Are two heads better than one? Developing a model for predicting group benefits in joint visuospatial tasks

在日常生活中，人类经常一起执行感知任务以达到共同的目标（例如，当在一大群人中联合搜索朋友时）。在这些情况下，个人可以协作（例如，通过分配任务需求）以比单独执行任务时更好地执行任务（即，获得团体利益）。近年来，研究人员调查了影响是否以及在多大程度上获得群体利益的因素。然而，缺乏一个考虑到影响群体利益的若干因素的总体框架。针对这些问题，我打算设计一个更统一的框架来评估群体利益，其中包括影响群体利益的几个因素。此外，我的目标是解决可能影响群体利益的因素，但以前没有探索，并将它们纳入模型。基于这些目标，我打算进行三个研究方向。首先，我的目标是设计一个模型的群体利益，其中包括以前调查的因素，如绩效反馈，信息的合作者的行为，并在个人表演的相似性。通过这样一个模型，我的目标是了解这些群体利益预测因素的相对重要性。其次，我的目标是研究注意负荷的生理相关性的变化（即，学生人数）和群体利益是相互关联的。也就是说，我的目标是研究瞳孔大小在单独或联合执行联合视觉空间任务之间的差异，以及瞳孔大小如何随着时间的推移而变化，同时二人组设计协调策略（即，分工战略）。此外，我的目标是测试在多大程度上瞳孔大小可以预测一个整体模型中的群体利益的其他预测因素。第三，我的目标是调查是否二人组，执行分工的劳动力战略，这是符合他们的个人处理能力可以优于二人组执行一个典型的使用分工的劳动力战略。总而言之，这三个研究方向，我的目标是提供一个更完整的了解不同的因素，影响群体利益的联合视觉空间任务，解决文献中的几个空白。从本质上讲，我的目标是设计和扩展一个模型，预测集团的利益，考虑到几个因素，影响联合表现。建立这样一个模型将是一个重要的一步，不仅提高研究人员的联合视觉空间任务中的群体利益的理解，但也将使研究人员在联合视觉空间任务中的群体利益作出更精确的预测。由于人类在日常生活中经常合作执行感知任务，因此促进对影响群体利益的因素的理解也具有很高的实用价值。它可以帮助提升和预测需要感知处理的联合任务的表现，并有助于防止错误，从而降低事故的风险。

In daily life, humans often perform perceptual tasks together to reach a shared goal (e.g., when jointly searching for a friend in a large crowd). In these situations, individuals may collaborate (e.g., by distributing task demands) to perform the task better than when the task is performed alone (i.e., they attain a group benefit). In recent years, researchers investigated factors influencing if, and to what extent, a group benefit is attained. Yet, an overall framework, which considers several factors influencing group benefits is missing. Addressing these issue, I intend to devise a more unified framework to assess group benefits which includes several factors influencing group benefits. Moreover, I aim to address factors that may influence group benefits but have not been explored before and incorporate them in the model. With these goals in mind, I intend to pursue three research directions. First, I aim to devise a model of group benefits, which includes previously investigated factors such as performance feedback, information about the performed actions of co-actors, and similarities in the individual performances. With such a model, I aim to understand the relative importance of these predictors of group benefits. Second, I aim to investigate how changes of a physiological correlate of attentional load (i.e., pupil sizes) and group benefits are interrelated. That is, I aim to investigate how pupil sizes differ between performing a joint visuospatial task alone or jointly and how pupil sizes change over time while dyads devise coordination strategies (i.e., division of labour strategies). Moreover, I aim to test to what extent pupil sizes can predict a group benefit on top of other predictors of group benefits in an overall model. Third, I aim to investigate whether dyads that perform division of labour strategies which are in line with their individual processing capacities can outperform dyads performing a typically used division of labour strategy. Taken together, with these three research directions, I aim to provide a more complete understanding of the different factors that influence group benefits in joint visuospatial tasks, addressing several gaps in the literature. In essence, I aim to devise and extend a model for predicting group benefits which takes into account several factors that influence joint performance. Building such a model will be an important step not only to improve researchers’ understanding of group benefits in joint visuospatial tasks but will also enable researchers to make more precise predictions of group benefits in joint visuospatial tasks. As humans frequently perform perceptual tasks collaboratively in daily life, fostering understanding of the factors that influence group benefits has also a high practical value. It can help to elevate and predict performance in joint tasks requiring perceptual processing as well as help to prevent errors, thereby reducing the risk of accidents.