As we navigate and behave in the world, we are constantly deciding, a few times per second, where to look next. The outcomes of these decisions in response to visual input are comparatively easy to measure as trajectories of eye movements, offering insight into many unconscious and conscious visual and cognitive processes. In this article, we review recent advances in predicting where we look. We focus on evaluating and comparing models: How can we consistently measure how well models predict eye movements, and how can we judge the contribution of different mechanisms? Probabilistic models facilitate a unified approach to fixation prediction that allows us to use explainable information explained to compare different models across different settings, such as static and video saliency, as well as scanpath prediction. We review how the large variety of saliency maps and scanpath models can be translated into this unifying framework, how much different factors contribute, and how we can select the most informative examples for model comparison. We conclude that the universal scale of information gain offers a powerful tool for the inspection of candidate mechanisms and experimental design that helps us understand the continual decision-making process that determines where we look.

中文翻译：

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预测视觉注视

当我们在世界上航行和行事时，我们每秒都会不断地决定下一步该去哪里。这些响应视觉输入的决策的结果相对容易测量为眼球运动轨迹，从而提供对许多无意识和有意识的视觉和认知过程的洞察。在本文中，我们回顾了预测我们的视线方向的最新进展。我们专注于评估和比较模型：如何一致地衡量模型预测眼球运动的效果，以及如何判断不同机制的贡献？概率模型促进了注视预测的统一方法，使我们能够使用解释的可解释信息来比较不同设置下的不同模型，例如静态和视频显着性以及扫描路径预测。我们回顾了如何将各种各样的显着图和扫描路径模型转化为这个统一的框架，不同的因素有多少贡献，以及我们如何选择信息最丰富的示例进行模型比较。我们的结论是，信息增益的普遍规模为检查候选机制和实验设计提供了强大的工具，帮助我们理解决定我们关注方向的持续决策过程。