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Errors as a Means of Reducing Impulsive Food Choice
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Published on: June 5, 2016

A model of time estimation and error feedback in predictive timing behavior.

Wilsaan M Joiner1, Mark Shelhamer

  • 1Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA. joinerw@nei.nih.gov

Journal of Computational Neuroscience
|June 20, 2008
PubMed
Summary

This study introduces a closed-loop model for sensorimotor prediction in eye movements. The model demonstrates how motor timing adjusts based on recent performance errors within a specific temporal window, improving predictive tracking.

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Area of Science:

  • Neuroscience
  • Motor Control
  • Computational Neuroscience

Background:

  • Sensorimotor prediction involves performance adjustments based on experience.
  • Oculomotor tracking of visual targets reveals predictive eye movements (saccades).
  • Initiation errors in predictive tracking correlate within a temporal window, indicating feedback in timing behavior.

Purpose of the Study:

  • To propose and validate a closed-loop model for predictive motor timing.
  • To investigate how performance and errors influence timing adjustments.
  • To understand the role of a temporal correlation window in sensorimotor feedback.

Main Methods:

  • Developed a closed-loop model with an internal clock (integrate-to-threshold mechanism).
  • Model threshold adjusted by previous movement timing and error feedback.
  • Estimated correlation window size and applied the model to experimental paradigms.

Main Results:

  • Model replicates key predictive tracking behaviors: gradual shift from reaction to prediction, phase transitions, hysteresis, scalar property, and perturbation resilience.
  • Intertrial correlations of a specific form were observed.
  • Correlation window size increases with repeated tracking.

Conclusions:

  • Repetitive predictive motor timing is adjusted by performance and errors within a limited time range.
  • This time range expands with increased confidence from prior performance.
  • The model provides insights into the mechanisms of sensorimotor adaptation and learning.