In part I of this blog I laid the foundation for my claim that the Military needs to fully understand the recent upgrades to cognitive load theory (CLT) or risk wasting money, time and the performance improvements promised by new military technology. We followed the development of the theory from its genesis by John Sweller (1988) through 2012 when upgrades, based on research in the field of evolutionary educational psychology (Geary, 2008; Paas & Sweller, 2012) suggested that different kinds of information (social and cultural) are handled differently in working memory. Finally, we introduced new research that found that cognitive load could be actively controlled through the addition of specific visual stimuli that takes advantage of the brain’s bias towards socially-relevant information (Bevilacqua, Paas & Krigbaum, 2016: Bevilacqua 2017). It was also found that this bias works differently for males and females (Bevilacqua, 2017; Castro-Alonzo, et al., 2019).
In the remainder of this article I will add detail to explain these new discoveries and how they will impact the use of new high-tech military hardware such as augmented/Virtual reality, surveillance systems, autonomous systems and situational awareness systems. Just like Sweller’s original research in 1988, if not designed correctly from the point of view of cognitive load, technology can degrade soldier performance rather than enhance it
If these new upgrades are not fully understood the potential problems that the military may experience with employing new high-tech systems are as follows:
- If gender differences are not taken into account, Male and/or female soldier performance will suffer
- Other CL moderators such as emotional state, age, educational level and fatigue are known moderators that must be taken into account when designing a high-tech system that stresses a soldier’s cognitive resources and challenges their performance. If not taken into account a soldier’s performance can actually be degraded and not improved. A one-size-fits-all solution based solely on multimedia design considerations does not work.
- Not understanding the differences in social and cultural information could make new systems overly complex and actually degrade soldier performance
- Cognitive load is not as dependent on multimedia design or user-experience design (commonly called UX design) as it is on evolutionary factors. Multimedia and UX designs can be extremely expensive and have been shown in many cases to actually increase cognitive load
The following sections look at the new discoveries in CLT and how they can be employed to save money and significantly increase soldier performance while employing new systems that rely heavily on information display to increase soldier situational awareness.
Active Cognitive Load Reduction (ACLR)
Over five years of human experiments have consistently shown that providing continuous visual stimuli during cognitive task performance can reduce cognitive load levels. But how can this be? CLT has always been based on the assumption that any input from the senses must be fully processed by working memory before being passed on for storage in long-term memory (Learning). Yet the data continues to support this result.
To find the answers we need we must revisit the field of evolutionary educational psychology and look at the evolution of working memory based upon gender differences. We know that from the earliest times men were primarily hunters and women were primarily gatherers. This differentiation in tasks had nothing to do with strength, stamina, or intelligence but rather the fact that women were the only humans capable of child-bearing. This meant that women tended to stay closer to tribal safe areas to provide protection for their young. Men, who were not burdened with children, were therefore left to wander far and wide in search of game to feed the tribes. The eye -brain combination is tasked with constant surveillance of the immediate area of the human to provide for survival. Light entering the eye is focused on the back of the eye where it is transformed into electro-chemical signals that are interpreted by the brain. If a potential threat is detected, attention is immediately directed to that area of vision to focus more clearly and help identify the threat level so action can be taken. As gatherers, probably with children in tow, any movement within the visual field would immediately cue a woman’s attention as being a potential threat. However, as hunters, males had to learn to ignore certain types of non-biological motion such as leaves blowing in the wind or the lapping of waves on the ocean. If this ability to differentiate biological from non-biological motion did not evolve in males, humans would not have been successful hunters and the human race would have eventually perished.
There are several competing theories regarding where this processing takes place in the brain however we actually do not know for sure where this processing takes place. The neuronal pathways within the brain are far too complex for us to follow. We can however, treat the brain as a black box and note its outputs when specific, controlled inputs are used. This kind of objective measurement gives us output data that can be related to inputs to give us some idea of how the brain modulates between inputs and outputs.
I alluded earlier in Part I of this blog to the fact that the cognitive load community had relied to a large extent on subjective measures for measuring cognitive load (De Waard, & Lewis-Evans, 2014). In a subjective measurement the researcher must rely on the opinion of the test subject to determine if a task was more or less difficult under varying conditions. This method can be successful however, in the case of cognitive load measurement subjective measures never quite provided the resolution needed to truly measure cognitive load levels. In addition, cognitive load is not a single entity but is a combination of intrinsic load that results from the complexity of the task itself and extrinsic load that is added by outside elements from the environment (DeLeeuw, & Mayer, 2008).
To solve this issue, The BRC technical team created an objective measurement instrument called the time on task exogenous load index (ToTEL-X) instrument (Figure 1) (Bevilacqua & Krigbaum, 2020).
This validated instrument allows the researcher to compare time-on-task for tasks performed with and without external load elements applied. Thus, the contribution to total load coming from the environment can be determined separately.
A full set of tests to establish the reliability and validity of the instrument were performed on ToTEL-X using the NASA-RTLX (NASA-Raw Task Load Index) (Hart & Staveland, 1988) tool that has been used for several decades and has been extensively validated over that time by numerous researchers. Five separate experiments using ToTEL-X all showed that both the reduction of load in response to optical stimuli and the gender difference were real. As a next step the technical team at Bevilacqua Research Corporation began a series of operational tests using real tasks to further validate these effects. In the first test the team added the optical stimuli to the sides of the display on an operational news website. The data, shown in figure 2 below shows the clear difference in time spent on the site that occurred when the stimuli was present and not present (Bevilacqua, Brown, Catron & Hembree, 2020). In these experiments the time-on-task is directly related to the level of cognitive load so a shorter time indicates a reduced load level.
This particular test, which measured the time spent by visitors (over 22,000 random sessions, both male and female) on a commercial news website, clearly shows that overall visitors spent less time (i.e., came to an understanding faster) when the optical stimuli was present. The difference in mean times, 1.58 seconds with no stimuli and 1.38 seconds with optical stimuli represents a 13% reduction in the time needed for visitors to understand the news content and move on. This is a significant difference however if we separate males and females, we see the real story behind these data. Figure 3 shows the mean time difference with and without stimuli present for males only and Figure 4 shows the times for females only.
These data represent the results from one operational test and clearly show both the effect of active cognitive load reduction using optical stimuli and the associated gender effect.
Implications for the Military
The leading-edge research that the BRC team has performed reveals some obvious implications for the military as follows:
- Active Cognitive Load Reduction can reduce a soldier’s CL levels significantly independent of the situation or environment and can be implemented much cheaper than methods that strive to optimize information presentation
- Although Active Cognitive Load reduction using optical stimuli could significantly reduce CL in males, it will increase it for females (NOTE: we are currently working on new forms of stimuli that work better for females)
- This technique can be used to enhance training, by allowing working memory to store schemas into long-term memory faster and deeper
- BRC has designed a physiological feedback system that will allow a soldier’s current cognitive state to be measured using a wearable appliance, thus allowing the optical stimulation to be optimized in real time. This keeps the soldiers cognitive load at a minimum at all times.
- Since mental fatigue is directly related to physical fatigue, soldiers will be able to operate at maximum efficiency longer, reducing errors and saving lives.
- As the inventor/discoverer of active cognitive load reduction, BRC holds all the patents for this solution. No other contractor can provide it to the Government.
It should be noted that the ACLR optical stimuli can be added to any computer display. In this configuration the approach is called Cognitive neuroframingTM. BRC has also developed prototype smart glasses that help to keep cognitive load low at all times (Figure 5).
It would be impossible to show the results of 7 years of advanced cognitive load research in a short blog article. The BRC human and Artificial Intelligence team is immediately available to do targeted briefings on Advanced Cognitive Load Reduction and to provide design and development support to add this disruptive capability to military and civilian systems. For more information or to request a briefing contact Dr. Andy Bevilacqua (256) 882-6229, ext. 102, email@example.com.
Bevilacqua, A., Paas, F., & Krigbaum, G. (2016). Effects of motion in the far peripheral visual field on cognitive test performance and cognitive load. Perceptual and Motor Skills, 122(2), 452-469. http://dx.doi.org/10.1177/0031512516633344tive
Bevilacqua, A. (2017). Commentary: Should gender differences be included in the evolutionary upgrade to cognitive load theory? Educational Psychology Review, 29(1), 189–194. https://doi.org/10.1007/s10648-016-9362-6.
Bevilacqua, A., Brown, R., Catron, C., & Hembree, G. (2020). Active Cognitive Load Reduction Using Optical Stimulation. In Work.
Bevilacqua, A., & Krigbaum, G. (2020). Reliability and Validity of the Time on Task Exogenous
Load Index (ToTEL-X) Tool for Cognitive Load Measurement in Complex Learning/Work Environments. Submitted for Publication.
Castro-Alonso, J., Wong, M., Adesope, O., Ayers, P., & Paas, F., (2019). Gender
Imbalance in Instructional Dynamic Versus Static Visualizations: a Meta-analysis. Educational Psychology Review. https://doi.org/10.1007/s10648-019-09469-1
DeLeeuw, K.E. & Mayer, R.E. (2008). A Comparison of Three Measures of Cognitive
Load: Evidence for Separable Measures of Intrinsic, Extraneous, and Germane Load. Journal of Educational Psychology, 100(1), 223-234.
De Waard, D., & Lewis-Evans, B. (2014). Self-report scales alone cannot capture mental workload. A reply to De Winter, Controversy in human factors constructs and the explosive use of the NASA TLX: a measurement perspective. Cognition, Technology, & Work, 16, 303-305. http://dx.doi.org/10.1007/s10111-014-0277-z
Geary, D. C. (2002). Principles of evolutionary educational psychology. Learning and Individual Differences, 12, 317-345. Retrieved from https://web.missouri.edu/~gearyd/GearyEvoEd.pdf
Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (task load index): Results of empirical and theoretical research. In P. A. Hancock, & N. Meshkati (Eds.), Human Mental Workload. Amsterdam: North Holland Press.
Paas, F., & Sweller, J. (2012). An evolutionary upgrade of cognitive load theory: Using the human motor system and collaboration to support the learning of complex cognitive tasks. Educational Psychology Review, 24, 27-45. http://dx.doi.org/10.1007/s10648-011-9179-2
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257-285.