Many of us grew up thinking about the senses (eyes, ears, nose, skin, and mouth) as the information/signal gatherers, the brain as the control center and the nervous system as the pathways that attach the brain to the muscles and internal organs. Although we now know that this simplistic view of how information is shared within the body is not exactly correct, for the sake of this discussion, we will use this classis paradigm as the basis of our discussion (Figure 1).

As a system, we know that there are limitations on how much information can be processed at once (again we must ignore the possibility of information sharing between the body and some external information source). As these limits are approached the system must make a choice to ignore some of the information it is collecting or to revert to degraded modes of operation by implementing heuristics (rules) that keep the probability of survival of the body at its peak possible state at all times. One way to think about this is that there are automatic functions that are necessary to keep the body alive and these are given the highest priority (priority 1) for mental processing resources. These include functions such as heart rate, respiration, chemical transport, etc. Next (Priority 2) are automatic functions that provide the processing needed for immediate survival needs such as movement in the peripheral visual field, eye movement, sounds and other threat sensing control functions. Finally, any processing power left over can be used for future survival needs such as learning. In this over-simplified model total cognitive or mental load is defined quite simply as the instantaneous total processing workload experienced at any point in time by a human (i.e., Priority 1 + Priority 2 + Priority 3).

For many years Cognitive Load Theory held the view that priority 1 and 2 functions could not be overcome without great effort. For example, it takes years of training to lower one’s own heart rate (priority 1) and although one might train one’s self to concentrate one’s mental focus on an object within the field of view to keep from being distracted from the task at hand, all photons entering the eyes from the entire visual field are still being processed in working memory. Under this view of cognitive load, the only way to reduce load is to reduce the amount or complexity of the information being presented to the senses. This is why study environments are quiet with little movement or other distractions and people learn better by scaffolding techniques that build new information on top of existing related knowledge in long term memory.

For years we lived with that understanding of cognitive load and a whole industry grew up around products and techniques to reduce the amount and complexity of sensory information to allow as much processing power to be made available for priority 3 functions as possible. Meditation, biofeedback, visualization with the eyes closed, and repetition are all examples of this type of cognitive load reduction schemes. The other rapidly growing way to reduce the amount of processing being used for priority 1 and 2 functions is through forced neurostimulation, i.e., electrical, magnetic, or chemical stimulation of neural pathways to force the brain to ignore the heuristics that determine how critical the threat environment may be. This kind of forced reduction of working memory load is dangerous and can lead to permanent changes in brain function. These types of devices should clearly never be used on children or young adults (Figure 2).

Despite this long history of belief within the scientific community that priority 1 and 2 functions were largely immutable we know that just as some signals from the senses increase the amount of processing needed to satisfy working memory heuristics, there are natural signals that can decrease processing needs. Examples include the relaxation that occurs when one sits looking at a gently falling rain or listens to the rhythmic lapping of small waves on a beach. In fact, techniques such as the use of these sounds, soothing colors, sweet aromas, or white noise signals are often used as a safe way to reduce overall cognitive load. The problem with these techniques however, is that they require the complete attention of the subject to be effective.

Knowing that the brain and sympathetic nervous system can be made to both increase and decrease total cognitive load we realize that one has only to identify those particular signals that cause these changes and apply them at the appropriate times to regulate load levels for optimum human performance under a wide variety of environments or during different activities.

In 2015 a small research team at Bevilacqua Research Corporation designed and executed an experiment to look at the effect of movement on cognitive load levels. It was expected that additional movement within the far peripheral field of view during cognitive task execution would increase cognitive load. Instead it was found that cognitive load was significantly reduced as shown in some of the original data from that experiment (Figure 3).

Eight years later we are beginning to understand that movement is one of the key elements that defines the language that controls working memory and the nervous system.

Just like a semantic language like English or French that relies on the correct ordering of an alphabet to provide humans with knowledge and understanding, this language is made up of frequencies, directions, colors, signal levels aromas, temperatures and other sensory inputs that when ordered correctly define the difference between a threat and a non-threat in the immediate environment.

Using specific kinds of movement as its first “cognitive vowel”, the team has been able to provide a significant reduction in cognitive load even during the parallel execution of complex cognitive tasks. The introduction of the correct movement signal format can either reduce or increase cognitive load providing a safe method of optimizing physical and cognitive performance in humans.

For more information regarding this significant new research contact the lead researcher Dr. Andy Bevilacqua, Bevilacqua Research Corporation, Huntsville, Alabama at andyb@brc2.com.