The Emergence of Specialized Roles Within Groups.

Humans routinely form groups to achieve goals that no individual can accomplish alone. Group coordination often brings to mind synchrony and alignment, where all individuals do the same thing (e.g., driving on the right side of the road, marching in lockstep, or playing musical instruments on a regular beat). Yet, effective coordination also typically involves differentiation, where specialized roles emerge for different members (e.g., prep stations in a kitchen or positions on an athletic team). Role specialization poses a challenge for computational models of group coordination, which have largely focused on achieving synchrony. Here, we present the CARMI framework, which characterizes role specialization processes in terms of five core features that we hope will help guide future model development: Communication, Adaptation to feedback, Repulsion, Multi-level planning, and Intention modeling. Although there are many paths to role formation, we suggest that roles emerge when each agent in a group dynamically allocates their behavior toward a shared goal to complement what they expect others to do. In other words, coordination concerns beliefs (who will do what) rather than simple actions. We describe three related experimental paradigms-"Group Binary Search," "Battles of the Exes," and "Find the Unicorn"-that we have used to study differentiation processes in the lab, each emphasizing different aspects of the CARMI framework.

The division of linguistic labour for offloading conceptual understanding.

The division of linguistic labour (DLL), initially theorized by philosophers, has gained the attention of cognitive scientists in the last decade. Contrary to some controversial philosophical accounts of DLL, we propose that it is an extended mind strategy of offloading conceptual understanding onto other people. In this article, we empirically explore this proposal by providing an exploratory experimental paradigm to search for the mechanisms underwriting DLL and how they may work in practice. We developed a between-subjects experiment in which participants had to categorize two pairs of highly confusable dog breeds after receiving categorization training on just one pair of breeds. In the treatment group, participants were grouped in dyads and were allowed to interact with each other by means of the labels of these four dog breeds. In their queries to trained 'experts', novices frequently used labels to refer to breeds that they could not identify themselves. Experts were highly responsive to their paired novices' queries, and the rates of querying for the two members within a dyad were positively correlated. Independent categorization failure and offloading categorization success lead to subsequent increases in querying by novices, indicating adaptive use of offloading. Self-reports of breed knowledge were higher for experts within a dyad compared to isolated experts. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.

Self-organized division of cognitive labor.

Often members of a group benefit from dividing the group's task into separate components, where each member specializes their role so as to accomplish only one of the components. While this division of labor phenomenon has been observed with respect to both manual and cognitive labor, there is no clear understanding of the cognitive mechanisms allowing for its emergence, especially when there are multiple divisions possible and communication is limited. Indeed, maximization of expected utility often does not differentiate between alternative ways in which individuals could divide labor. We developed an iterative two-person game in which there are multiple ways of dividing labor, but in which it is not possible to explicitly negotiate a division. We implemented the game both as a human experimental task and as a computational model. Our results show that the majority of human dyads can finish the game with an efficient division of labor. Moreover, we fitted our computational model to the behavioral data, which allowed us to explain how the perceived similarity between a player's actions and the task's focal points guided the players' choices from one round to the other, thus bridging the group dynamics and its underlying cognitive process. Potential applications of this model outside cognitive science include the improvement of cooperation in human groups, multi-agent systems, as well as human-robot collaboration.