The role of familiarity and experience in the implementation of efficient visual search strategies

想象一下，在你的办公室里寻找你的钥匙。你可能会从扫描办公室的表面开始，比如你的办公桌、桌子和架子。然后你可以检查口袋、袋子和文件下面，直到你找到钥匙或放弃。这次搜索的效率有多高？你花了多少时间在你已经检查过的地方寻找，搜索了太长时间，或者在没有有用信息的地方寻找？在这个方案中，我们将搜索效率定义为眼球运动的比例，这些眼球运动指向可以容易地确定以提供新信息的位置。在办公室的例子中，一些表面将是空的，而另一些则堆满了书籍和报纸。如果你的钥匙在一个空表面的中间，你就已经知道它们在哪里了；直接查看这些位置不会获得任何新的信息。一个高效的搜索者会把他们的眼睛指向杂乱的区域，那里需要中央视觉。我们最近的研究使用这一指标来定义效率，发现了令人惊讶的大范围的个体策略，一些人是高效的，一些人是随机的，而另一些人是非常低效的。这些不同之处表明，与其问“搜索是最优的还是随机的？”我们应该问的是，在什么情况下，搜索是最优的还是随机的？这就是当前提案的目的。关于视觉信息如何在搜索过程中引导注意力，人们已经知道了很多。人们对搜索策略的了解要少得多，搜索策略对绩效衡量的影响要大得多。我们的关键假设是，策略上的个体差异至少部分可以通过对视觉内容和配置的体验差异来解释，即使(至少在我们的实验中)这些与最佳眼动是什么或实施有效策略的难度无关。为了评估这一假设，我们系统地测量了视觉内容、搜索阵列的布局、个人动机、学习和先前的专业知识对搜索效率的影响。理解策略是建立一个完整的视觉搜索模型的基础。这一结果对理解经验在形成策略中的作用具有重要意义，这种作用可能超越视觉搜索的背景。这些结果还可以用于设计促进更高效搜索的环境，以及开发能够更快、更准确地检测目标的培训计划。

Imagine searching your office for your keys. You will likely start by scanning surfaces in your office such as your desk, table, and shelves. You may then check pockets, bags, and underneath papers, until you either find the keys or give up. How efficient was this search? How much time did you waste looking in places you already inspected, searching an area for too long, or looking in places that contained no useful information? In this proposal, we define search efficiency as the proportion of eye movements that are directed to locations that can be easily ascertained to provide new information. In the office example, some surfaces will be empty, and some cluttered with books and papers. If your keys were in the middle of an empty surface, you would already know where they were; no new information would be gained by looking directly at these locations. An efficient searcher would instead direct their eyes to the cluttered regions, where central vision is needed. Our recent studies using this metric to define efficiency have found a surprisingly large range of individual strategies, with some people being highly efficient, some random, and some highly inefficient. These differences suggest that rather than asking "is search optimal or random?" we should be asking for whom, and in what circumstances, search is optimal or random. This is the aim of the current proposal. Much is already known about how visual information guides attention during search. Far less is known about search strategy, which contributes far more variance to performance measures. Our key hypothesis is that individual differences in strategy can be explained, at least in part, by differences in experience with the visual content and configuration, even though (in our experiments at least) these have no bearing on what the optimal eye movements are or the difficulty of implementing an efficient strategy. To assess this hypothesis, we systematically measure the effect on search efficiency of visual content, layout of the search array, individual motivation, learning, and prior expertise. Understanding strategy is fundamental to building a complete model of visual search. The results have implications for understanding the role of experience in shaping strategy that could have relevance beyond the context of visual search. The results can also be useful in designing environments that promote more efficient search, and developing training programs that can lead to faster and more accurate detection of targets.