Problem solving is a composite cognitive process, invoking a number of systems and subsystems, such as perception and memory. Individuals may form collectives to solve a given problem together, in collaboration, especially when complexity is thought to be high. To determine if and when collaborative problem solving is desired, we must quantify collaboration first. For this, we investigate the practical virtue of collaborative problem solving. Using visual graph analysis, we perform a study with 72 participants in two countries and three languages. We compare ad hoc pairs to individuals and nominal pairs, solving two different tasks on graphs in visuospatial mixed reality. The average collaborating pair does not outdo its nominal counterpart, but it does have a significant trade-off against the individual: an ad hoc pair uses 1.46 more time to achieve 4.6 higher accuracy. We also use the concept of task instance complexity to quantify differences in complexity. As task instance complexity increases, these differences largely scale, though with two notable exceptions. With this study we show the importance of using nominal groups as benchmark in collaborative virtual environments research. We conclude that a mixed reality environment does not automatically imply superior collaboration.

翻译：问题解决是一种复合认知过程，涉及感知与记忆等多个系统及子系统。当问题复杂度较高时，个体可能通过协作形成集体以共同解决问题。为判定协作问题解决的必要性及适用条件，首先需对协作进行量化。为此，本研究通过视觉图分析探究协作问题解决的实际效能。我们在两个国家以三种语言对72名参与者展开实验，比较临时组对、独立个体及名义组对在视觉空间混合现实中解决两类图论任务的表现。实验结果表明：平均而言，协作组对虽未超越其名义对照组，但与独立个体存在显著权衡关系——临时组对需多耗费1.46倍时间以换取4.6%的准确率提升。本研究进一步采用任务实例复杂度概念量化复杂度差异，发现随着任务实例复杂度增加，这些差异基本呈比例扩大（存在两个显著例外）。通过本实验，我们论证了在协作虚拟环境研究中采用名义组作为基准的重要性，并得出结论：混合现实环境并不必然催生更优的协作效能。