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Related Experiment Video

Updated: May 17, 2026

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
10:39

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Published on: May 3, 2018

Dynamic sensorimotor planning during long-term sequence learning: the role of variability, response chunking and

Timothy Verstynen1, Jeff Phillips, Emily Braun

  • 1Learning Research and Development Center, University of Pittsburgh, Pennsylvania, United States of America. timothyv@gmail.com

Plos One
|October 12, 2012
PubMed
Summary

Long-term skill learning involves binding actions into sequences. This study shows how action planning, response speed, accuracy, and chunking dynamically change over two weeks of practice.

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Area of Science:

  • Cognitive Neuroscience
  • Motor Learning
  • Human Motor Control

Background:

  • Everyday skills are acquired by integrating discrete actions into sequential responses over extended periods.
  • Understanding the dynamics of action planning and response binding during long-term learning is crucial for skill acquisition.

Purpose of the Study:

  • To investigate how action planning and response binding dynamics evolve over long timescales (weeks) during skill acquisition.
  • To examine the relationship between learning rates for speed, accuracy, and response chunking and underlying planning processes.

Main Methods:

  • A bimanual serial reaction time task (32-item sequence) was administered to 23 subjects over 10 days.
  • Response times, accuracy, response time variability, and response chunking were measured.
  • A state-space model was employed to analyze predictive and error-corrective planning processes.

Main Results:

  • Response times and accuracy improved over training, but at different rates.
  • Faster learners exhibited increased response time variability early in training, followed by decreased variability.
  • Response chunking increased, with responses becoming temporally correlated and asymptoting to approximately 7 bound responses.
  • Non-monotonic associations were found between state-space model parameters and learning rates for speed, accuracy, and chunking.

Conclusions:

  • Long-term sequence learning involves dynamic modulation of response speed, variability, accuracy, and chunking.
  • Different aspects of the response planning process are relevant at distinct stages of long-term skill acquisition.
  • These findings provide insights into how the brain binds multiple movements into unified sequences during practice.