Multiple theories of problem-solving hypothesize that there are distinct qualitative phases exhibited during effective problem-solving. However, limited research has attempted to identify when transitions between phases occur. We integrate theory on collaborative problem-solving with dynamical systems theory suggesting that when a system is undergoing a phase transition it should exhibit a peak in entropy and that entropy levels should also relate to team performance. Communications from 40 teams that collaborated on a complex problem were coded for occurrence of problem-solving processes. (...) We applied a sliding window entropy technique to each team's communications and specified criteria for identifying data points that qualify as peaks and determining which peaks were robust. We used multilevel modeling, and provide a qualitative example, to evaluate whether phases exhibit distinct distributions of communication processes. We also tested whether there was a relationship between entropy values at transition points and CPS performance. We found that a proportion of entropy peaks was robust and that the relative occurrence of communication codes varied significantly across phases. Peaks in entropy thus corresponded to qualitative shifts in teams’ CPS communications, providing empirical evidence that teams exhibit phase transitions during CPS. Also, lower average levels of entropy at the phase transition points predicted better CPS performance. We specify future directions to improve understanding of phase transitions during CPS, and collaborative cognition, more broadly. (shrink)

In this paper we suggest that differing approaches to the science of social cognition mirror the arguments between radical embodied and traditional approaches to cognition. We contrast the use in social cognition of theoretical inference and mental simulation mechanisms with approaches emphasizing a direct perception of others’ mental states. We build from a recent integrative framework unifying these divergent perspectives through the use of dual-process theory and supporting social neuroscience research. Our elaboration considers two complementary notions of direct perception: one (...) primarily stemming from ecological psychology and the other from enactive cognition theory. We use this as the foundation from which to offer an account of the informational basis for social information and assert a set of research propositions to further the science of social cognition. In doing so, we point out how perception of the minds of others can be supported in some cases by lawful information, supporting direct perception of social affordances and perhaps, mental states, and in other cases by cues that support indirect perceptual inference. Our goal is to extend accounts of social cognition by integrating advances across disciplines to provide a multi-level and multi-theoretic description that can advance this field and offer a means through which to reconcile radical embodied and traditional approaches to cognitive neuroscience. (shrink)

In this study, we explore the future of work by examining differences in productivity when teams are composed of only humans or both humans and machine agents. Our objective was to characterize the similarities and differences between human and human–machine teams as they work to coordinate across their specialized roles. This form of research is increasingly important given that machine agents are becoming commonplace in sociotechnical systems and playing a more active role in collaborative work. One particular class of machine (...) agents, bots, is being introduced to these systems to facilitate both taskwork and teamwork. We investigated the association between bots and productivity outcomes in open source software development through an analysis of hundreds of project teams. The presence of bots in teams was associated with higher levels of productivity and higher work centralization in addition to greater amounts of observed communication. The adoption of bots in software teams may have tradeoffs, in that doing so may increase productivity, but could also increase workload. We discuss the theoretical and practical implications of these findings for advancing human–machine teaming research. (shrink)

This article builds upon earlier research in which the authors presented case studies focused on the design and development of two original ethical learning video games. Through this case study and a review of relevant literature, the authors explored the content creation of, and theoretical rationale for, the design and development of ethics games. Both games were geared toward an undergraduate student audience as casual learning games to be completed in a few hours of gameplay. To update and expand this (...) original work, the authors reviewed contemporary research on identity, cognition, and self in relation to video game environments as well as literature dealing more specifically with ethics and video games. From this literature base and their applied design experiences, the authors offer ten guidelines as best practices to follow for aspiring ethics game developers. (shrink)

In this study, we explore the future of work by examining differences in productivity when teams are composed of only humans or both humans and machine agents. Our objective was to characterize the similarities and differences between human and human–machine teams as they work to coordinate across their specialized roles. This form of research is increasingly important given that machine agents are becoming commonplace in sociotechnical systems and playing a more active role in collaborative work. One particular class of machine (...) agents, bots, is being introduced to these systems to facilitate both taskwork and teamwork. We investigated the association between bots and productivity outcomes in open source software development through an analysis of hundreds of project teams. The presence of bots in teams was associated with higher levels of productivity and higher work centralization in addition to greater amounts of observed communication. The adoption of bots in software teams may have tradeoffs, in that doing so may increase productivity, but could also increase workload. We discuss the theoretical and practical implications of these findings for advancing human–machine teaming research. (shrink)