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Updated: May 20, 2026
10:39
The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
Published on: May 3, 2018
A generative model for measuring latent timing structure in motor sequences
Christopher M Glaze1, Todd W Troyer
1Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America. cglaze@sas.upenn.edu
Plos One
|July 21, 2012
Summary
We developed a statistical model to dissect motor timing variability into global tempo, neural noise, and peripheral jitter. This model quantifies distinct timing components in action sequences, applicable to various systems.
Area of Science:
- Neuroscience
- Computational Biology
- Behavioral Science
Background:
- Motor variability arises from diverse neural and peripheral sources across timescales.
- Understanding these sources is crucial for deciphering complex motor control.
Purpose of the Study:
- To present a statistical model for quantifying distinct components of timing variability in action sequences.
- To apply this model to analyze temporal structure in zebra finch song.
Main Methods:
- Developed a statistical model to measure global tempo changes, independent timing noise, and timing jitter.
- Applied maximum likelihood estimation for trial-to-trial factor assignment.
- Validated the model with artificially generated data.
Main Results:
- Zebra finch syllables exhibit roughly equal variability from global tempo, neural noise, and jitter.
- Overall song length is primarily influenced by global tempo changes.
- Global and independent variability scale with syllable length, unlike timing jitter.
Conclusions:
- The model successfully separates distinct sources of timing variability in action sequences.
- Findings are consistent with established models of sequence timing.
- The framework offers broad applicability to neural and behavioral data analysis.
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