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Intermittent control: a computational theory of human control.

Peter Gawthrop1, Ian Loram, Martin Lakie

  • 1School of Engineering, University of Glasgow, UK. Peter.Gawthrop@glasgow.ac.uk

Biological Cybernetics
|February 18, 2011
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Summary
This summary is machine-generated.

Event-driven intermittent control explains human motor control better than continuous models by incorporating feedback delays and response variations. This framework, "continuous observation, intermittent action," reconciles existing theories and explains complex behaviors.

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Area of Science:

  • Human motor control
  • Cybernetics
  • Control theory

Background:

  • Continuous control models advance human motor control understanding.
  • Existing models overlook intermittent feedback, triggered responses, and refractory periods.
  • Human control systems involve significant time delays.

Purpose of the Study:

  • To present event-driven intermittent control as a unifying framework for human motor control.
  • To explain phenomena not accounted for by continuous control models.
  • To demonstrate the biological-cybernetic advantages of intermittent control.

Main Methods:

  • Developed an event-driven intermittent controller based on a continuous-time predictive control model.
  • Incorporated features like sampling, system matched hold, intermittent prediction, and event triggering.
  • Modeled human operator behavior under various conditions, including double stimulus tracking and quiet standing.

Main Results:

  • Intermittent control explains a wider range of human operator behaviors than continuous control.
  • The model naturally explains refractoriness and bimodal stabilization distributions.
  • Under specific conditions (small thresholds, regular sampling), intermittent control mimics continuous control.

Conclusions:

  • Event-driven intermittent control offers a more comprehensive framework for human motor control.
  • This approach reconciles continuous and intermittent control hypotheses.
  • Intermittent predictive control is computationally advantageous for systems with time delays.