When analyzing measures of cognitive task performance it is important to understand what you are looking at. By calculating multiple cognitive measures we are able to paint a complete picture of how the athlete is doing. This gives us plenty of information to evaluate and compare baseline test performance. For example, you will notice that not all measures improve between two sets of baseline tests. This is typical with most athletes. We believe that the more measures you have about an individual’s performance, the more important it is to put these measures into context and understand what they mean as part of a bigger picture, namely, that athlete’s learning journey. In sum, you can use the measures you collect to create mental performance and learning profiles of your athletes. Each athlete’s cognitive performance can be represented by a triangular profile that reflects their speed, accuracy, and consistency.
For example, you may have an athlete who, between tests, is performing faster in their cognitive training and evaluation program, but with a higher variation. This is a fast but inconsistent profile. This is not the desired outcome of their cognitive training plan. It is great to see that the athlete is performing faster, but in this case, the benefit comes at a cost, namely, they are losing consistency. You may wish to take steps (e.g., implement special training modes) to correct this speed-consistency trade-off.
In contrast, you may also have an athlete who is performing slightly slower, but their variation has improved by 40% coupled with an increase in accuracy of 20%. This is the profile of an athlete who is more accurate and more consistent. Armed with this information, you can begin to work on improving their processing speed.
So we need some threshold or ballpark performance metrics to help decide if the coach should intervene with their athlete. In other words, how much speed can be sacrificed in order to be more consistent and accurate?
We do not want athletes to perform super slow for the sake of being 100% correct.
We also do not want athletes to perform so fast that they make lots of mistakes and are highly variable with their responses.
Either one of the above case profiles also reduces the cognitive load on the athlete. It is the combination of maintaining accuracy, consistency, and speed that makes the task cognitively demanding for the brain. The whole "something’s gotta give" scenario applies here. If something gives, either speed or accuracy or variation, not only are the data going to show less improvement in those areas, they will also indicate that the athlete was reducing their cognitive workload, thereby taking training too easy.
This profile-based analysis provides you with the rationale to focus on improving the other areas when you adjust your athlete’s training program. You want the athlete to maintain the improvements they made in performance accuracy and variation, while you challenge them to get quicker. How can this be achieved? By adjusting the program for the next mesocycle and with the addition of specialised training modes.
Frequently, high-level athletes improve across the board. However, it is good to know what to do and what it means when you encounter data-based performance profiles like the below examples.
To make it easier to see improvements at a glance, we have included a colour legend next to each cognitive measure.
Cognitive performance has improved
No change in cognitive performance.
Cognitive Performance has declined.
In the below example, you can see that most cognitive measures improved. This is indicated by the purple dot next to the percentage of change. You can also see some cognitive measures got worse.
Sustained attention accuracy improved by 22% and variation improved by 25%, showing that the athlete is more consistent with their responses and with higher accuracy. Yes, it is true that the reaction time, speed, and RCS were slower but as these changes in performance are within the 5% threshold of change we consider overall that the increase in accuracy and variation is a positive outcome for this cognitive task.
In task-switching, you can see most metrics improved, showing that the athlete improved their processing speed, consistency, and accuracy. The variation was only slightly improved and this should be noted as something to focus on in the next training program, in order to increase the athlete's consistency when faced with this cognitive demand.
At the bottom of the baseline comparison, Soma Analytics takes all data into consideration and gives you an overall baseline comparison. This allows you to give the athlete the big picture and also to break down each cognitive task/demand.
In the below example, you can see most tasks across most metrics improved. In only two tasks, there was a slight decrease in performance accuracy and variation. This shows that the athlete has sacrificed speed for accuracy and consistency. This can be noted down and in the next mesocycle you can apply specialised training modes to focus on increasing such an athlete's error detection and consistency of responses
In this situation, we would recommend applying EDM mode to improve an athlete's error detection.
In a perfect world, the ideal cognitive performance data would look like this,
Reaction time Decreased
The most important thing to note here is that performance data are rarely going to be perfect. Understanding what the data are showing you in various situations gives you the power to make the right adjustments to the next program. In conclusion, Somas Data Analytics gives you the power to understand your athlete’s cognitive performance triangle and in so doing can help you to develop and evolve bespoke training plans to optimize their mental game.