In this paper, we study the problem of an optimal oculomotor control during the execution of visual search tasks. We introduce a computational model of human eye movements, which takes into account various constraints of the human visual and oculomotor systems. In the model, the choice of the subsequent fixation location is posed as a problem of a stochastic optimal control, which relies on reinforcement learning methods. We show that if biological constraints are taken into account, the trajectories simulated under a learned policy share both basic statistical properties and a scaling behavior with human eye movements. We validated our model simulations with human psychophysical eye-tracking experiments.

中文翻译：

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视觉搜索期间眼动的最佳控制

在本文中，我们研究了视觉搜索任务执行过程中的最佳动眼神经控制问题。我们引入了人眼运动的计算模型，该模型考虑了人类视觉和动眼系统的各种约束。在该模型中，后续注视位置的选择被视为一个随机最优控制问题，它依赖于强化学习方法。我们表明，如果考虑生物约束，在学习策略下模拟的轨迹共享基本统计特性和人眼运动的缩放行为。我们通过人类心理物理眼动追踪实验验证了我们的模型模拟。