Role of Constant, Random and Blocked Practice in an Electromyography-Based Oral Motor Learning Task

ABSTRACT

Purpose: The role of principles of motor learning (PMLs) in speech has received much attention in the past decade. Oral motor learning, however, has not received similar consideration. This study evaluated the role of three practice conditions in an oral motor tracking task.

Method: Forty-five healthy adult participants were randomly and equally assigned to one of three practice conditions (constant, blocked, and random) and participated in an electromyography-based task. The study consisted of four sessions, at one session a day for four consecutive days. The first three days sessions included a practice phase, with immediate visual feedback, and an immediate retention phase, without visual feedback. The fourth session did not include practice, but only delayed retention testing, lasting 10–15 minutes, without visual feedback.

Results: Random group participants performed better than participants in constant and blocked practice conditions on all the four days. Constant group participants demonstrated superior learning over blocked group participants only on day 4.

Conclusion: Findings indicate that random practice facilitates oral motor learning, which is in line with limb/speech motor learning literature. Future research should systematically investigate the outcomes of random practice as a function of different oral and speech-based tasks.

Keywords:

• Practice
• Electromyography
• Acquisition
• Retention

DECLARATION OF INTEREST

Participants in this study were compensated from a grant funded by the NSF-REU program.

ACKNOWLEDGMENT

The authors would like to thank the participants and student researchers who were involved in data collection and analysis.

Notes

1. The practice amount for participants in each of the three practice conditions was based on previous studies related to speech and oral motor learning.

2. To convert participants' EMG data from mV to percentage, three mathematical operations were performed. First, the amplitude of EMG signal was subtracted from its offset, following this it was divided by the MVC value, and finally it was multiplied by 100.