@article{ref1,
title="Confidence in predicted position error explains saccadic decisions during pursuit",
journal="Journal of Neurophysiology",
year="2020",
author="Coutinho, Jonathan D. and Lefevre, Philippe and Blohm, Gunnar",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="A fundamental problem in motor control is the coordination of complementary movement  types to achieve a common goal. As a common example, humans view moving objects  through coordinated pursuit and saccadic eye movements. Pursuit is initiated and  continuously controlled by retinal image velocity. During pursuit, eye position may  lag behind the target. This can be compensated by the discrete execution of a  catch-up saccade. The decision to trigger a saccade is influenced by both position  and velocity errors and the timing of saccades can be highly variable. The observed  distributions of saccade frequency and trigger time remain poorly understood and  this decision process remains imprecisely quantified. Here we propose a predictive,  probabilistic model explaining the decision to trigger saccades during pursuit to  foveate moving targets. In this model, expected position error and its associated  uncertainty are predicted through Bayesian inference across noisy, delayed sensory  observations (Kalman filtering). This probabilistic prediction is used to estimate  the confidence that a saccade is needed (quantified through log-probability ratio),  triggering a saccade upon accumulating to a fixed threshold. The model qualitatively  explains behavioural observations on the frequency and trigger time distributions of  saccades during pursuit over a range of target motion trajectories. Furthermore,  this model makes novel predictions that saccade decisions are highly sensitive to  uncertainty for small predicted position errors, but this influence diminishes as  the magnitude of predicted position error increases. We suggest that this  predictive, confidence-based decision making strategy represents a fundamental  principle for the probabilistic neural control of coordinated movements.<p /> <p>Language: en</p>",
language="en",
issn="0022-3077",
doi="10.1152/jn.00492.2019",
url="http://dx.doi.org/10.1152/jn.00492.2019"
}