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Performance Gains in an Open Skill Video-Game Task: The Role of Neural Efficiency and Neural Proficiency
Edson Filho1, Tammy-Ann Husselman2, Luca Zugic2
1Wheelock College of Education & Human Development, Boston University, 2 Silber Way, 02215, Boston, MA, United States. efilho@bu.edu.
Applied Psychophysiology and Biofeedback
|June 10, 2022
Summary
Practicing video games improves performance but decreases attention, supporting the neural proficiency hypothesis. This suggests neurofeedback should enhance task-relevant brain networks for motor learning.
Area of Science:
- Neuroscience
- Cognitive Psychology
- Human-Computer Interaction
Background:
- Video game practice can enhance cognitive functions.
- Understanding the neural mechanisms of skill acquisition is crucial.
- Distinguishing between neural efficiency and neural proficiency hypotheses is key for motor learning research.
Purpose of the Study:
- To investigate the effects of open-skill video game practice on performance, attention, motivation, perceived effort, and brain activity (theta, alpha, beta waves).
- To determine whether performance gains are explained by neural efficiency (cortical idling) or neural proficiency (mixed cortical activation).
Main Methods:
- 16 novice participants played a Nintendo Wii video game (Link's Crossbow Training).
- EEG data was recorded continuously using the EEGO System.
- Performance, attention, motivation, and perceived effort were assessed pre- and post-practice.
Main Results:
- Performance significantly increased, while attention decreased post-practice, indicating reduced attentional overload.
- No significant changes were observed in motivation or perceived effort.
- EEG analysis showed increased cortical activity across frequency bands, supporting the neural proficiency hypothesis.
Conclusions:
- Video game practice enhances performance and reduces attentional demands.
- Performance gains in open-skill tasks are primarily driven by neural proficiency, not just efficiency.
- Neurofeedback interventions should focus on amplifying task-relevant neural networks for optimal motor learning.

