In this article, we cover each measure that Soma NPT monitors in order to demonstrate the importance of looking at more than one measure when analyzing cognitive data.

We will cover the following measures.

  • Reaction Time

  • Speed

  • Variation

  • Accuracy

  • RCS

  • RPE

  • RMF

  • RME

  • BPM

  • rMSSD

  • SDNN

  • VAS-F

  • Duration

  • Time

Cognitive Task Measure

Reaction Time

The most well-known cognitive measure is reaction time. While this is a vital measure, it may not fully capture whether an athlete has improved their cognitive performance. This is because reaction time can be highly variable.

For example, you can have two athletes, each with an average reaction time of 303ms but the way they obtained that average reaction time can be very different, as shown in the example below.

Athlete A

  • Consistent responses with low variation.

  • Slowest response - 10.8% from the mean.

Athlete B

  • Inconsistent responses with high variation.

  • Slowest response - 78% from the mean.

This example nicely shows that reaction time can be highly variable and even though both athletes have the same average reaction time it is clear that Athlete A performs more consistently than Athlete B.

Having deeper insights into an athlete's cognitive performance is important. This is why Soma Analytics gives coaches the ability to display trends of all cognitive measures with Minute on Minute (MoM) breakdowns in addition to the overall grand mean data for each cognitive task. By doing so, it enables coaches to see the mean data for the given task and also how variable an athlete is performing every minute of their cognitive training. In the examples below you can see the grand mean and MoM trend data of the athlete's reaction times over a 20-min task. You will notice one athlete is extremely variable from minute to minute and the other athlete is very consistent over the entire 20-min task.

Reaction Time MoM

Reaction Time Mean

Reaction Time MoM

Reaction Time Mean

To summarise reaction time is highly variable and on its own does not unambiguously and clearly indicate if the athlete has improved their cognitive performance.

Reaction Time is the amount of time it takes to respond to a stimulus, measured in milliseconds (thousands of a second).

Cognitive Task Measure


Speed is a statistical measurement. So when we look at it in Soma Analytics, Speed as a statistical measurement is calculated in order to make the data show a clearer trend. Speed normalises the data distribution and reduces the effect of outliers (numbers that differ significantly from other observations or data points) in the data. Normalising data distribution is important because without it there can be large variations in the data which can skew your interpretation of how well your athlete is doing. It is not possible to draw conclusions about your data when the trend is too variable.

In the speed data below, you can see the MoM changes are not as variable as in the reaction time graph above (the lines have less peaks). This is an example of how speed normalises the distribution making the data easier to interpret in terms of a tangible result, whether or not they are improving.

Speed MoM

How is speed calculated?

1000/Reaction Time (RT)

1000/RT leads to an interpretation of coefficients as additive changes in processing rate possibly tied to neural spike rates (e.g., Carpenter, 1981; Carpenter & Williams, 1995)

Speed normalises data distribution.

Cognitive Task Measure


The coefficient of variation (CV) is a statistical measure of the relative dispersion of data points in a data series around the mean. Variation is used to measure the degree of variation between testing trials in an individual athlete’s repeated measurements. This cognitive data point is the most important data point to identify changes in cognitive performance because variation tells us how consistent an athlete is. The lower the variation the more consistent the athlete is and the better their cognitive performance.

In the variation example below, you can see one athlete has high levels of variation minute on minute, while the other athlete has lower variation over the 20m cognitive task.

High Variation MoM

Low Variation MoM

High Variation (52%) Mean

Low Variation (21%) Mean

To summarise, variation gives you insights into real changes in cognitive performance and how consistent the athlete is performing.

Variation is used to measure the degree of variation between responses.

Cognitive Task Measure


Accuracy is a very straight forward measure. How accurately is your athlete performing.

Knowing how accurate your athlete is over the duration of the cognitive task and if and when accuracy starts to decline, gives you insight into the consistency of their cognitive performance. In the example below, you can see one athlete is more consistent over the duration of the task by analysing their time-varying MoM data trends.

Accuracy MoM

Accuracy MoM

To summarise, accuracy per minute and overall accuracy gives you insights into how your athlete is performing. For instance, these measures might help identify an athlete’s cognitive breaking points or show you how well they can sustain their cognitive performance.

Accuracy is the proportion of correct responses.

Cognitive Task Measure


Rate Correct Score (RCS) is the number of correct responses per second. RCS is a key measure that allows you to identify how fast your athlete can respond accurately per second. For instance, if your athlete performed a task with an RCS of 4, this would indicate they made 4 correct responses per second (i.e., one correct response every 250 ms). This measure is important because it takes the speed-accuracy trade-off into account; fast errorful responding would generate a low RCS, whereas fast error-free would generate a high RCS.

You will notice in the examples below that RCS is stable, however, it falls when athletes start to make mistakes, and rises when they eliminate mistakes.



To summarise RCS gives coaches and athletes important insights to how many correct responses they make per second. By looking for changes in RCS in the MoM data trends it is possible to see if an athlete has reached the limits of their cognitive capabilities.

A comparison of methods to combine speed and accuracy measures of performance: A rejoinder on the binning procedure

RCS is the number of correct responses per second.


Rating of Perceived Exertion

The rating of perceived exertion scale is used to assess an athlete's overall exertion during a cognitive task that captures how hard they feel they have been made to work. It is an integration of various central and peripheral feelings, such as sweating, breathing, and muscle tension.

This scale is useful for obtaining feedback from an athlete of how they are perceiving their exertion levels when performing a cognitive task. This scale is best used when the athlete is combining their physical and cognitive training.

Assess an athlete's perceived exertion during a cognitive task.


Rating of Mental Effort

The rating of mental effort scale is used to assess an athlete's mental effort during a cognitive task.

This scale is useful for getting feedback from the athlete about the perceived demands of a cognitive task. It captures the mental workload, or the amount of mental resources, demanded by a cognitive task.

Assess an athlete's mental effort during a cognitive task.


Rating of Mental Fatigue

The rating of mental fatigue scale is used to assess an athlete's level of mental fatigue or tiredness upon completing a cognitive task.

This scale is useful when you want to know the extent to which a cognitive task produces mental fatigue in your athlete. This is important because this temporary state of mental tiredness is associated with impaired performance.

Assess an athlete's mental fatigue levels after completing a cognitive task.


Physiological Measure

By monitoring an athlete's Heart Rate Variability (HRV) you can see the effect of each cognitive task (or cognitive training plan) on your athlete’s autonomic nervous system. This can enable you to see if you are under-loading (i.e., not creating enough mental stress) or over-loading (i.e., creating too much stress) so that you can titrate your athlete’s cognitive training load by increasing and decreasing task demands, respectively. HRV is a key indicator of workload and provides your with another data measure to help you optimise your athlete's cognitive training.

What are the advantages of Tracking Heart Rate (HR) as an athlete?

Heart rate is a quick and easy measure to check. Most athletes should know their own average resting heart rate and their maximum heart rate. Coupled with general subjective feelings of well-being, heart rate can indicate recovery, as well as how hard your athlete is working. If their heart rate is unusually high during a training session that is not normally particularly difficult, and the athlete is feeling a little flat, it suggests they are not recovered enough to push as hard usual, or suggests the need to incorporate recovery measures as part of training.

What are the advantages of Tracking HRV as an athlete?

HRV measurements can give more in-depth information and give you a heads up BEFORE your heart rate is affected and you start feeling flat. Particularly with athletes, it can be hard to tell the difference between mental fatigue and physical fatigue, not feeling like doing something today as opposed to having genuine limits on performance. HRV offers a means to capture how your nervous system is doing and thereby help head problems off at the pass.

  • How your HRV trends over weeks can help you identify if you’re recovering from session to session.

  • How your HRV trends over months can reveal how you are dealing with competition, stress, even if your performance should be improving.

It is very important to understand that regular and consistent measurements are required to give you a proper picture of HRV. You need to know your own TREND. As it is with many measurements when it comes to the body, a one-off snapshot is not always a great indicator.

In the example below you can see the athlete starts their cognitive training with a 67.95 rMMSD and after 5 minutes their rMMSD has declined to 46.35. This indicates that the task is likely to have caused parasympathetic nervous system withdrawal and sympathetic nervous system activation. This is typical and shows that the athlete is finding the cognitive training challenging.

In the second trend example, you can see from session 1 to session 3, the athletes HRV is higher even with the increase of task intensity (50% to 60%) This shows that the athlete is starting to adapt to the cognitive demands placed on their brain.

HRV gives insights to how well the athlete adapts to the stress of the cognitive training, ensures the cognitive training is well planned, and that the athlete is pushing themselves every session.

Cognitive functions need to be continually incrementally increased otherwise few gains are seen. (Bergman Nutley et al. 2011, Holmes et al. 2009, Klingberg et al. 2005).



HRV Trend

HRV Trend

A recent study exploring the link between sports performance and Brain Endurance Training (BET), has demonstrated significant improvements in cognitive abilities. Pre- and post-testing included a 5-minute physical task, followed by a mentally fatiguing task (20-minutes), followed by a repeat of the physical task. Compared to the control group with no training, the BET group improved cognitive performance, as seen by increased task performance with a reduction in mental fatigue and exertion. From only 5 weeks of computerised cognitive task training, lasting 20-minutes per session (20 total sessions), the BET group displayed an increase in heart rate variability, over time, post-training (P<0.05). This indicated less effort required for the physical tasks in post-testing.

Pre-Testing Post-Testing HRV

Cognitive training in the BET group showed a decline in Mental Exertion compared to the control group during the same 2-back test used in pre and post-testing (P=0.25). These values represent an overall improvement in mental exertion seen by a 30% decrease in the BET group compared to a 23% increase in the control.

Mental Exertion

Regarding cardiac activity, we noted a significant decrease in participant's average heart rate in the BET group during the physical and novel cognitive tasks compared to the control group. This finding may be explained by the fact that the BET group perceived the tasks as less demanding seen by the increase in heart rate variability, consistent with the concurrent method (C.Ring, N. Dallaway, S.J.E. Lucas, 2017). While heart rate variability is typically correlated with perceived exertion (Shortz et al.), an increased HRV is associated with greater self-control (Zahn et al., 2016) and enhanced performance (Smith et al., 2019).

Assess an athlete's physiological and cognitive state.

Physiological Measure


How many times your heart beats per minute.

Monitoring an athletes BPM can indicate how they are adapting to the cognitive stress over the course of the training plan.

Physiological Measure


rMSSD is the square root of the mean of the sum of squared successive differences between successive NN intervals (i.e., milliseconds between consecutive heartbeats).

Physiological Measure


SDNN is the standard deviation of all NN intervals. The SDNN describes a measure of the variability of a series of heartbeats. It reflects the influence on the heart of both the sympathetic and parasympathetic nervous systems.

🔗 Everything You Should Know About Heart Rate Variability (HRV)

🔗 Heart Rate Variability and Cognitive Function: A Systematic Review

🔗 Heart rate variability and cognitive processing: The autonomic response to task demands

🔗 Phasic heart rate variability and the association with cognitive performance

🔗 Heart Rate Variability and Decision-Making: Autonomic Responses in Making Decisions

🔗 Heart Rate Variability, Prefrontal Neural Function, and Cognitive Performance: The Neurovisceral Integration Perspective on Self-regulation, Adaptation, and Health

🔗 Mental fatigue and heart rate variability (HRV): The time-on-task effect.


Visual analog scale to evaluate fatigue severity (VAS-F).

The Visual Scale to evaluate fatigue severity has been used to monitor an athletes mental fatigue levels. This combined with all the other metrics available to you, can assist you with load management.

🔗 Comparing the Effects of Three Cognitive Tasks on Indicators of Mental Fatigue.


Duration of the task

When comparing data metrics it is important to compare the same durations of a cognitive task.


Time the task was completed

Knowing the time of the day the cognitive training was completed can give you insights into optimal timing windows for training. Your athlete might be a “morning” or “evening” person, and the scheduling of training might influence their performance.

Learn More

🔗 Cognitive Task Selection For Sports Performance.

🔗 How To Integrate Cognitive Training.

🔗 How To Manipulate Cognitive Load With Task Duration.

🔗 How To Manipulate Cognitive Load With Task Intensity.

🔗 How to Interpret your Athlete’s Cognitive Data.

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