Abstract
The coordination between continuously-controlled and discretely-initiated orienting movements is a fundamental problem in motor control. This coordination is vital for maintaining high-acuity vision of moving objects, which requires foveating the object’s image through continuous smooth pursuit and discretely triggered saccadic eye movements. However, the decision mechanism underlying this coordination remains inadequately characterized. During these coordinated eye movements, pursuit is rapidly initiated by retinal image velocity while intervening saccades are triggered with highly variable latency contingent on both retinal image velocity and position. Here we propose a new computational strategy simulating the decision mechanism triggering saccades during pursuit. In this model, future position error and its associated uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations of retinal image motion. This probabilistic prediction of position error enables the computation of the confidence that a saccade will be needed, triggering saccades upon accumulating confidence to a threshold. Saccade confidence corresponds to the relative certainty that the image is positioned outside the fovea (quantified through log probability ratio). This model predicts the occurrence and latency distributions of saccades across a range of step-ramp target trajectories and externally imposed sensory uncertainty levels (e.g. via Gaussian blurring of the visual target). We suggest this stochastic, predictive decision making process represents a fundamental principle for the neural coordination of movement.
