Equations of mind: Data science for inferring nonlinear dynamics of socio-cognitive systems

The Dynamical Hypothesis in Situ: Challenges and Opportunities for a Dynamical Social Approach to Interpersonal Coordination

Over the past three decades, Van Gelder's dynamical hypothesis has been instrumental in reconceptualizing the ways in which perception-action-cognition unfolds over time and in context. Here, I examine how the dynamical approach has enriched the theoretical understanding of social dynamics within cognitive science, with a particular focus on interpersonal coordination. I frame this review around seven principles in dynamical systems: three that are well-represented in interpersonal coordination research to date (emergent behavior, context-sensitive behavior, and attractors) and four that could be useful opportunities for future growth (hysteresis, sensitivity to initial conditions, equifinality, and reciprocal compensation). In addition to identifying specific promising lines of theoretical inquiry, I focus on the significant potential afforded by computationally intensive science-especially in naturally occurring data or trace data. Building on the foundation laid over the past three decades, I argue that looking to increasingly situated and naturalistic settings (and data) is not only necessary to realize the full commitment to the dynamical hypothesis but is also critical to building parsimonious and principled theories of social phenomena.

Cognitive science as complexity science

It is uncontroversial to claim that cognitive science studies many complex phenomena. What is less acknowledged are the contradictions among many traditional commitments of its investigative approaches and the nature of cognitive systems. Consider, for example, methodological tensions that arise due to the fact that like most natural systems, cognitive systems are nonlinear; and yet, traditionally cognitive science has relied on linear statistical data analyses. Cognitive science as complexity science is offered as an interdisciplinary framework for the investigation of cognition that can dissolve such contradictions and tensions. Here, cognition is treated as exhibiting the following four key features: emergence, nonlinearity, self-organization, and universality. This framework integrates concepts, methods, and theories from such disciplines as systems theory, nonlinear dynamical systems theory, and synergetics. By adopting this approach, the cognitive sciences benefit from a common set of practices to investigate, explain, and understand cognition in its varied and complex forms. This article is categorized under: Computer Science > Neural Networks Psychology > Theory and Methods Philosophy > Foundations of Cognitive Science Neuroscience > Cognition.

Human social motor solutions for human-machine interaction in dynamical task contexts

Multiagent activity is commonplace in everyday life and can improve the behavioral efficiency of task performance and learning. Thus, augmenting social contexts with the use of interactive virtual and robotic agents is of great interest across health, sport, and industry domains. However, the effectiveness of human-machine interaction (HMI) to effectively train humans for future social encounters depends on the ability of artificial agents to respond to human coactors in a natural, human-like manner. One way to achieve effective HMI is by developing dynamical models utilizing dynamical motor primitives (DMPs) of human multiagent coordination that not only capture the behavioral dynamics of successful human performance but also, provide a tractable control architecture for computerized agents. Previous research has demonstrated how DMPs can successfully capture human-like dynamics of simple nonsocial, single-actor movements. However, it is unclear whether DMPs can be used to model more complex multiagent task scenarios. This study tested this human-centered approach to HMI using a complex dyadic shepherding task, in which pairs of coacting agents had to work together to corral and contain small herds of virtual sheep. Human-human and human-artificial agent dyads were tested across two different task contexts. The results revealed (i) that the performance of human-human dyads was equivalent to those composed of a human and the artificial agent and (ii) that, using a "Turing-like" methodology, most participants in the HMI condition were unaware that they were working alongside an artificial agent, further validating the isomorphism of human and artificial agent behavior.