Predicting group benefits in joint multiple object tracking

In everyday life, people often work together to accomplish a joint goal. Often in such tasks working together results in a higher performance compared to working alone – a so-called "group benefit". While several factors influencing group benefits have already been investigated in a range of tasks, to date they have not been examined collectively with an integrative statistical approach such as linear modeling. To address this gap in the literature, we investigated several factors that are highly relevant for group benefits and used these factors as predictors in a linear model to predict group benefits. We found that predictors collectively account for half of the variance and make non-redundant contributions towards predicting group benefits, suggesting that they independently influence group benefits. The model also accurately predicts group benefits, suggesting that it could be used to predict group benefits for individuals that have not yet performed a joint task together. Given that the investigated factors are relevant for other joint tasks, our model provides a first step towards developing a more general model for predicting group benefits across several joint tasks.