Introduction

The intricate networks of brain cells, in conjunction with the body and the environment, process vast amounts of information, giving rise to various cognitive functions such as attention, memory, language, and reasoning (Attneave et al., 1950; Buzsáki, 2009; Milner et al., 1998). Given the importance of cognitive functions in our daily lives and considering the increase in cognitive impairments worldwide (Auerbach et al., 2018; Nichols et al., 2022), a recent focus has been on enhancing cognitive functions. Although some cognitive enhancement techniques show promise, overall effectiveness is unclear (Colzato et al., 2021; Gobet & Sala, 2023; von Bastian et al., 2022).

Cognitive enhancement programs have focused on cognitive control, including executive functions such as inhibition, working memory, and cognitive flexibility (Diamond, 2013; Farah, 2015). These are highly developed in humans. They play a crucial role in higher-level social-cognitive processes and mental health (Bombonato et al., 2024; Diamond, 2013; Menon & D’Esposito, 2022). Consequently, executive control dysfunction is a significant source of disability in many neurological conditions (Hildebrandt, 2018), including learning disorders (Borella et al., 2010), Attention Deficit Hyperactivity Disorder (ADHD, Ramos et al., 2020), addiction (Baler & Volkow, 2006), and depression (Taylor Tavares et al., 2007). Cognitive control enhancement techniques such as computer-based cognitive training, neurostimulation, and aerobic exercise have become important (Bostrom & Sandberg, 2009; Racine et al., 2021; Yamada et al., 2021), but their effectiveness is still limited (Colzato et al., 2021; Gobet & Sala, 2023; Shawn Green et al., 2019; Shipstead et al., 2012).

Beyond immediate neuroscience insights focusing on how the brain works, I argue that understanding the evolutionary origins of mental functions is essential for developing more effective cognitive enhancement approaches. Researchers can get valuable insights into the functional significance of cognitive processes and develop targeted interventions and treatments by acknowledging the evolved physiological adaptations, as informed by the theory of evolution (Darwin, 1859).

The theory of evolution has been the most effective approach for understanding biological processes and demonstrating our universal human nature, influencing substantially many disciplines, including psychology (Buss, 1995; Tooby & Cosmides, 2005; Zagaria et al., 2020). Following the paradigm shift in psychology in the 1950s and the birth of cognitive science (G. A. Miller, 2003), a new wave of cognitive revolution has emerged, with a burgeoning interest in applying evolutionary theory to study adaptive cognitive systems (Buss, 2019; Cosmides & Tooby, 2013; Cosmides et al., 2018; Sznycer et al., 2021). In addition, evolutionary-based approaches in anthropology, neuroscience, and medicine have been developed to understand the mechanisms underpinning mental disorders (Benton et al., 2021; Fox, 2018; M. A. Gibson & Lawson, 2015; Gilbert, 2019; Giosan et al., 2020; Hayes et al., 2020; Hayes & Sanford, 2015; D. E. Lieberman, 2015; Raichlen & Alexander, 2017; D. J. Stein, 2019). Despite the accumulating evidence supporting this paradigm shift, the study of cognitive enhancement has not yet adequately used the potential of an evolutionary perspective to inform and improve its strategies and approaches.

Various neurobiological mechanisms were primarily forged by natural selection over the evolutionary past to solve adaptive problems—that is, recurrent challenges associated with survival and reproductive success requiring adaptation, such as food acquisition, sexual strategies, communication and coordination, dominance conflict, and so on (Buss, 2019; Cosmides & Tooby, 1995, 2013); indeed, physiological mechanisms underlying cognitive processes evolved as specialized solutions or adaptations tailored to solve specific fitness-related problems. From this evolutionary perspective, this article views a broad spectrum of cognitive processes as adaptive problem-solving mechanisms that enable individuals (or groups) to navigate, shape, and adapt to their social and physical environments (Al-Shawaf et al., 2016; Frijda, 1986, 2017; Tooby & Cosmides, 1990a, 2008).

From this view, a newborn’s brain is not a blank slate capable of composing cognitive algorithms solely by experience; instead, human babies are born with intricate rich-content mechanisms that can be combined during development to tackle adaptive problems (Cosmides & Tooby, 2013; D. E. Lieberman, 2015; Nowak et al., 2010; Pinker, 2002; Raichlen & Alexander, 2017). Infants possess various neurobiological predispositions crucial for survival, such as seeking food and avoiding poison (Beauchamp & Mennella, 2009; Liberman et al., 2016; Włodarczyk et al., 2018). Moreover, neonatal cognitive mechanisms have an innate capacity to understand language and also social interactions such as imitation and cooperation (Elsner & Wertz, 2019; Magielse et al., 2023; Olson et al., 2023; Van Overwalle et al., 2020). These adaptive cognitive mechanisms provide a foundation for cognitive development, influencing how knowledge is acquired, and new executive programs develop in interaction with the body and the environment (Amore et al., 2021; Curley & Champagne, 2023; Diamond, 2000; Heineman et al., 2018; Piek et al., 2008; Pierce et al., 2023; Timmons et al., 2012).

During phylogeny, adaptive cognitive mechanisms, including emotional systems such as anger, disgust, fear, joy, sadness, and surprise, were shaped in response to adaptive problems, allowing fast prediction of potential threats and opportunities (Al-Shawaf et al., 2016; Cosmides & Tooby, 2000; Damasio & Carvalho, 2013; Frijda, 2016; Malezieux et al., 2023; Nesse & Ellsworth, 2009; Sznycer et al., 2021; Tybur et al., 2013; Wallon, 1972). These evolutionary-conserved mechanisms are fine-tuned to specific problems. For instance, encountering a snake triggers an immediate fear response, a crucial adaptation for survival. However, these fast operational processes struggle with ambiguous problems or conflicting conditions with uncertain outcomes, such as encountering a snake resembling a stick or a mushroom that appears nutritious but toxic.

Fast automatic cognitive processes must be controlled and regulated in many physical and social circumstances to enhance long-term survival prospects. For example, while an available opportunity (e.g., food) may trigger an automatic desire to indulge, we must control and postpone this immediate temptation in certain situations to achieve long-term goals or more-valued rewards later (e.g., group support). To deal with these conflicting or uncertain conditions, in parallel and closely intertwined with fast operational systems, cognitive control capabilities evolved (Ardila, 2008; Chin et al., 2023; McNaughton & Vann, 2022; Shine, 2021). Cognitive control processes—including the abilities to inhibit impulsive reactions, hold and work with information in mind, and switch between tasks—have played a pivotal role in developing higher-level cognitive abilities such as reasoning, planning, and creativity (Diamond, 2013; Kane & Engle, 2002; Titz & Karbach, 2014). These processes enhanced the flexible capacity to address physical and social challenges, particularly in unfamiliar and conflicting situations (Ardila, 2008; Chin et al., 2023; Haidle, 2010; Koziol et al., 2012; Levy, 2024; Malaei et al., 2020; Pessoa, 2022).

Solving most adaptive problems in our ancestral environments required precise synchronizations between cognitive processes and diverse types of whole-body physical activity (WBPA, Bramble & Lieberman, 2004; D. E. Lieberman, 2015; Raichlen & Alexander, 2017; Wallace et al., 2018). This crucial demand for coordination between brain and body sculpted interconnected cognitive and motor (CM) mechanisms with specialized problem-solving capacities that can be activated effectively in specific conditions (Chin et al., 2023; Dere et al., 2019; Koziol et al., 2012, 2014; Kreibig, 2010; Mendoza & Merchant, 2014; Raichlen & Gordon, 2011; Richardson et al., 2008; Sherman & Usrey, 2021; Werner et al., 2019). For example, facing a threatening animal in a jungle can prompt CM systems, involving a combination of cognitive and physical processes, such as rapidly assessing the situation and executing physical actions like climbing a tree or throwing objects. Notably, these interactions with ancestral environments and attempts to solve adaptive problems served as the building blocks for cognitive processes, as emphasized by a bottom-up embodied approach to cognition (Ianì, 2019; Koziol et al., 2012; Newen et al., 2018; Pfeifer et al., 2014). Based on this understanding, highlighting the evolutionary-based links between cognitive processes, WBPAs, and ancestral problems/environments can provide crucial information to develop a more practical approach to stimulate cognition.

The main objective of this article is to propose a theoretical framework for evolutionarily inspired cognitive enhancement, highlighting the critical importance of stimulating physiological adaptations. The article begins by addressing evolutionary pressures that shaped synchronization between brain and body development, illustrating how our cognitive capacities evolved in concert with the demands and patterns of human WBPAs. Then, I briefly overview the most prominent cognitive enhancement strategies: cognitive training, neurostimulation, physical exercise, and cognitive-motor training. Finally, I call for an evolutionary cognitive enhancement (ECE) approach that synthesizes insights from neuroscience (Karkou & Meekums, 2017; Raichlen et al., 2020a, 2020b; Teixeira-Machado et al., 2019), evolutionary psychology (Buss, 2019; Cosmides & Tooby, 2013), and anthropology models (D. E. Lieberman, 2015; Raichlen & Alexander, 2017) to design therapies that stimulate our adaptive cognitive capacities.

Evolutionary History and Adaptations

Over 4 billion years of evolution have sculpted countless species, each with stunning diversity and intricacy of cognitive capabilities (Darwin, 1859; MacLean et al., 2014; Pearce et al., 2018). These cognitive processes enable organisms to recognize and respond to opportunities and threats, ultimately enhancing their reproductive success. Among the outstanding outcomes of this evolutionary process are humans with significant problem-solving capabilities.

Evolutionary anthropology suggests that our hominin lineage split off from the Pan lineage (chimpanzees and bonobos) around 6 to 8 million years ago (Mya), and our genus (Homo) evolved approximately 2 Mya (Conroy & Pontzer, 2012; Langergraber et al., 2012; Pontzer et al., 2018). One of the most significant changes in hominid physiology is upright walking (Gruss & Schmitt, 2015; Marino et al., 2022; Wood & Harrison, 2011). The transition to bipedal locomotion allowed our ancestors to forage more effectively and address problems in increasingly open and non-forested habitats (D. E. Lieberman, 2015; Marino et al., 2022; Raichlen & Alexander, 2017).

Starting at approximately 2 Mya, early human ancestors embarked on a new hunting and gathering lifestyle, requiring a substantial expansion and diversification of their WBPAs compared to their less active hominin ancestors (D. E. Lieberman, 2016; Malina & Little, 2008). This shift influenced brain structures, as well as psychological and cultural evolution (Bramble & Lieberman, 2004; Lovejoy, 1988; Raichlen & Gordon, 2011; Ruff et al., 1997), including the development of early stone tools around 2 Mya and language establishment likely between ∼300 and 100 thousand years ago (Ambrose, 2001; Marean, 2015; Perreault & Mathew, 2012). Hence, the evolution of bipedalism and new forms of WBPAs, such as forceful overhand throwing and stick use, within hunter-gatherer societies, played a significant role in shaping human cognitive architecture (Carscadden et al., 2018; K. R. Gibson et al., 1994; Hoenecke et al., 2024; Larson, 2009; Lombard & Haidle, 2012; Lovejoy, 2005).

The evolution of the motor cortical areas opened up new avenues for problem-solving strategies, allowing cognitive processes to become intricately linked to diverse types of WBPAs (Koziol et al., 2012; Mendoza & Merchant, 2014; Sherman & Usrey, 2021); cortical motor networks became tightly connected to cognitive functions such as memory, language, imitation, and decision-making (Buchsbaum & D’Esposito, 2019; Hauk et al., 2004; Ianì, 2019; Inagaki et al., 2022; Mendoza & Merchant, 2014). In particular, substantial evidence supports links between language processing and the sensorimotor circuits (Hauk et al., 2004; Pulvermüller, 2005; Pulvermüller & Fadiga, 2010). Moreover, although traditionally associated with motor functions, the cerebellum has also been shown to be involved in a wide range of cognitive functions (Koziol et al., 2014; Magielse et al., 2023; Schmahmann, 2019; Sokolov et al., 2017; Turner et al., 2007; Van Overwalle & Mariën, 2016). As I explain later, these solid connections between movement-related brain regions and cognitive systems suggest the evolution of content-rich CM systems, which enable brain-body-environment synchronization in response to adaptive problems.

Problem-solving in the hunter-gatherer lifestyle, which prevailed through much of human history before the agricultural and industrial revolutions, relied heavily on specialized neurobiological adaptations (D. E. Lieberman, 2016; Marean, 2015; Schrire, 2016). These adaptations were needed to extract information about the physical environment (such as topographic features and celestial cues such as moon cycles for timekeeping), animal behavior (including patterns of prey, predator, and breeding), social communication (such as storytelling, cooperation, and disease avoidance), and tool use (involving activities like digging and defense) (Cosmides et al., 2018; Hamilton et al., 2007; D. E. Lieberman, 2016; F. Marlowe, 2010; F. W. Marlowe, 2007; Nowell et al., 2024; Raichlen et al., 2014; Smith, 2004; Sznycer et al., 2021; Tooby & Cosmides, 1990b; Tybur et al., 2011; Wood & Harrison, 2011).

Hunter-gatherers would have regularly faced adaptive problems, including mate selection, repelling sexual rivals, food deprivation, hunting, avoiding contagious diseases, regulating social-emotional interactions, acquiring language, and engaging in collaboration (Bramble & Lieberman, 2004; Cosmides et al., 2018; Cosmides & Tooby, 2013; Mattson, 2015; G. A. Miller, 2003). These persistent challenges were typically associated with or required various forms of WBPA, such as walking, running, sprinting, jumping, punching, kicking, climbing, crawling, balancing, carrying, lifting, throwing, catching, swimming, dancing, repetitive patterns of movements, and imitating others, and so on (D. E. Lieberman, 2015; Pontzer et al., 2018). These functional demands to respond to adaptive problems using diverse patterns of WBPAs constructed extensive CM mechanisms (Cosmides & Tooby, 2000; D. E. Lieberman, 2015, 2016; G. A. Miller, 2003; Raichlen et al., 2014; Raichlen & Alexander, 2017; Tooby & Cosmides, 2005).

The strong and enduring selection pressures that hunter-gatherer societies experienced played a fundamental role in establishing a range of socio-cognitive mechanisms related to group living (Cosmides & Tooby, 2013; Schaller & Crandall, 2003; Tooby, 2020). Cognitive processes such as empathy, reciprocity, and coordination evolved to facilitate effective interactions among our ancestors, promoting collaboration, cooperation, and collective decision-making. These social cognitive processes supported activities such as hunting, gathering, and group dancing, where coordinating WBPA with other group members played a crucial role (D. E. Lieberman, 2015; G. A. Miller, 2003; Pontzer et al., 2018; Smith, 2004). Accordingly, the WBPA patterns in cooperative performances, such as dancing, were essential signals and criteria in the process of mate selection (M. Brown et al., 2022; Fink et al., 2021; A. K. Hill et al., 2017; Hugill et al., 2010; McCarty et al., 2013; Puts, 2016).

Men and women have faced many similar adaptive challenges, resulting in the development of numerous characteristics shared between the sexes. However, different sexual and parental requirements shaped specific cognitive processes, leading to the emergence of sex differences in particular domains (Archer, 2019; Buss & Schmitt, 1993, 2019; Choleris et al., 2018; Cosmides & Tooby, 2013; Grissom & Reyes, 2019; Hampson et al., 2021; Kruger & Byker, 2009; D. L. Lieberman et al., 2012; Proverbio, 2023; Yagi & Galea, 2019). In hunter-gatherer societies, males were primarily involved in hunting, which required navigating large territories and tracking and predicting prey movements. Research on spatial cognitive abilities has demonstrated that, on average, males tend to outperform females in several tasks requiring spatial navigation and orientation abilities (Boone et al., 2018; Munion et al., 2019). Conversely, females were primarily gatherers, necessitating detailed knowledge of various edible plants' locations and seasonal availability, leading to superior object-location memory (Bocchi et al., 2020; Cosmides & Tooby, 2013; Silverman et al., 2000). Furthermore, to some degree, biological and evolutionary processes have contributed to sex differences in specific emotional profiles (Buss, 2018; Christov-Moore et al., 2014; Kret & De Gelder, 2012); for example, females have higher levels of disgust than males, which is thought to be associated with avoiding undesirable consequences such as infection or choosing an inferior mate (Al-Shawaf et al., 2018; Haidt et al., 1994; Tybur et al., 2009). In addition, while males tend to display heightened aggression and competitiveness, females typically exhibit greater nurturing behaviors and social bonding (Ahmad & Smith, 2022; Archer, 2004, 2019; Björkqvist, 2018; Nivette et al., 2019).

Cognitive functions have undergone extensive adaptation (R. A. Hill & Barton, 2005; Nairne & Pandeirada, 2016; Nairne et al., 2007; Nesse & Ellsworth, 2009; Öhman et al., 2001; Paivio, 2014; Seitz et al., 2020). For example, sensory information related to ancestral environments, such as red color, bitter taste, growling or roaring sounds, and sharpness, can be rapidly perceived. This immediate perception triggers relevant emotional systems (e.g., fear, anger, and disgust) before engaging in cognitive control processes and may lead to the generation of appropriate WBPA. Likewise, cognitive mechanisms, such as attention and memory, are well adapted to prioritize information processing relevant to adaptive challenges, such as the rapid recognition of living organisms (Cosmides & Tooby, 2013; New et al., 2007). Determining the selection pressure for human cognitive adaptations can provide a valuable basis for developing effective cognitive enhancement interventions.

The solution to any adaptive problem often involves multiple interconnected subtasks, each requiring coordinated CM processes (Al-Shawaf et al., 2016; Marr, 2010; Tooby & Cosmides, 2005). For example, picking mushrooms in a forest requires determining where and when to go for them, remembering the paths to return home, interacting with others, avoiding potential health dangers like snakes or feces, and recognizing safe mushrooms but ignoring poisonous ones. While these subtasks can stimulate multiple CM functions, specific emotions (e.g., fear) or cognitive control processes (e.g., working memory) may sometimes become more involved/activated. For instance, inhibitory control is essential for suppressing natural desires or impulses, such as seizing an opportunity for food, when such actions could have negative consequences (e.g., social exclusion). Therefore, the environment in which the human mind evolved was rife with intertwined challenges and conflicts, the resolution of which required synchronization between multiple integrated CM systems.

Conventional Cognitive Enhancement Strategies

While the basic platform for developing brain networks is encoded in human DNA, this does not involve simple instructions but requires environmental input. At all stages of development, from the fertilized egg onwards, cells interact with each other and their environment to determine how they will grow. This is particularly true of neurocognitive systems, which interact with the body and the environment, reshaping and rewiring themselves in response to experience (Pascual-Leone et al., 2005). Taking advantage of this neuroplasticity, researchers have developed methods and interventions to promote mental functions. Below is a brief overview of several well-researched cognitive improvement methods.

Cognitive Training

The impact of cognitive training programs on brain functions has been studied extensively (Chiu et al., 2017; Clark et al., 2017; Gallen & D’Esposito, 2019; Green & Bavelier, 2015; Klingberg, 2010; Olesen et al., 2004; Richmond et al., 2011). These programs often use computer-based tasks, such as working memory tasks (e.g., N-back task), requiring user interaction through a screen and keyboard. This approach has been used in several cognitive rehabilitation programs (e.g., RehaCom software: Fernández et al., 2012; García-Fernández et al., 2019) to improve executive functioning in people with neurological disorders. However, it is worth noting that cognitive training has had mixed results (Gobet & Sala, 2023; Mewton et al., 2020; Shipstead et al., 2012; Simons et al., 2016; Thompson et al., 2013).

While working memory training improves performance on similar practiced tasks (near transfer), evidence for its effectiveness in boosting performance in untrained tasks (far transfer) is less conclusive (Hou et al., 2020; Karbach & Verhaeghen, 2014; Kassai et al., 2019; Melby-Lervåg et al., 2016; Ripp et al., 2022). For example, Ripp et al. (2022) have shown that n-back training for eight weeks did not result in near or far transfer effects, nor did it show relevant changes in brain structure. Broader cognitive benefits, such as increased fluid intelligence and better daily activities, might be achieved through training programs that combine challenging cognitive and physical tasks (Lustig et al., 2009; Wu et al., 2013; Yogev-Seligmann et al., 2008). Looking through an evolutionary lens, cognitive control training with limited involvement of relevant WBPAs and emotions may not adequately engage the adaptive CM capacities.

Neurostimulation

Non-invasive brain stimulation methods include transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS). These neuromodulation strategies aim to affect brain excitability and activity. Anodal tDCS, for example, has been shown to enhance the activity and function of the targeted cortical area (e.g., by strengthening glutamatergic synapses), whereas cathodal tDCS can suppress these impacts (Dayan et al., 2013; Ziemann et al., 2008). This method has been used to treat various psychiatric disorders (Lefaucheur et al., 2017) and improve executive control in non-clinical populations (Coffman et al., 2014).

Neurostimulation techniques appear to enhance specific cognitive domains, mainly working memory and attention (Coffman et al., 2014; Hurley & Machado, 2017, 2018; Luber & Lisanby, 2014), yet consistently replicating these benefits has proven challenging (Begemann et al., 2020; Hara et al., 2021; Imburgio & Orr, 2018; Jiang et al., 2024; Medina & Cason, 2017; Vöröslakos et al., 2018). A meta-analysis (Horvath et al., 2015) found no significant evidence supporting the effects of tDCS during-task (“online”) and after-task (“offline”) applications on various cognitive functions, including working memory, executive control, and language. Moreover, some studies applying tDCS to the frontal cortex have reported a decrease in specific aspects of intelligence, like perceptual reasoning (e.g., Sellers et al., 2015), suggesting that boosting neuronal activity may not always translate to improved cognitive processing (Friedrich et al., 2019; Imburgio & Orr, 2018; Rose et al., 2020).

Applying brain stimulation without considering the evolutionary roots of human cognitive functions is akin to engineering or changing complex systems without comprehensive functional maps. This oversight may have unfavorable long-term consequences, especially in children and adolescents (Day et al., 2022). Designing interventions that engage the natural functioning of human physiology, such as combining brain stimulation with WBPA-based cognitive training, holds promise for enhancing cognitive gains (Wang et al., 2021; Y. Xu et al., 2023a, 2023b).

Physical Training

Physical exercise has a much broader impact on the brain than cognitive training and neurostimulation (Cheval et al., 2023a, 2023b; Erickson et al., 2015, 2022; Etnier et al., 1997; Guiney & Machado, 2013; A. G. Thomas et al., 2012; Weinstein et al., 2012). Physical activity, including WBPA, has been linked to beneficial physiological processes such as higher cerebral blood flow and oxygenation (Ainslie et al., 2008; Cameron et al., 2015; Guiney et al., 2015) and functional and structural neuroplasticity (Knaepen et al., 2010; Lista & Sorrentino, 2010; Mahalakshmi et al., 2020; A. G. Thomas et al., 2012). While the benefits of WBPA for cognitive performance have been widely accepted (Åberg et al., 2009; Angevaren et al., 2008; Anzeneder et al., 2023; Mancı et al., 2023; L. Xu et al., 2023a, 2023b), recent research highlights the importance of higher levels of moderate and vigorous physical activity to maximize cognitive performance (Cheval et al., 2023a, 2023b).

Acute WBPA has been found to promote prefrontal activation and enhance cognitive control functions, including working memory and inhibitory control (Basso & Suzuki, 2017; Benzing et al., 2018; Chang et al., 2012; Hillman et al., 2019; Lebeau et al., 2024; Moreau & Chou, 2019; Tsujii et al., 2013). Moreover, acute-exercise research has highlighted the immediate cognitive benefits of WBPA in real-life environments (Nasrollahi et al., 2022; Stenling et al., 2019). Furthermore, several studies have shown that mental and physical wellness is enhanced when WBPA is paired with exposure to natural environments (Fitzgerald & Danner, 2012; Gladwell et al., 2013; Marquez et al., 2020; Nisbet et al., 2011; Pretty et al., 2005; Rogerson et al., 2020; Tsao et al., 2022). Though further research is needed to improve the data quality (Kramer & Colcombe, 2018; Lahart et al., 2019), these findings back up the relevance of intense WBPA in natural outdoor spaces, where CM systems originally evolved and morphed to respond to adaptive problems.

Combined Cognitive and Physical Training

Concurrent cognitive and physical training can result in more significant cognitive gains than performing either alone or in sequence (Bherer et al., 2021; Dhir et al., 2021; Gavelin et al., 2021; Gheysen et al., 2018; Joubert & Chainay, 2018; Kim & Park, 2023; Pellegrini-Laplagne et al., 2023; Raichlen et al., 2020a, 2020b; Tait et al., 2017; Theill et al., 2013). Examples of combined cognitive and physical training include walking while doing a verbal fluency exercise (e.g., naming animals) or counting backward (Wollesen & Voelcker-Rehage, 2014; Wollesen et al., 2020). In addition, engaging in WBPA along with cognitive tasks can stimulate different CM networks and increase variables relevant to brain plasticity (Kempermann et al., 2010; Leone et al., 2017; Ou et al., 2024; Raichlen & Alexander, 2017; Wollesen et al., 2020; Wu et al., 2013). Notably, while WBPA, such as aerobic and strength training, primarily improves overall physiological processes like cardiovascular fitness, complex WBPA training involving activities such as balance, coordination, and flexibility is believed to have a direct impact on neuroplasticity and cognition (Gheysen et al., 2018; Netz, 2019).

Moreover, several studies have tried to develop psychological assessment tools that directly combine cognitive and physical activities, such as the walking Stroop carpet (Perrochon et al., 2013) and the walking Corsi test (Piccardi et al., 2008, 2014). Unlike traditional paper-and-pencil tests or typical computerized cognitive control tasks (Heaton et al., 2014; Karlsen et al., 2022; Sahakian & Owen, 1992), the entire body movements are integral to performing these CM tasks. For example, in the walking Corsi test, which has been used to measure individual and sex differences in spatial memory and navigational abilities (Bianchini et al., 2010; Piccardi et al., 2010, 2014), the blocks of the Corsi task (Pompili et al., 2016) are drawn on the floor. Participants are asked to replicate the walking pattern of the examiner, addressing problems directly by using WBPA-based problem-solving. Although these methods have not been used as training strategies, and the task conditions have not been designed based on the nature of ancestral adaptive problems, the direct engagement of WBPAs in addressing tasks is a noteworthy aspect of this line of research.

Physical movements have also been incorporated into computerized cognitive control tasks such as the Simon task (J. Chen et al., 2021; Gupta et al., 2022; J. Miller, 2016; Proctor et al., 2023; Watanabe et al., 2016). In the conventional Simon task, seated participants respond to stimuli (e.g., color, shape) by pressing the corresponding left or right keys on a keyboard, disregarding the stimuli’s left or right location. Given individuals naturally tend to respond to stimuli on the left with their left hand and vice versa, an interference effect (increased response time and errors) arises in incompatible conditions—when there is a mismatch between the stimulus location (left or right) and the required response hand (Ansorge & Wühr, 2004; Proctor et al., 2023). Unlike this typical approach involving finger movements for button presses, some studies have investigated using other types of physical responses, such as foot pedal presses (J. Chen et al., 2021; Gupta et al., 2022; Leuthold & Schröter, 2006; J. Miller, 2016; Mittelstädt & Miller, 2020; Proctor et al., 2023; Watanabe et al., 2016). Incorporating different physical responses directly into computer-based cognitive tasks can allow WBPA involvement to help resolve conflicting tasks or problems.

Evolutionary Cognitive Enhancement (ECE)

The evolutionary pressures experienced by our hunter-gatherer ancestors shaped neurobiological systems involving intricate whole-body-based solutions (Bramble & Lieberman, 2004; Koziol et al., 2012; D. E. Lieberman, 2015; Musculus et al., 2021; Raichlen et al., 2017). These cognitive capacities are well adapted to address a wide range of social and physical challenges using WBPAs, as shown by research on hunter-gatherer tribes (e.g., the Hadza) and modern human populations (D. E. Lieberman, 2015; Pontzer et al., 2018; Raichlen et al., 2017, 2020a, 2020b). However, the rapid advances in technology and the extensive availability of resources have considerably diminished the perceived value of body movements in everyday modern life (Benvenuti et al., 2023; Büchi et al., 2019; Luhmann et al., 2023; Sbarra et al., 2019).

Research indicates that adults worldwide are sitting more than ever, with many exceeding 8 h of daily sitting (Bonnet & Barela, 2021; Bonnet & Cheval, 2023; Ng & Popkin, 2012). Indeed, modern tasks often rely on tech-based solutions (e.g., staring at a screen and typing using fingers), reducing the extent and range of body engagement in problem-solving strategies (see Fig. 1). Therefore, the CM systems associated with the generation and regulation of various adaptive WBPAs—collaborative physical activities, such as rushing, jumping, climbing, throwing, and digging, required to address adaptive problems—are less necessary in most modern lifestyles, particularly among adults (Bonnet & Cheval, 2023; Fitzgerald & Danner, 2012; Fox, 2018; D. Lieberman, 2020; Raichlen et al., 2014).

Fig. 1
figure 1

Cognitive-motor engagement. A Adapting to the physical and social challenges of hunter-gatherer life (e.g., hunting, foraging, collaboration) drove the evolution of our cognitive-motor abilities. B Modern lifestyles often involve fewer adaptive problems, potentially leading to a decline in the engagement and development of these crucial cognitive-motor mechanisms. C While valuable, current cognitive enhancement approaches (e.g., neurostimulation, memory training, and brain games) often overlook the importance of stimulating our built-in adaptive capacities. D Integrating physically and socially demanding activities into our daily routines and computer-based tasks may offer a more natural and effective way to boost cognitive functions

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This inactive lifestyle has contributed to a mismatch between humans’ evolved physiology and environment, leading to a significant increase in the prevalence of non-communicable diseases such as Alzheimer’s, obesity, cancer, and heart disease (Bonnet & Cheval, 2023; Cerniglia et al., 2017; Eaton & Eaton, 2003; Fox, 2018; Kivipelto et al., 2018; Kopp, 2019; O’Keefe et al., 2011; Pontzer et al., 2018; Raichlen et al., 2017). A sedentary lifestyle is also linked to cognitive disabilities such as impairments in executive function (Bonnet & Barela, 2021; Chandrasekaran et al., 2021; A.-M. Gibson et al., 2017; Pruimboom, 2011). In fact, in response to the reduced demand for various adaptive WBPAs and associated cognitive-emotional processes, neurobiological systems may not be stimulated adequately, leading to cognitive decline with age mainly based on energy conservation processes (Kempermann et al., 2010; D. E. Lieberman, 2015; Raichlen & Alexander, 2017). So, adaptive CM networks may become underused despite their propensity to be activated, process information effectively, and support learning and mental health (Chandler & Tricot, 2015; Cosmides & Tooby, 2000; D. E. Lieberman, 2015; O’Keefe et al., 2011; D. J. Stein, 2019).

Considering the significant impact of the evolution of upright walking and other types of adaptive WBPAs on brain development and the establishment of adaptive CM systems (Bramble & Lieberman, 2004; Lovejoy, 1988; Ruff et al., 1997), as well as the beneficial effects of CM training on brain functions (Gavelin et al., 2021; Gheysen et al., 2018; Raichlen & Alexander, 2017), I propose an evolutionary cognitive enhancement (ECE) approach, grounded in the adaptive interactions between brain, body, and environment. The proposed ECE approach aims to stimulate and leverage the evolved problem-solving capabilities of CM systems by designing targeted training programs and tools that bridge the gap between our ancestral challenges and the demands of modern, tech-based life. From this view, the training program features, including stimuli, manipulanda, and instructions, should involve adaptive WBPA-based solutions. Hence, developers of ECE-based therapies should first consider and acknowledge evolved whole-body capacities, including adaptive CM mechanisms. By understanding CM adaptations and the evolutionary forces that shaped them, I argue that we can develop cognitive tasks with relevant physical and social stimuli and body engagement, leading to significantly improved cognitive performance.

In the context of a particular cognitive function like working memory (WM), an ECE-based therapy should engage WM in coordination with relevant emotions and WBPA types. In line with this view, the following questions should be answered to design an ECE program for improving WM: What were the primary functions of WM over the evolutionary past? What kinds of adaptive problems were mainly addressed by WM? What specific emotions and WBPA types were essential in hunter-gatherer societies to synchronize with WM to address adaptive problems? How can these adaptive problems and their relevant stimuli and conditions be effectively incorporated into modern tasks and settings?

Among adaptive cognitive processes, adaptive memory is a particularly well-studied area (Ianì, 2019; Nairne & Pandeirada, 2016). Our memory systems prioritize information crucial for survival, enhancing cognitive performance for tasks relevant to adaptive problems (Fernandes et al., 2021; Nairne, 2022; Nairne et al., 2017). More specifically, memory systems have a greater tendency or capacity to remember living things over objects, both in past experiences and future tasks (Félix et al., 2023; Nairne et al., 2017). Bridging these fundamental aspects of memory (Nairne, 2022) with CM training findings can provide a broader framework for designing methods that optimize learning and cognitive functions.

Given the nature of our adaptive neurobiological processes, it is essential to consider the extent and variety of WBPAs that cognitive training tasks require. Conventional computerized cognitive training typically requires a limited physical involvement to respond, such as key presses and mouse clicks. To engage neurobiological adaptations, including CM systems, various moderate and intense WBPAs (e.g., punching, kicking, and throwing objects) need to be incorporated into cognitive training. Reseach has applied innovative technologies like touchscreens and virtual reality to facilitate WBPA engagement (Badilla-Quintana et al., 2020; Johnson-Glenberg, 2018; Johnson-Glenberg et al., 2016; M. Bagher et al., 2023). These methods enhance sensory and motor interactions with tasks and environments, leading to significant enhancements in cognitive functions, including learning, memory, and language abilities (Cardona, 2017; Öttl et al., 2017; Weisberg & Newcombe, 2017; Werner et al., 2019; Zona et al., 2019).

Executive function tasks (such as Stroop, Simon, Flanker, Go/No-go, and N-back tasks) should be presented in CM format by replacing mouse clicks with different WBPA actions. Similar to walking cognitive tasks (Perrochon et al., 2013; Piccardi et al., 2008, 2014), executive function task conditions (e.g., Simon task stimuli; blue and red circles) can be implemented in a specified real-life environment (e.g., a floor or stairway). In addition, CM task performance can be monitored using recent affordable technologies such as foot pressure sensors. This approach brings cognitive enhancement therapies closer to aligning with naturalistic settings, enhancing their relevance and effectiveness.

Furthermore, the task stimuli implemented in the specified ECE environment should naturally elicit relevant emotions and facilitate involving adaptive WBPAs. Cues such as social information (e.g., heads) take precedence over salient objects (Birmingham et al., 2009; End & Gamer, 2017; Flechsenhar & Gamer, 2017). Human faces and animals naturally trigger specific emotional systems because they capture more attention due to their evolutionary significance. In addition, ECE-based therapy can harness the power of adaptive cognitive-social capacities by providing social engagement contexts that allow individuals to accomplish CM tasks collaboratively (Richardson et al., 2007; Schmidt & Richardson, 2008).

To implement adaptive problems in a specific cognitive task, such as the conventional working memory task (N-back), the interaction between the task and individuals (including WBPA, stimuli, manipulanda, and instructions) needs to be adapted. Given a combination of anatomical adaptations, such as our bipedalism and more developed shoulders compared to other primates, and neurobiological adaptations that facilitate the complex coordination required, a significant adaptive WBPA is thought to be throwing (Calvin, 1982; Carscadden et al., 2018; Hoenecke et al., 2024; Lombardo & Deaner, 2018; Roach et al., 2013; M. Stein et al., 2017). Despite its substantial significance in our evolutionary past, throwing has mainly disappeared in modern daily problem-solving (Bonnet & Barela, 2021; Bonnet & Cheval, 2023; Ng & Popkin, 2012). Future research can incorporate throwing and other adaptive WBPA, such as stick use, into cognitive tasks, such as the N-Back task, beyond manual keypress responses. Moreover, given previous research on adaptive memory, replacing digits with evolutionarily relevant stimuli, such as images of animals, may improve performance in working memory tasks.

Furthermore, some tools allow us to study WBPA-based problem-solving in social contexts. For instance, to complete executive function tasks like the Simon task, two or more individuals can work together, each standing and holding a stick or similar object, to directly tap the correct relevant targets/stimuli. Integrating these adaptive conditions into computerized tasks can activate and strengthen the adaptive neurobiological pathways, promoting neural plasticity and cognitive flexibility.

An excellent example of a possible ECE therapy is social group dancing. Dancing evolved as a diverse array of rhythmic bodily movements intertwined with various cognitive and emotional processes across different cultural contexts (Fink et al., 2021). It encourages individuals to synchronize specific WBPAs with music and, critically, in coordination with others. This need for coordination activates integrated CM circuits and enhances mental health (Chan et al., 2020; Fink et al., 2021; Fong Yan et al., 2018; Karkou & Meekums, 2017; Kosmat & Vranic, 2017). Thus, learning challenging dance routines can foster greater neuroplasticity than physical activity alone (Rehfeld et al., 2018). Dance has also been suggested as a promising approach to improving interpersonal coordination and developing brain synchrony in individuals with mental problems such as autism spectrum disorder (Begemann et al., 2020; Fink et al., 2021).

The ECE view, here, is consistent with the embodied cognition approach, which suggests that cognitive processes are rooted in bodily experiences and sensorimotor interactions with the environment (Barsalou, 2008; J. S. Brown et al., 1989; Newen, et al., 2018; Richardson et al., 2008; L. E. Thomas & Lleras, 2009; Werner et al., 2019; Wilson, 2002; Zona et al., 2019). However, while emphasizing the immediate reciprocal link between cognition and physical experience (Ianì, 2019; Werner et al., 2019; Wilson, 2008), the embodied cognition literature often neglects the role of evolutionary pressures that shaped mind–body-environment connections. It is worth noting that individuals usually do not consider the deep evolutionary timescales in their assessments and analyses (Buonomano, 2017; Catley & Novick, 2009; Hansson & Redfors, 2006; Stenlund et al., 2022). In addition, the embodied approach primarily focuses on understanding the mechanisms and processes of cognition rather than improving cognitive abilities. In contrast, the proposed ECE approach views cognitive enhancement through the lens of an evolutionary perspective, underscoring that cognitive capacities have evolved to respond to selective pressures.

ECE highlights the importance of rethinking the conventional cognitive assessment tasks and intervention techniques used in clinical settings. These approaches often focus solely on evaluating and training cognitive abilities, neglecting the relevance of emotional systems and WBPA patterns integral to human physiology. Technologies such as virtual reality (D. Chen et al., 2024; Woessner et al., 2021) and pressure sensor systems that facilitate increased bodily engagement need to be integrated into current psychological toolkits. In non-clinical contexts, there is potential for the next generation of commonly used technologies, such as smartphones and laptops, to be developed with novel software and hardware that can provide opportunities to engage adaptive CM processes. Specifically, these technologies can encourage adaptive patterns of WBPA in modern workplaces (e.g., keyboards that can be operated using WBPA rather than just finger movements). Moreover, engineers and city planners might adopt the ECE approach by designing buildings and environments (e.g., streets, parks, and schools) that allow people to use adaptive CM capacities. Integrating the ECE approach into daily activities can improve mental functions and mitigate the rate of cognitive decline with age.

Conclusions

Diverse CM mechanisms are well adapted to process certain physical and social information effectively and generate and regulate various adaptive WBPAs, such as dancing and throwing objects. However, industrialization and automation have profoundly influenced human lifestyles, diminishing the linkage of various forms of WBPA engagement with problem-solving strategies. Given recent technological advances, psychologists developed computer-based cognitive tasks, such as cognitive control tasks, to measure and train cognitive functions. However, compared to diverse collaborative WBPAs inherent in historical human behaviors, current static cognitive tasks typically do not require adaptive WBPAs. Moreover, modern cognitive enhancement strategies such as cognitive training, neurostimulation, and physical training strongly emphasize neuroplasticity while neglecting the evolutionary principles that underlie neurocognitive mechanisms.

The proposed ECE approach is informed by research from diverse domains, including evolutionary neuroscience and anthropology (Fox, 2018; D. E. Lieberman, 2016; O’Keefe et al., 2011; Pontzer et al., 2018; Raichlen et al., 2017; Raichlen et al., 2020a, 2020b), embodied cognition (Cardona, 2017; Öttl et al., 2017; Weisberg & Newcombe, 2017; Zona et al., 2019), and the benefits of simultaneous cognitive and physical activities (Bherer et al., 2021; Dhir et al., 2021; Gheysen et al., 2018; Joubert & Chainay, 2018; Raichlen et al., 2020a, 2020b; Tait et al., 2017; Theill et al., 2013). This perspective advocates for developing strategies and tools that engage and leverage content-rich adaptive CM mechanisms by incorporating adaptive WBPAs into modern daily tasks. The framework presented in this article has the potential to pave the way for future research in a range of fields, particularly when cognition is sought to be improved.