s capacity to adapt decreases with age, neuroplasticity persists throughout life (Power and Schlaggar, 2017). Intellectual engagement, physical exercise, and maintaining social connections positively influence cognitive performance in middle and late adulthood (Herzog et al., 2009; Mora, 2013; Reuter-Lorenz and Park, 2024). Furthermore, factors such as higher education levels (Milgram et al., 2006), physical fitness, and bilingualism or multilingualism have been associated with delayed cognitive decline and a reduced risk of dementia (Bialystok et al., 2007; Alladi et al., 2013; Anderson et al., 2020). Mishra and Gazzaley (2014) and Kolb and Muhammad (2014) highlight the potential of targeted cognitive training and enriching life experiences to enhance neuroplasticity, strengthen neural and cognitive functions, and build resilience across the lifespan.

Depletion of neural resources arises from factors that negatively impact brain structure and function. Prenatal factors, including maternal smoking, low birth weight, and stress, as well as postnatal exposure to environmental toxins, can significantly impair motor and cognitive development (Lin et al., 2017; Puga et al., 2024; Nivins et al., 2023; Nolvi et al., 2023). Additional factors, such as poor nutrition, exposure to toxins, or lack of sensory and emotional stimulation, can hinder brain development (Garner et al., 2012; Cusick et al., 2021; Pizzol et al., 2021; Cardenas-Iniguez et al., 2022; Sheridan et al., 2022). In adulthood, lifestyle choices such as smoking, high-fat diet consumption, and sedentary behavior contribute to vascular risks, diabetes, brain atrophy, and cognitive dysfunction (Debette et al., 2011; Lane et al., 2020; Zou et al., 2024). Head trauma, chronic stress, and depression further exacerbate the depletion of neural resources (Lupien et al., 2018).

The SMART COMPASS model highlights the interaction between the brain’s structure, function, and the dynamic processes that support cognitive abilities throughout the lifespan. Significant structural changes occur during early childhood, with primary sensory and motor areas maturing early to enable fundamental abilities such as seeing, hearing, and moving (Huang et al., 2015). In contrast, more complex regions, such as the prefrontal cortex, develop later (Tsujimoto, 2008), undergoing a phase of synaptic overproduction (“wild growth”), followed by pruning. This process strengthens frequently used neural connections while eliminating unused ones, creating a highly efficient and adaptable brain (Sakai, 2020). In later adulthood, structural changes include cortical thinning, reduced white matter, and decreased synaptic connectivity (Fjell and Walhovd, 2010; Hedman et al., 2012). These changes manifest functionally as reduced neural interactions, dysregulated resting states (Persson et al., 2007), and a loss of specialization, especially in the prefrontal cortex (Park et al., 2004; Koen and Rugg, 2019).

The model indicates that life experiences, motor learning, motor skill learning, motor-cognitive dual-task training, and fitness training significantly affect brain structure and function through complex biological mechanisms (Domingos et al., 2021; see also Solis-Urra et al., 2024). Environmental enrichment (e.g., physical activity, intellectual engagement, and social interactions) triggers the release of exerkines and myokines, such as lactate and irisin, along with cytokines like interleukin-6, from muscle and fat tissue. Additionally, growth factors such as brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor (VEGF), and insulin-like growth factor (IGF) are released in the central and peripheral nervous systems. These molecular processes drive neurogenesis, supporting the formation of new neurons and synaptogenesis, facilitating new neural connections. VEGF further promotes angiogenesis, the development of new blood vessels, enhancing cerebral blood flow and oxygen delivery. These structural changes help preserve and enhance the integrity of gray and white matter, maintaining brain adaptability and resilience [an overview of the possible mechanisms is described in Tyndall et al. (2018) and Dupuy et al. (2024)]. On a functional level, these structural adaptations improve neurovascular activity (Tarumi et al., 2025), connectivity within neural networks (Moore et al., 2022), and Default Network Modulation (Reuter-Lorenz and Park, 2024), leading to measurable enhancements in cognitive performance. This is particularly evident in areas such as working memory, cognitive flexibility, and inhibition (Voss et al., 2020; Moore et al., 2022).

These adaptations are not merely biological changes but actively support higher-order cognitive functions such as working memory, attentional control, cognitive flexibility, and inhibition. Aerobic and resistance training, for instance, have been shown to enhance working memory and inhibitory control in older adults (Voelcker-Rehage and Niemann, 2013). They also improve the efficiency of motor representations by strengthening neural networks involved in movement planning, execution, and sensorimotor integration (Goble et al., 2009; Taubert et al., 2011). Increased connectivity between prefrontal, parietal, and motor areas – especially in older adults – supports complex motor coordination and reflects enhanced neural integration (Heuninckx et al., 2008; Ward and Frackowiak, 2003). Such functional connectivity improvements facilitate better top-down control and coordination between executive and motor systems, which is particularly relevant in cognitively demanding motor tasks like dual-task performance (Seidler et al., 2010; Li et al., 2001; Voelcker-Rehage and Niemann, 2013). Together, these findings highlight the essential role of structural and functional plasticity – shaped by molecular processes, life experiences, and physical activity – in preserving and enhancing cognitive-motor performance throughout the lifespan.

Rather than treating cognition as a single, uniform construct, the model highlights the importance of differentiating between distinct cognitive abilities – an especially relevant approach when examining motor performance and learning processes. In their review, Ren et al. (2013) distinguish between the development of (neuronal) representations – often referred to as internal models or schemas – and executive control processes (executive functions), which play a central role in our model.

The functional association between action representations, sensation, perception, feed-forward or feedback control strategies, internal models, and the ability to mentally represent actions is imperative for motor action planning and control (Wolpert et al., 1995). The integration of information regarding the position and velocity of a limb in space, based on sensory feedback and prior experiences, will enable an individual to control their movement more accurately and complete it to achieve a desired outcome. Suppose a child exhibits an inability to plan or execute motor movements effectively. In that case, this will result in a diminished capacity to establish connections between motor movements and other domains of information, such as emotions or cognitive tasks (e.g., mathematics, reading, and problem-solving). This individual will experience an alternative manner in which the body influences cognition, signifying its substantial involvement (through alterations in perception and attention, variances in behavior, or the stimulation of neural motor systems). This entails a disparate impact of embodiment (Eigsti, 2013). It is well established that children exhibit less coordinated movement than adolescents and young adults. Children with motor and/or cognitive impairments [e.g., developmental coordination disorder (DCD), Autism, and Down syndrome] demonstrate increased temporal and spatial variability, reduced anticipatory adjustments, and variations in their capacity to generate and detect information about affordances and invariant structures (Adams et al., 2014; Schott, 2025). As the body and brain undergo a series of physical, physiological, and cognitive changes during childhood and adolescence, it stands to reason that the representation of the self and, by extension, the representation of action and its affordances also change with age (Schott, 2025). Motor imagery (MI) is a widely used experimental paradigm for studying cognitive aspects of action planning, control, and embodied cognition. According to Decety and Grèzes (1999), this phenomenon is characterized as an active cognitive process whereby the representation of a particular action is internally reproduced in working memory, devoid of any overt motor output from a first-person perspective. Many studies have indicated that MI processes are likely present in early childhood. Evidence of this can be seen in the speed-accuracy trade-off in imagined movements (e.g., hand laterality judgment paradigm) of children aged approximately 7 years. However, this relationship is more evident in older children. As children mature, the durations of imagined movement approach actual execution durations (for an overview, see Schott, 2025). Concurrently, the accuracy of MI improves gradually during development (Toussaint et al., 2013). Specifically, 11-year-olds demonstrate significantly higher MI accuracy levels than 7- and 8-year-olds (Toussaint et al., 2013), suggesting that action simulation processes undergo continuous refinement during childhood.

Representations also mirror crystallized intelligence, drawing on accumulated knowledge and experience. In contrast, fluid intelligence, involving problem-solving and logical reasoning, is closely connected to executive functions, as it supports the creation of new representations and promotes adaptive thinking. These cognitive abilities follow different developmental trajectories. Fluid intelligence typically peaks in young adulthood and then gradually declines, whereas crystallized intelligence remains relatively stable or can increase through ongoing knowledge acquisition (Salthouse, 2004; Lindenberger and von Oertzen, 2006; Craik and Bialystok, 2006).

Representations become more complex and precise over time (Strick et al., 2021). Singleton et al. (2021) showed that motor representations in the motor cortex of rats become more complex and precise as they develop, undergo reorganization, and can be modified explicitly through motor learning. Representations include declarative knowledge, such as facts and concepts, and procedural skills, like motor skills. These structures are stored in specialized neural networks in the premotor and parietal cortex and are adapted through repeated use, experience, or motor learning (Seitz et al., 2008). Although the formation of representations slows in early to middle adulthood, most knowledge systems stay robust into late adulthood as long as they are regularly utilized (Watrin et al., 2022).

Executive functions – a common division of executive functions is into cognitive flexibility, inhibitory control, and working memory (Miyake et al., 2000) – likewise follow distinct development patterns. Inhibition and cognitive flexibility emerge early in childhood, although the latter continues to mature into adolescence (Clark et al., 2013). Working memory shows substantial improvements from childhood (Broomell and Bell, 2022; Moriguchi et al., 2016) through adolescence and refines further during early adulthood (Ferguson et al., 2021). Later in life, declines in processing speed, working memory capacity, and cognitive flexibility often lead to slower reaction times and reduced multitasking abilities (Idowu and Szameitat, 2023). Age-related declines in executive functions can make retrieving stored representations more challenging or their formation less efficient, even if they remain intact at the neural level (Shadmehr and Holcomb, 1997; Pedraza et al., 2024).

Crucially, the interaction of representations and executive functions underpins learning and adaptability. While executive processes facilitate the establishment, organization, and use of new knowledge structures, representations stay more resilient when regularly accessed and practiced. This reciprocal relationship underscores the importance of continuous cognitive and physical engagement in meeting evolving life demands and preserving cognitive performance over the long term.

According to the previous description of the model, developmental processes, maturation, and biological aging processes lead to neuronal changes that ultimately influence cognitive abilities. While representations are largely preserved throughout the lifespan, executive functions experience a notable decline in older adulthood. This chapter highlights the intricate interplay between cognitive performance and motor domains, including motor learning potential, performance, and efficiency.

Motor learning potential refers to the individual’s capacity and readiness to acquire, refine, and adapt motor skills in contexts that offer learning opportunities through practice and experience. It has been hypothesized that it may mediate the relationship between static neurocognition and functional outcome (Budoff and Friedman, 1964). Motor performance describes the execution and quality of motor tasks, reflecting skill performance in specific activities. Motor learning efficiency demonstrates the speed and effectiveness of acquiring and retaining motor skills, often influenced by cognitive strategies (implicit or explicit), practice conditions, and feedback mechanisms. In older adulthood, motor learning remains feasible but occurs more slowly and requires greater effort, reflecting a reduction in efficiency (Rodrigue et al., 2005; Voelcker-Rehage, 2008; Yan et al., 2010; Ehsani et al., 2015).

Figure 2A on the progression of motor skill development shows that two individuals learn at the same rate but approach different motor skill levels. Depending on various factors (e.g., achievement motivation and deliberate practice), neither individual may reach the maximum theoretical potential of the motor skill-specific level. However, the individual who develops motor skills toward a higher potential will gain an advantage that a competing individual will never achieve. The main difference between differences in learning rate and differences in potential is that some individuals must overcome additional hurdles to eliminate skill advantages. The advantages based on differences in learning rate are eliminated by continuing to gain experience in deliberate practice. Individuals must change their training activities to eliminate advantages based on differences in motor learning potential rather than repeating them. Because the required changes may be difficult to observe and offer uncertain benefits (e.g., risks or trade-offs), differences in potential add an additional level of difficulty to an otherwise simple task (see also Challenge Point Framework; Guadagnoli and Lee, 2004). The skill development pathways in Figure 2B illustrate how different combinations of motor learning rate and skill development potential can lead to different individual advantages. An individual whose motor skills develop toward a high potential at a high learning rate will enjoy a temporary advantage over individuals who improve toward a high potential at a lower learning rate, and a permanent advantage over individuals who develop their motor skills at a higher learning rate.

The motor learning potential is particularly high during early developmental phases, with critical periods playing a vital role in shaping motor skills. Experiences during these phases have lasting effects, while insufficient motor stimulation can lead to persistent deficits. Although motor learning potential diminishes with age, adults can still develop or restructure motor skills, albeit more slowly and with increased effort (Etnier et al., 2001; Rodrigue et al., 2005; Voelcker-Rehage, 2008). This demonstrates the enduring plasticity of the brain, even in later life. Motor performance follows a clear trajectory: steady improvement from childhood to early adulthood, followed by a gradual decline in later life (see Holfelder and Schott, 2022 for object control skills; Marchesi et al., 2022 for balance-related motor performance). Upper limb motor behavior also shows age-related changes, particularly in tasks involving fine motor control such as writing, grasping, or manipulating small objects. These tasks depend heavily on cognitive resources, especially working memory and executive functions. Consequently, individuals with reduced cognitive capacities – due to developmental disorders or age-related decline – often exhibit lower fine motor performance (Corti et al., 2017; Dahdal et al., 2016; Liou et al., 2020). The relationship between motor skills and cognitive abilities is particularly evident in fine motor tasks (Hoogendam et al., 2014; Corti et al., 2017), with stronger correlations indicating that lower cognitive functioning is typically accompanied by poorer fine motor performance. Stronger correlations are observed in individuals with developmental disorders (Dahdal et al., 2016) or age-related cognitive decline (Liou et al., 2020), but also for object control skills (Holfelder and Schott, 2024). Age-related degeneration in the prefrontal cortex, which is responsible for storing and retrieving motor representations, also impacts motor performance (Seidler et al., 2010).

Based on the work of Ren et al. (2013), the SMART COMPASS model highlights the crucial interplay between knowledge structures (representations) and executive functions in linking cognitive and motor functions. It emphasizes the dynamic and reciprocal relationship between cognitive and motor systems across the lifespan. Motor representations, or mental movement models, are generally acquired through repeated practice and are mainly executed automatically, especially for familiar and well-learned movements. Their basic execution does not depend heavily on executive functions (Yogev-Seligmann et al., 2008). Moreover, motor behavior can also reinforce cognitive functions. For instance, engaging in complex motor activities may stimulate executive control, planning, and attentional resources, particularly in early developmental phases and later adulthood (Voelcker-Rehage and Niemann, 2013; Diamond, 2000). This reciprocal dynamic supports the notion that structured motor skill training can serve as a valuable tool for cognitive enhancement.

Motor representations, or mental models of movement, remain relatively stable, ensuring continuity in motor performance even in older age. In contrast, executive functions, including working memory and cognitive flexibility, decline significantly with age, primarily affecting motor representations’ formation, adaptation, and strategic retrieval when required in novel or complex situations (Kennedy et al., 2008). This decline and age-related degeneration in the prefrontal cortex – a key region for managing executive functions and motor representations – impact cognitive and motor performance.

The concept of scaffolding integrates and extends these mechanisms by emphasizing the active construction, adaptation, and strengthening of neural “Scaffolds” that support both structural and functional changes (compensation vs. enhancement). Scaffolding is a dynamic process incorporating Brain Reserve, Cognitive Reserve, Maintenance, Compensation, and Enhancement elements. In the present article, we approach scaffolding as a comprehensive, lifespan-encompassing mechanism that bridges these distinct concepts and highlights the interplay between structural integrity and functional adaptability. Focusing on the active and ongoing processes of neural reorganization, we emphasize how (developmental) scaffolding contributes to resilience and adaptability across the lifespan and under varying conditions of challenge or pathology. Crucially, we emphasize not only protective and compensatory effects in aging but also the constructive, performance-enhancing potential of scaffolding in early and middle life stages. This includes childhood, adolescence, and young adulthood, where scaffolding supports development and growth under conditions of increased cognitive demand or opportunity (Cabeza et al., 2018; Carranza-Pinedo and Diprossimo, 2025; Reuter-Lorenz and Park, 2014). In relation to Brain Reserve, it contributes to structural resilience by building new neural connections and stabilizing existing ones. This enhances structural capacity and actively supports long-term stability. In early development, scaffolding fosters the establishment and refinement of neural circuits, thereby boosting reserve and enabling higher cognitive capacities (Nyberg et al., 2012; Stern et al., 2020). Like the concept of Cognitive Reserve, scaffolding endorses using alternative networks and strategies. However, it distinguishes itself by focusing on the active development and restructuring of neural connectivity rather than relying solely on previously accumulated experiences (Cabeza et al., 2018). This is particularly relevant in periods of developmental plasticity, where scaffolding helps build and refine neural systems that underpin emerging cognitive abilities.

Additionally, scaffolding complements the goals of Brain Maintenance by promoting the stabilization and preservation of neural networks. This preventive component helps delay age-related degeneration and strengthens structures essential for cognitive functions (Nyberg et al., 2012). This process delays age-related degeneration in older adults and supports the continuous strengthening and optimization of brain function during development and adulthood. From childhood through late life, scaffolding mechanisms help preserve and enhance the efficiency of neural processing, whether by preventing decline or by supporting peak performance in learning, creativity, and problem-solving (Nyberg et al., 2012). In Compensation, scaffolding is key in improving preparedness for functional limitations. Constructing neural scaffolds establishes a foundation for effective compensatory mechanisms, thereby maintaining motor and cognitive performance (Reuter-Lorenz and Park, 2014, 2024). Beyond compensatory functions, scaffolding also enhances cognitive and behavioral flexibility throughout the lifespan. In youth, it facilitates the acquisition of new skills; in adulthood, it supports career and social adaptability; and in older age, it sustains performance despite neurobiological change. Scaffolding is thus an all-encompassing mechanism that unites aspects of Brain Reserve, Cognitive Reserve, Maintenance, Compensation, and Cognitive Enhancement without being confined to any single concept. It reflects both short-term adaptations and long-term development, acting as a core process of neurocognitive resilience and growth across the lifespan. Building functional and structural scaffolds allows the brain to develop and sustain resilience against age- and disease-related changes, while also enabling enhancement, optimization, and growth in earlier life stages. This concept of scaffolding offers a detailed perspective on the complexity of neural adaptation processes throughout the lifespan (Cabeza et al., 2018; Stern et al., 2020; Nyberg et al., 2012).

The SMART COMPASS model highlights that a crucial factor in promoting participation in physical activity or structured fitness training is the consideration of psychological needs and creating conditions that enhance autonomy and self-efficacy (Wulf and Lewthwaite, 2016). In this context, the OPTIMAL theory of motor learning, developed by Wulf and Lewthwaite (2016), describes fundamental mechanisms that take these factors into account and facilitate both motor skill acquisition and performance improvement (see also Wulf et al., 2010). Their theory is based on three principles: autonomy, enhanced expectations, and an external focus of attention, with autonomy and expectancies central to the SMART COMPASS model.

Autonomy refers to the perception of control over one’s learning process. Wulf and Lewthwaite (2016) demonstrated that even small choices, such as deciding the order of exercises or the type of feedback received, can significantly improve motor learning. Autonomy support increases intrinsic motivation, reduces stress, and strengthens self-efficacy. When learners feel a sense of control over their performance, they engage in deeper processing of motor information, leading to improved movement quality and long-term retention of skills. Wulf and Lewthwaite (2016) understand that self-efficacy expectancy refers to a person’s belief in their ability to perform a specific task successfully. This concept is based on Albert Bandura’s notion of self-efficacy but is applied in her work within the context of motor control and motor learning. Wulf and Lewthwaite (2016) emphasize that motor skills can be optimized through physical practice and by strengthening individuals’ belief in their abilities. Their research shows that individuals with confidence in their abilities perform better in motor tasks. This effect can be achieved through positive feedback, social comparisons, or structuring tasks to ensure frequent success experiences. Their research thus highlights the close connection between psychological concepts like self-efficacy, motor learning, and athletic performance.

These elements shape motivation and cognitive engagement during learning, promoting more effective skill development and long-term retention (Wulf et al., 2014; Bacelar et al., 2024). The theory emphasizes the interaction between autonomy support and enhanced expectancies, highlighting how these factors reinforce each other in the learning process. When individuals have a sense of control over their learning, they are more likely to believe in their success, boosting their motivation and strengthening their autonomy (Ghorbani and Bund, 2020). From a practical perspective, coaches, teachers, and trainers should actively support autonomy by offering choices in the learning process and fostering a supportive environment. At the same time, they should enhance learners’ expectations by providing positive and constructive feedback while ensuring they experience frequent successes. This combination improves movement quality and increases motivation and overall enjoyment of the learning experience (Lemos et al., 2017; Ghorbani, 2019).

By incorporating this perspective into the SMART COMPASS model, it becomes evident that motor learning extends beyond mechanical repetition. Psychological factors, such as autonomy and positive performance expectations, are crucial in optimizing skill acquisition and retention. When these principles are applied, educators and trainers can cultivate more effective and engaging learning environments, ultimately leading to improved motor skill development and long-term success.