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

点击翻译按钮获取中文摘要

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.