Written by: Adam Eckart, Executive Contributor
Executive Contributors at Brainz Magazine are handpicked and invited to contribute because of their knowledge and valuable insight within their area of expertise.
In a previous Brainz Magazine article, I reviewed the short and long-term beneficial effects of exercise on cognitive function ‒ you can read it here:
In summary, exercise volume and intensity have profound effects on executive functioning by enhancing adaptive processes that protect the brain from age-related decline. These physiological adaptations are evolutionarily hard-wired in humans due to our heritage of nomadic living. Humans are designed to respond to and utilize the environment to achieve goals that improve the likelihood of survival. Our cognitive and motor skills developed in tandem to solve complex problems, manipulate the physical and social environment, and travel long distances to find greener pastures. This is apparently why most neurodegenerative brain disorders impact cognition and motor functioning similarly.
With the understanding of this link, we can engage in routine exercise regimens as one method of brain hygiene to keep our cognitive abilities sharp. Another method of improving cognitive function is to intently engage in cognitively challenging tasks, or in other words, perform strength training for the brain. The conditions necessary for strengthening our cognitive abilities over time are not dissimilar to the principles of exercise training which include specificity, progessive overload, reversibility, variation, and individuality. The effects of these principles, when applied systematically to purposeful improvement, may be the most apparent when applied to physical fitness, yet, they can ultimately be applied to any endeavor in which efficiency is paramount such as in business systems, learning new subjects, or playing the ukulele. Let’s take a closer look at the principles of exercise training in the context of improving the cognitive skills that are so important to success in all aspects of life.
This principle refers to the notion that the body adapts specifically to the demands imposed upon it. To provide an example from physical training, if our goal is to strengthen our upper body, we must choose exercises that maximize the load that could potentially be placed on the major muscles of the upper body. If we choose an exercise that does not maximize the potential load, we decrease the strength gains that could be achieved. Furthermore, if we want to maximize strength in only our biceps, using the same exercise we performed to gain strength in the upper body overall would not be sufficient because the exercise does not specifically target the biceps. Now, there may be some transfer effects, especially if the biceps are involved to some extent in the exercise, but not as much as there would be if the biceps were the primary contributor to the movement. The same appears to be true in terms of cognitive performance enhancement. Far transfer refers to the improvement of general cognitive abilities (CGA) by engaging in specific activities that require multiple cognitive skills. The term “far transfer” implies that CGA improvements are not necessarily constrained to the activity, but rather, these improvements transfer to unrelated tasks. One example of this is the claim that playing chess improves overall working memory. But recently, activities that have been long-purported to improve CGA such as chess, gaming, or app-based brain games have been shown to be relatively ineffective when treatment groups are compared to active control groups, calling into question the validity of far transfer (Sala & Gobet, 2019). Sala and Gobet (2019) argue that most studies have design flaws that undermine causal effects, report improvements in different tasks that use similar underlying neural networks, or do not use post-intervention tests that measure cognitive skills in unrelated tasks. Using the chess example, researchers explain that the superior memory of elite chess players’ is likely limited to game-related factors such as the chess board and pieces. Driving this point home is the finding that education level has a larger effect on test scores that assess recently-learned subject matter compared to tests of fluid intelligence, which is a better predictor of success (Ritchie & Tucker-Drob, 2018). The key takeaway from this information is that cognitive ability is task-specific, just like physical training. If you want to improve in a task, you must perform that task more often.
Progressive overload refers to the deliberate increase in workload over time as the body adapts to a consistently-applied stimulus. Through compensatory adaptation, the tolerance of the target system increases, requiring higher workloads in order to achieve a given intensity or effort level. In physical training, the total workload of the training is monitored and continually progressed beyond the total workload of the previous exposure in a process called overreaching. Overreaching places higher stress (workloads) on the body which promotes supercompensation after a period of recovery. Too much stress or too little recovery in a given period causes a maladaptive response called overtraining syndrome. The key to avoiding overtraining is to find the optimal zone of stress and recovery. Drawing a parallel to learning theory, the gap between an individual’s current and desired cognitive abilities is bridged by applying increased demand on task-specific cognitive skills. Performing challenging cognitive tasks over time builds cognitive reserve and compensatory adaptations that improve the efficiency of the underlying neurological mechanisms. As with physical training, stimulus-induced stress is vital to the process of cognitive skill improvement. Adrenaline released in response to stress ramps up focus and when the learner (or exerciser) is engaged (motivated), increased dopamine produces a perception of low effort also known as being “in the zone” of proximal development. Being engaged, however, requires finding the sweet spot between too challenging and not challenging enough (Van Der Linden, Tops, & Bakker, 2021). This often requires structured training from experts who build a scaffold of learning experiences that later become whittled down over time as the learner becomes more independent. In a recent trial, over 220 older adults with similar age-related decline participated in either learning new skills, social activities (i.e. playing games, watching movies), placebo (i.e. crossword puzzles), or everyday tasks as a control (Park et al., 2014). Each group performed 10 hours per week of directed activities and 5 hours per week of choice activities with the exception of the skill learning group that performed specific assignments outside of the directed instruction. After 14 weeks, the productive engagement group outperformed the passive engagement groups in tests of episodic memory, visuo-spatial processing, and processing speed. The key here is that you should continually seek opportunities to learn from experts in a structured environment that pushes you beyond where you would push yourself.
Reversibility (Use it or Lose It)
Almost all of us have been shocked at our miserable display when attempting to perform something we were once good at but have not done for some time, be it math or handstands. Yet, given a little time spent practicing the task, we regain our skill relatively quickly, in many cases faster than it took to learn the first time. This phenomenon is often referred to as muscle memory which is the result of the increased robustness of the underlying structures and mechanisms gained during experiential learning (Boyke, Driemeyer, Gaser, Büchel, & May, 2008). The more energy spent learning a specific task, the more ingrained these skills become (Brehmer et al., 2011). Invariably, however, we stop engaging in certain activities because life takes us in other directions or we simply lose interest (perhaps because we stopped improving). After a period of detraining, we almost immediately lose newly developed skills that have yet to become crystallized. But, we don’t lose everything we gained, rather, we return to a slightly higher baseline of skill. For example, despite reductions in performance, both younger and older study participants maintained gains above baseline in memory-related tasks 18 months after a 5-week training program (Dahlin, Nyberg, Bäckman, & Neely, 2008). Importantly, continuous improvement in cognitive function is more of a behavioral issue than a physiological one. As noted in the previous section, advancing age does not prevent you from improvement, but lack of engagement does. Because you are more likely to engage consistently in challenging or novel tasks long-term when you are motivated, you should seek out new skills that directly align with your goals, and once you’ve achieved your goals, set new ones!
The principle of variation refers to the systematic change in training variables to account for adaptations, diminishing returns, and the need for recovery periods. In the strength and conditioning world, we call this periodization. While there are many different ways to periodize training, the basic tenets could be applied to cognitive training. First, training loads must be high enough for long enough to elicit adaptation and be followed by periods of lower workloads to allow the body to recover and adapt. Next, when training for multiple skills, training periods should emphasize one skill to ensure optimal overload is being applied to that skill. After a plateau in improvement, change the stimulus to expose the system to novelty. Finally, recovery periods should be long enough to allow adaptations to occur, but not so long that recent gains begin to diminish. It is well established that short intense periods of cognitive work, called chunking, can be beneficial to maximize attention span and enhance learning (Murphy, 2008). Think of these chunks of time as your cognitive exercise sets. When your attention span on a task begins to wane, take a rest and come back to it later. Slowly increase the time (endurance), number (volume), and intensity (strength) of these chunks over the course of weeks and months, but be sure to include extended breaks after prolonged periods of intense cognitive work. Do not try increasing all variables at once, rather, build one variable (such as writing or learning) for a period of time before changing it up.
As the name implies, the principle of individuality refers to individual differences in baseline skill, rate of improvement, and improvement potential. Some differences are genetically determined and some differences relate to other factors including age, health status, behaviors, and environment. Logically, individuals should engage in cognitive and physical challenges that push their current limitations, but how can we maximize our rate of improvement? One major factor affecting cognitive performance is age. As we age, the variability in our cognitive abilities increases due to declining neural efficiency. But there are some tried and true methods for reducing variability in task performance. The most important method is getting feedback during a task. In one study, older research participants who received feedback between performances of a reaction-time task reduced the variability between trials similarly to younger participants (Garrett, MacDonald, & Craik, 2012). In other words, getting feedback eliminates the variability associated with age. With feedback, we improve our efficiency by focusing on the things that make a difference. In that same study, education level was a predictor of task improvement when participants were exposed to feedback. But education was more correlative than causative. The authors speculate that education level was probably more indicative of participants’ willingness to accept the feedback given by instructors, than it was about the intelligence level of the participants. Case in point, accept and apply feedback from experts who can help you avoid costly inefficiencies.
Human ability is neither static nor destined to decline. Whether it’s running an ultramarathon or learning quantum physics, we humans have amazing potential to adapt our bodies and brains. But adaptation cannot occur without understanding the conditions for improvement or the motivation to push the boundaries of our limitations. Ultimately, it is up to each one of us to create motivation and a fulfilling life by pursuing worthwhile goals and adopting an attitude of continuous improvement.
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Adam Eckart, Executive Contributor Brainz Magazine
Dr. Adam Eckart is an Assistant Professor in the School of Health and Human Performance at Kean University. Dr. Eckart holds a Doctorate of Health Sciences from A.T. Still University, a Master's of Science in Exercise Science, and a Bachelor's of Arts in Adult Fitness from Kean University. Dr. Eckart holds a plethora of industry certifications including the Certified Strength and Conditioning Specialist, USA Weightlifting Level One, Precision Nutrition, Functional Movement Screen, ACSM Exercise is Medicine, and Advanced Cardiac Life Support certifications, among others. Eckart's research interests include exercise and lifestyle medicine with a focus on the convergence of genetics, exercise and nutritional interventions, and chronic diseases. Dr. Eckart has 15 years of experience as a personal trainer and fitness business owner allowing him to blend the theoretical and practical applications of sports, fitness training, and behavior change.
Boyke, J., Driemeyer, J., Gaser, C., Büchel, C., & May, A. (2008). Training-induced brain structure changes in the elderly. Journal of Neuroscience, 28(28), 7031-7035.
Brehmer, Y., Rieckmann, A., Bellander, M., Westerberg, H., Fischer, H., & Bäckman, L. (2011). Neural correlates of training-related working-memory gains in old age. Neuroimage, 58(4), 1110-1120.
Dahlin, E., Nyberg, L., Bäckman, L., & Neely, A. S. (2008). Plasticity of executive functioning in young and older adults: immediate training gains, transfer, and long-term maintenance. Psychology and aging, 23(4), 720.
Garrett, D. D., MacDonald, S. W., & Craik, F. I. (2012). Intraindividual reaction time variability is malleable: feedback-and education-related reductions in variability with age. Frontiers in human neuroscience, 6, 101.
Murphy, M. (2008). Matching workplace training to adult attention span to improve learner reaction, learning score and retention. Journal of Instruction Delivery Systems, 22(2), 6-13.
Park, D. C., Lodi-Smith, J., Drew, L., Haber, S., Hebrank, A., Bischof, G. N., & Aamodt, W. (2014). The impact of sustained engagement on cognitive function in older adults: The Synapse Project. Psychological science, 25(1), 103-112.
Ritchie, S. J., & Tucker-Drob, E. M. (2018). How much does education improve intelligence? A meta-analysis. Psychological science, 29(8), 1358-1369.
Sala, G., & Gobet, F. (2019). Cognitive training does not enhance general cognition. Trends in cognitive sciences, 23(1), 9-20.
Van Der Linden, D., Tops, M., & Bakker, A. B. (2021). The Neuroscience of the Flow State: Involvement of the Locus Coeruleus Norepinephrine System. Frontiers in Psychology, 12.