Analyzing Fitts' Law using Offline and Online Optimal Control with Motor Noise

TL;DR

This study investigates how the combined effects of signal-dependent motor noise and planning variability explain the speed-accuracy tradeoff in human reaching movements, using control theory models and comparing them to experimental data.

Contribution

It introduces a model incorporating both noise sources and demonstrates their joint role in the speed-accuracy tradeoff through offline and online control simulations.

Findings

Both models replicate human-like behavior in reaching tasks.

Speed-accuracy tradeoff arises from combined noise factors.

Online and offline control both exhibit the tradeoff.

Abstract

The cause of the speed-accuracy tradeoff (typically quantified via Fitts' Law) is a debated topic of interest in motor neuroscience, and is commonly studied using tools from control theory. Two prominent theories involve the presence of signal dependent motor noise and planning variability -- these factors are generally incorporated separately. In this work, we study how well the simultaneous presence of both factors explains the speed-accuracy tradeoff. A human arm reaching model is developed with bio-realistic signal dependent motor noise, and a Gaussian noise model is used to deterministically approximate the motor noise. Both offline trajectory optimization and online model predictive control are used to simulate the planning and execution of several different reaching tasks with varying target sizes and movement durations. These reaching trajectories are then compared to experimental human reaching data, revealing that both models produce behavior consistent with humans, and the speed-accuracy tradeoff is present in both online and offline control. These results suggest the speed-accuracy tradeoff is likely caused by a combination of these two factors, and also that it plays a role in both offline and online computation.