In many important search tasks, observers must find what they are looking for using only visual information (e.g., X-ray baggage screening/medical screening). However, numerous other search tasks can only be effectively completed when the searcher uses their hands to find what they are looking for (e.g., "rummage" search). Unfortunately, it is not currently well understood how observers conduct such "interactive" searches nor what the best strategies might be for doing so. Here, we first review the limited literature on interactive search. We then present a novel methodology for the study of interactive search that involves having observers seek out LEGO® targets in a cluttered tray of assorted bricks. In our validation task, we confirm the validity of this approach by demonstrating that it produces sensible patterns of diminishing returns in response time as targets are removed from the set as well as hastened search times for larger targets. In our experiment, we modify the approach, refining its systematicity and experimental control. We also build on prior work exploring strategy use in visual search by investigating the extent to which active and passive strategy use impacts performance in interactive search. In contrast to our prior findings in hybrid visual search (Madrid & Hout, 2019), our current findings suggest that in interactive search, an active search strategy can be superior to a passive one. We close by offering a conceptual model (the Interactive Multiple Decision Model [i-MDM]) that explicates the steps involved in a search task of this nature, and we then provide suggestions for how to further refine the task to achieve higher internal validity and to delve deeper into questions of theoretical importance in the field of interactive search. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

中文翻译：

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检查在交互式搜索乐高®积木过程中使用被动和主动策略的效果。

在许多重要的搜索任务中，观察者必须仅使用视觉信息（例如，X 射线行李检查/医疗检查）找到他们正在寻找的内容。然而，许多其他的搜索任务只有在搜索者用手来寻找他们正在寻找的东西时才能有效地完成（例如，“翻找”搜索）。不幸的是，目前尚不清楚观察者如何进行这种“交互式”搜索，也不清楚这样做的最佳策略是什么。在这里，我们首先回顾了有限的交互式搜索文献。然后，我们提出了一种用于研究交互式搜索的新方法，该方法涉及让观察者在杂乱无章的各种积木托盘中寻找 LEGO® 目标。在我们的验证任务中，我们通过证明当目标从集合中移除以及加快搜索更大目标的时间时，它会在响应时间上产生合理的收益递减模式，从而证实了这种方法的有效性。在我们的实验中，我们修改了方法，改进了它的系统性和实验控制。我们还通过调查主动和被动策略使用对交互式搜索性能的影响程度来探索视觉搜索中策略使用的先前工作。与我们之前在混合视觉搜索中的发现（Madrid & Hout, 2019）相比，我们目前的发现表明，在交互式搜索中，主动搜索策略可能优于被动搜索策略。最后，我们提供了一个概念模型（交互式多决策模型 [i-MDM]），该模型解释了这种性质的搜索任务所涉及的步骤，然后，我们就如何进一步细化任务以实现更高的内部有效性并深入研究交互式搜索领域的理论重要性问题提供建议。（PsycInfo 数据库记录 (c) 2022 APA，保留所有权利）。