The problem of gaze allocation has previously been studied in the framework of eye-movement control models, which require prior knowledge of visibility maps (VMs). These encode the signal-to-noise ratio, at each point in the visual field, which can be used to define an optimal policy of gaze allocation. However, it is not always possible to estimate the VM, in a given experimental setting, as it depends on many factors, including the visual system of the individual observer. Hence, few eye-movement datasets include the corresponding VM estimates. This can be problematic for the analysis of certain clinical conditions, such as Age-related Macular Degeneration (AMD), which are associated with reduced sensitivity in the affected locations of the visual field. The corresponding VMs are highly idiosyncratic, and cannot be modeled by estimates obtained from healthy observers. We propose an algorithm for maximum likelihood VM estimation, working directly from eye-movement sequences. We apply this algorithm to two eye-tracking datasets, based on visual search tasks, obtained from AMD patients. We show that the inferred VMs are spatially consistent with the measured visual field sensitivities. We also show that simulations with the estimated VMs can account for the asymmetric distribution of saccade vectors, which is typical of AMD patients.

中文翻译：

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中心视力丧失后眼球运动的空间分布与最优视觉搜索策略一致

先前已经在眼球运动控制模型的框架中研究了凝视分配问题，这需要先验知识可见性图（VM）。这些编码了视野中每个点的信噪比，可用于定义注视分配的最佳策略。然而，在给定的实验环境中，并不总是可以估计 VM，因为它取决于许多因素，包括个体观察者的视觉系统。因此，很少有眼动数据集包含相应的 VM 估计。这对于分析某些临床状况可能是有问题的，例如年龄相关性黄斑变性 (AMD)，这与视野受影响位置的敏感性降低有关。相应的虚拟机是高度特殊的，并且不能通过从健康观察者获得的估计来建模。我们提出了一种最大似然 VM 估计算法，直接从眼动序列工作。我们将该算法应用于两个眼动追踪数据集，基于视觉搜索任务，从 AMD 患者获得。我们表明，推断的 VM 在空间上与测量的视野灵敏度一致。我们还表明，使用估计的 VM 进行的模拟可以解释眼跳向量的不对称分布，这是 AMD 患者的典型特征。我们表明，推断的 VM 在空间上与测量的视野灵敏度一致。我们还表明，使用估计的 VM 进行的模拟可以解释眼跳向量的不对称分布，这是 AMD 患者的典型特征。我们表明，推断的 VM 在空间上与测量的视野灵敏度一致。我们还表明，使用估计的 VM 进行的模拟可以解释眼跳向量的不对称分布，这是 AMD 患者的典型特征。