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

想象一下，在你的办公室里找钥匙。你可能会从扫描办公室的表面开始，比如你的书桌、桌子和架子。然后你可能会检查口袋、包和文件下面，直到你找到钥匙或放弃。这个搜索的效率如何？你浪费了多少时间在你已经检查过的地方寻找，搜索一个区域太长时间，或者寻找没有有用信息的地方？在这个建议中，我们将搜索效率定义为指向容易确定的位置以提供新信息的眼球运动的比例。在办公室的例子中，有些表面是空的，有些表面堆满了书和文件。如果你的钥匙在一个空表面的中央，你已经知道它们在哪里；直接观察这些地点不会获得新的信息。一个高效的搜索者会把他们的眼睛引导到杂乱的区域，那里需要中央视觉。我们最近的研究使用这个指标来定义效率，发现了令人惊讶的大范围的个人策略，有些人效率很高，有些人随机，有些人效率很低。这些差异表明，我们不应该问“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。关于视觉信息在搜索过程中如何引导注意力，我们已经知道了很多。对搜索策略的了解要少得多，它对性能度量的影响要大得多。我们的关键假设是，策略上的个体差异可以通过视觉内容和配置的经验差异来解释，至少部分解释，尽管（至少在我们的实验中）这些差异与最佳眼球运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、搜索数组布局、个人动机、学习和先前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。研究结果对理解经验在塑造策略中的作用具有启示意义，这种影响可能超出视觉搜索的范围。研究结果还可以用于设计促进更有效搜索的环境，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.