Optimizing External Focus of Attention, Nobel Prizes, and Torturing Data
📝 Weekly paper summary
Optimizing External Focus of Attention Instructions: The Role of Attainability (Coker, 2016)
Category
Repeated Measures
Context
A plethora of work outlines the differences in performance when adopting an internal versus external focus of attention. According to the Constrained-Action Hypothesis, the benefits in performance from adopting an external focus of attention are because it reduces conscious interference and "allows the motor system to more naturally self-organize." Researchers have proposed one hypothesis that if the distance between an action and its remote effect is further, it is easier for the person to distinguish effects from body movements. For example, focusing more on the ball flight after it left the clubface promotes superior golfing performance relative to keeping the clubface square on impact (the former being a relatively further external focus of attention). However, limited data existed about how far one can prompt an external focus from the performer and still be beneficial. Similar to setting SMART goals, it may be necessary that the distance one focuses their attention externally is still attainable.
Therefore, this study aimed to examine the role that attainability of the external focus of attention plays in enhancing performance. Specifically, the authors tested broad jump performance under three conditions:
- Internal focus of attention ("extend your knees as fast as possible")
- External focus of attention-near/attainable ("jump as close as possible to the cone" [placed at their best broad jump distance based on a pre-test])
- External focus of attention-far/unattainable ("jump as close as possible to the cone" [placed 3m in front of them])
Correctness
Contributions
- External focus of attention-near/attainable > External focus of attention-far/unattainable > internal focus of attention
- Individualizing the distance of an external focus of attention instruction according to one's current capacity maximizes the benefit of that instruction
🧠 Fun fact of the week
Marie Curie is the only person to earn a Nobel prize in two different sciences. She won her Nobel in Physics for spontaneous radiation and Chemistry for her radioactivity work. Truly a phenomenal scientist!
🎙 Podcast recommendation
Machine learning techniques are becoming more popular in biomechanics and motor control research. Dr. Graham provides an overview of how researchers are currently leveraging these techniques to further our understanding of human movement.
🗣 Quote of the week
"If your torture the data long enough, it will confess to anything."
- Ronald H. Coase