In real-world scene perception, human observers generate sequences of fixations to move image patches into the high-acuity center of the visual field. Models of visual attention developed over the last 25 years aim to predict two-dimensional probabilities of gaze positions for a given image via saliency maps. Recently, progress has been made on models for the generation of scan paths under the constraints of saliency as well as attentional and oculomotor restrictions. Experimental research demonstrated that task constraints can have a strong impact on viewing behavior. Here, we propose a scan-path model for both fixation positions and fixation durations, which include influences of task instructions and interindividual differences. Based on an eye-movement experiment with four different task conditions, we estimated model parameters for each individual observer and task condition using a fully Bayesian dynamical modeling framework using a joint spatial–temporal likelihood approach with sequential estimation. Resulting parameter values demonstrate that model properties such as the attentional span are adjusted to task requirements. Posterior predictive checks indicate that our dynamical model can reproduce task differences in scan-path statistics across individual observers.

中文翻译：

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场景查看期间任务依赖性的动态扫描路径模型。

在现实世界的场景感知中，人类观察者生成注视序列以将图像块移动到视野的高敏锐度中心。过去 25 年开发的视觉注意力模型旨在通过显着图预测给定图像的注视位置的二维概率。最近，在显着性以及注意力和动眼神经限制的约束下生成扫描路径的模型取得了进展。实验研究表明，任务限制会对观看行为产生强烈影响。在这里，我们提出了一个针对注视位置和注视持续时间的扫描路径模型，其中包括任务指令的影响和个体差异。基于四种不同任务条件下的眼动实验，我们使用具有顺序估计的联合时空似然法的完全贝叶斯动力学建模框架来估计每个单独观察者和任务条件的模型参数。生成的参数值表明模型属性（例如注意力跨度）已根据任务要求进行了调整。后验预测检查表明我们的动态模型可以重现单个观察者扫描路径统计中的任务差异。