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.

中文翻译：

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对预测位置误差的信心解释了追逐过程中的扫视决定

运动控制中的一个基本问题是协调互补运动类型以实现共同目标。作为一个常见的例子，人类通过协调追踪和扫视眼球运动来观察移动的物体。追踪由视网膜图像速度启动并持续控制。在追踪过程中，眼睛位置可能会落后于目标。这可以通过追赶扫视的离散执行来补偿。触发扫视的决定受位置和速度误差的影响，并且扫视的时间变化很大。观察到的跳视频率和触发时间的分布仍然知之甚少，而且这个决策过程仍然不准确地量化。在这里，我们提出了一个预测性的概率模型，解释了在追逐移动目标时触发跳视的决定。在这个模型中，预期位置误差及其相关不确定性是通过贝叶斯推理对嘈杂、延迟的感官观察（卡尔曼滤波）进行预测的。该概率预测用于估计需要扫视的置信度（通过对数概率比量化），在累积到固定阈值时触发扫视。该模型定性地解释了在跟踪一系列目标运动轨迹期间对扫视频率和触发时间分布的行为观察。此外，该模型做出了新颖的预测，即扫视决策对小预测位置误差的不确定性高度敏感，但这种影响会随着预测位置误差幅度的增加而减弱。我们建议这个预测，