Factorization of Force and Timing in Sensorimotor Performance: Long-Range Correlation Properties of Two Different
Ramesh Balasubramaniam1, Michael J Hove2, Butovens Médé1
1Cognitive & Information Sciences, University of California, Merced.
Topics in Cognitive Science
|October 25, 2017
Summary
Long-range correlations in neural and behavioral data reveal individual differences. This study found that timing and force production in repetitive movements showed independent long-range fluctuations, suggesting flexible system organization.
Area of Science:
- Neuroscience
- Cognitive Science
- Complex Systems
Background:
- Long-range correlations, often appearing as 1/fβ noise, characterize neural and behavioral variability.
- These correlations reflect individual differences and task performance variations.
- Sensorimotor tasks like tapping influence these correlations, which are task-specific.
Purpose of the Study:
- To investigate if simultaneously controlled behavioral variables exhibit differentially organized long-range fluctuations.
- To determine if timing and force production in repetitive movements show correlated long-range correlations within a single participant.
Main Methods:
- 13 participants performed repetitive finger movements, controlling both timing (500 ms intervals) and force (8 N).
- Synchronization-continuation paradigm used without external metronome or force feedback.
- Cross recurrence quantification analysis (CRQA) applied to timing and force data.
Main Results:
- High reproducibility of long-range correlations for timing and force separately across trials.
- No significant correlation found between long-range fluctuations of timing and force for individual participants.
- CRQA indicated limited shared structure between timing and force time series.
Conclusions:
- Complex systems can independently organize multiple processes while maintaining internal reliability.
- Sensorimotor control exhibits flexible organization, with timing and force fluctuations being relatively independent.
- Findings contribute to understanding the self-organization principles in biological systems.


