The learning effect is a phenomenon observed in many areas of testing and research that surround human performance with a physical or cognitive capacity. In this article we’ll explain the learning effect, when it can become an issue and how to account for it.
What is the learning effect in psychology & motor control?
The learning effect in psychology and motor learning relates to the quick improvements we see in performance when a learner makes their first 5-10 attempts at a new task. These quick initial jumps in performance get smaller and smaller as the learner becomes familiar with the task.
Why is the learning effect a problem?
Often in human sciences, we are interested in measuring performance. We may have one test we want athletes to complete over time to measure their performance gains. Or we may want to measure the effectiveness of an intervention, such as the use of imagery for improving a player’s maximum force production.
To measure the effectiveness of our work we often track performance in a task or a test that the athlete may not have completed before. The learning effect states that the very act of re-testing an athlete will see them improve, meaning we falsely believe their progress is due to our intervention.
The learning effect is not always a problem, sometimes we want to capture learning such as during contextual interference experiments. But the learning effect can be an issue if this is not the variable we are interested in measuring.
Learning effect example
For example, the vertical jump test aims to assess lower-body power in athletes. However, the first 10 jumps an athlete makes (if given adequate rest) will likely see an improvement in jumping performance. This doesn’t mean the athlete has become more powerful, but rather, they have learned a more effective coordination pattern to produce force.
A second example would be the Illinois agility test, aimed to measure change of direction. The first few runs will see any athlete improve their performance – this does not mean their agility has improved, just that they have learned a better way to problem solve the specific task at hand.
Why is the learning effect important?
If not accounted for, the learning effect can give the impression that an intervention or training was effective, when in fact, the change observed was due to this basic human phenomenon of learning, not the intervention in place.
Sometimes, you want the learning effect to be part of your data, such as finding out the best practice method for beginner golfers learning to putt. Other times you want to remove the learning effect from your data, such as the examples above where we are interested in measuring changes in lower body power and change of direction ability in athletes over time.
How to account for the learning effect in sport?
The best ways to account for the learning effect are:
- To include a control group who do not take part in the intervention. The mean increase in the control group tells you how much learning effect was present in your data.
- If the above is not possible. Ensure the athelte has mulitple attempts at a task before data is collected. This will not completely remove the learning effect, but minimise its presence.
Researchers, sport scientists, coaches and athletes should all have an understanding of the learning effect. When testing athletes over time, or researching variables related to human movement, consider if the learning effect will show up in your data and if this is an issue.
Where possible include a control group. When this is not possible, ensure familiarisation trials are given and standardised across all testing sessions.
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Will is a sport scientist and golf professional who specialises in motor control and motor learning. Will lecturers part-time in motor control and biomechanics, runs Golf Insider UK and consults elite athletes who are interested in optimising their training and performance.