Coordination effort in joint action is reflected in pupil size

A bonus task boosts people's willingness to offload cognition to an algorithm

With the increased sophistication of technology, humans have the possibility to offload a variety of tasks to algorithms. Here, we investigated whether the extent to which people are willing to offload an attentionally demanding task to an algorithm is modulated by the availability of a bonus task and by the knowledge about the algorithm's capacity. Participants performed a multiple object tracking (MOT) task which required them to visually track targets on a screen. Participants could offload an unlimited number of targets to a "computer partner". If participants decided to offload the entire task to the computer, they could instead perform a bonus task which resulted in additional financial gain-however, this gain was conditional on a high performance accuracy in the MOT task. Thus, participants should only offload the entire task if they trusted the computer to perform accurately. We found that participants were significantly more willing to completely offload the task if they were informed beforehand that the computer's accuracy was flawless (Experiment 1 vs. 2). Participants' offloading behavior was not significantly affected by whether the bonus task was incentivized or not (Experiment 2 vs. 3). These results combined with those from our previous study (Wahn et al. in PLoS ONE 18:e0286102, 2023), which did not include a bonus task but was identical otherwise, show that the human willingness to offload an attentionally demanding task to an algorithm is considerably boosted by the availability of a bonus task-even if not incentivized-and by the knowledge about the algorithm's capacity.

Offloading under cognitive load: Humans are willing to offload parts of an attentionally demanding task to an algorithm

In the near future, humans will increasingly be required to offload tasks to artificial systems to facilitate daily as well as professional activities. Yet, research has shown that humans are often averse to offloading tasks to algorithms (so-called "algorithmic aversion"). In the present study, we asked whether this aversion is also present when humans act under high cognitive load. Participants performed an attentionally demanding task (a multiple object tracking (MOT) task), which required them to track a subset of moving targets among distractors on a computer screen. Participants first performed the MOT task alone (Solo condition) and were then given the option to offload an unlimited number of targets to a computer partner (Joint condition). We found that participants significantly offloaded some (but not all) targets to the computer partner, thereby improving their individual tracking accuracy (Experiment 1). A similar tendency for offloading was observed when participants were informed beforehand that the computer partner's tracking accuracy was flawless (Experiment 2). The present findings show that humans are willing to (partially) offload task demands to an algorithm to reduce their own cognitive load. We suggest that the cognitive load of a task is an important factor to consider when evaluating human tendencies for offloading cognition onto artificial systems.

Labor division in collaborative visual search: a review

When looking for a certain object or person, individuals often engage in collaborative visual search, i.e., they search together by coordinating their behavior. For instance, when parents are looking for their child on a busy playground, they might search collaboratively by dividing the search area. This type of labor division in collaborative visual search could be beneficial not only in daily life, but also in professional life (e.g., at airport security screening, lifeguarding, or diagnostic radiology). To better understand the mechanisms underlying this type of collaborative behavior, as well as its benefits and costs, researchers have studied visual search scenarios in the laboratory. The aim of this review article is to provide a brief overview of the results of these studies. Are individuals faster if they search together compared to alone? And if so, should they simply search in parallel, or will they benefit from agreeing on a specific labor division? How should they divide the search space, and how to communicate this division? Should a consensus be reached (target present or absent?) before ending the search? We address these and further key questions, focusing on the aspect of labor division. In conclusion, we integrate the reviewed findings into an applied context, point out which questions still remain, and put forward suggestions for future research. We hope that this review can serve not only as a theoretical foundation for basic research but also as a practical inspiration for applied research and development.

What makes us act together? On the cognitive models supporting humans’ decisions for joint action

We face tasks every day that we can solve alone but decide to solve together with others. When do we choose to act together vs. alone? How long do we persist in working together when doing so is difficult? Do we prefer to act together when times are uncertain? An open question in joint action research is under what conditions humans prefer to act together or alone to achieve a certain goal, and whether their preference is based on a utility calculus that takes into account the costs and benefits associated with individual and joint action alternatives. Research on cooperation reveals that frequent engagement in joint activities provides high survival benefits, as it allows individuals to achieve goals together that are otherwise unavailable. Yet, survival advantage does not wholly explain the reasons for human cooperative behavior. In fact, humans are motivated to cooperate even when it is not necessary to achieve an outcome. Research in cognitive science suggests that navigating the potential costs of joint actions is a challenge for humans, and that joint actions might provide individuals with rewards that go beyond the achievement of instrumental goals. We here address the influence of key factors on the decision to engage in joint action, such as the coordination costs arising when acting together compared to alone and the social and instrumental rewards expected when acting together compared to alone. Addressing these questions will provide critical insight for the design of cognitive models of human decisions for cooperation.

Human adults prefer to cooperate even when it is costly

Joint actions are cooperative activities where humans coordinate their actions to achieve individual and shared goals. While the motivation to engage in joint action is clear when a goal cannot be achieved by individuals alone, we asked whether humans are motivated to act together even when acting together is not necessary and implies incurring additional costs compared to individual goal achievement. Using a utility-based empirical approach, we investigated the extent of humans' preference for joint action over individual action, when the instrumental costs of performing joint actions outweigh the benefits. The results of five experiments showed that human adults have a stable preference for joint action, even if individual action is more effective to achieve a certain goal. We propose that such preferences can be understood as ascribing additional reward value to performing actions together.