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，当在一大群人中共同寻找朋友时）。在这些情况下，个体可能会合作（例如，通过分配任务需求）来更好地执行任务，而不是单独执行任务（即，他们获得群体利益）。近年来，研究人员调查了影响群体利益的因素，以及群体利益在多大程度上得以实现。然而，一个考虑到影响群体利益的几个因素的整体框架是缺失的。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但之前未被探索的因素，并将其纳入模型。基于这些目标，我打算追求三个研究方向。首先，我的目标是设计一个群体利益模型，其中包括先前研究过的因素，如绩效反馈、合作演员所做动作的信息和个人绩效的相似性。有了这样一个模型，我的目标是理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力负荷（即瞳孔大小）和群体利益的生理相关变化是如何相互关联的。也就是说，我的目的是研究单独或共同执行联合视觉空间任务时瞳孔大小的差异，以及当两个人设计协调策略（即劳动分工策略）时瞳孔大小如何随时间变化。此外，我的目标是测试在一个整体模型中，瞳孔大小在多大程度上可以预测群体利益，而不是其他群体利益的预测因素。第三，我的目的是调查执行与其个人处理能力相一致的劳动分工策略的二元组是否可以优于执行通常使用的劳动分工策略的二元组。综上所述，通过这三个研究方向，我的目标是更全面地了解影响群体在联合视觉空间任务中获益的不同因素，解决文献中的几个空白。本质上，我的目标是设计并扩展一个模型，用于预测群体利益，该模型考虑了影响联合绩效的几个因素。建立这样的模型不仅是提高研究者对联合视觉空间任务群体利益理解的重要一步，也将使研究者能够对联合视觉空间任务群体利益做出更精确的预测。由于人类在日常生活中经常协同执行感知任务，促进对影响群体利益因素的理解也具有很高的实用价值。它可以帮助提高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.