Understanding Collective Human Behavior in Social Media Networks Via the Dynamical Hypothesis: Applications to Radicalization and Conspiratorial Beliefs.

The dynamical hypothesis has served to explore the ways in which cognitive agents can be understood dynamically and considered dynamical systems. Originally used to explain simple physical systems as a metaphor for cognition (i.e., the Watt governor) and eventually more complex animal systems (e.g., bird flocks), we argue that the dynamical hypothesis is among the most viable approaches to understanding pressing modern-day issues that arise from collective human behavior in online social networks. First, we discuss how the dynamical hypothesis is positioned to describe, predict, and explain the time-evolving nature of complex systems. Next, we adopt an interdisciplinary perspective to describe how online social networks are appropriately understood as dynamical systems. We introduce a dynamical modeling approach to reveal information about emergent properties in social media, where radicalized conspiratorial beliefs arise via coordination between user-level and community-level comments. Lastly, we contrast how the dynamical hypothesis differs from alternatives in explaining collective human behavior in social networks.

Reframing Cognitive Science as a Complexity Science.

Complexity science is an investigative framework that stems from a number of tried and tested disciplines-including systems theory, nonlinear dynamical systems theory, and synergetics-and extends a common set of concepts, methods, and principles to understand how natural systems operate. By quantitatively employing concepts, such as emergence, nonlinearity, and self-organization, complexity science offers a way to understand the structures and operations of natural cognitive systems in a manner that is conceptually compelling and mathematically rigorous. Thus, complexity science both transforms understandings of cognition and reframes more traditional approaches. Consequently, if cognitive systems are indeed complex systems, then cognitive science ought to consider complexity science as a centerpiece of the discipline.

Capturing Sequences of Learners' Self-Regulatory Interactions With Instructional Material During Game-Based Learning Using Auto-Recurrence Quantification Analysis.

Undergraduate students (N = 82) learned about microbiology with Crystal Island, a game-based learning environment (GBLE), which required participants to interact with instructional materials (i.e., books and research articles, non-player character [NPC] dialogue, posters) spread throughout the game. Participants were randomly assigned to one of two conditions: full agency, where they had complete control over their actions, and partial agency, where they were required to complete an ordered play-through of Crystal Island. As participants learned with Crystal Island, log-file and eye-tracking time series data were collected to pinpoint instances when participants interacted with instructional materials. Hierarchical linear growth models indicated relationships between eye gaze dwell time and (1) the type of representation a learner gathered information from (i.e., large sections of text, poster, or dialogue); (2) the ability of the learner to distinguish relevant from irrelevant information; (3) learning gains; and (4) agency. Auto-recurrence quantification analysis (aRQA) revealed the degree to which repetitive sequences of interactions with instructional material were random or predictable. Through hierarchical modeling, analyses suggested that greater dwell times and learning gains were associated with more predictable sequences of interaction with instructional materials. Results from hierarchical clustering found that participants with restricted agency and more recurrent action sequences had greater learning gains. Implications are provided for how learning unfolds over learners' time in game using a non-linear dynamical systems analysis and the extent to which it can be supported within GBLEs to design advanced learning technologies to scaffold self-regulation during game play.

Empirical Evidence for Extended Cognitive Systems.

We present an empirically supported theoretical and methodological framework for quantifying the system-level properties of person-plus-tool interactions in order to answer the question: "Are person-plus-tool-systems extended cognitive systems?" Nineteen participants provided perceptual judgments regarding their ability to pass through apertures of various widths while using visual information, blindfolded wielding a rod, or blindfolded wielding an Enactive Torch-a vibrotactile sensory-substitution device for detecting distance. Monofractal, multifractal, and recurrence quantification analyses were conducted to assess features of person-plus-tool movement dynamics. Trials where people utilized the rod or Enactive Torch demonstrated stable "self-similarity," or indices of healthy and adaptive single systems, regardless of aperture width, trial order, features of the participants' judgments, and participant characteristics. Enactive Torch trials exhibited a somewhat greater range of dynamic fluctuations than the rod trials, as well as less movement recurrence, suggesting that the Enactive Torch allowed for more exploratory movements. Findings provide support for the notion that person-plus-tool systems can be classified as extended cognitive systems and a framework for quantifying system-level properties of these systems. Implications concerning future research on extended cognition are discussed.

Beyond Dyadic Coordination: Multimodal Behavioral Irregularity in Triads Predicts Facets of Collaborative Problem Solving.

We hypothesize that effective collaboration is facilitated when individuals and environmental components form a synergy where they work together and regulate one another to produce stable patterns of behavior, or regularity, as well as adaptively reorganize to form new behaviors, or irregularity. We tested this hypothesis in a study with 32 triads who collaboratively solved a challenging visual computer programming task for 20 min following an introductory warm-up phase. Multidimensional recurrence quantification analysis was used to examine fine-grained (i.e., every 10 s) collective patterns of regularity across team members' speech rate, body movement, and team interaction with the shared user interface. We found that teams exhibited significant patterns of regularity as compared to shuffled baselines, but there were no systematic trends in regularity across time. We also found that periods of regularity were associated with a reduction in overall behavior. Notably, the production of irregular behavior predicted expert-coded metrics of collaborative activity, such as teams' ability to construct shared knowledge and effectively negotiate and coordinate execution of solutions, net of overall behavioral production and behavioral self-similarity. Our findings support the theory that groups can interact to form interpersonal synergies and indicate that information about system-level dynamics is a viable way to understand and predict effective collaborative processes.

Distributed cognition criteria: Defined, operationalized, and applied to human-dog systems.

Distributed cognition generally refers to situations in which task requirements are shared among multiple agents or, potentially, off-loaded onto the environment. With few exceptions, socially distributed cognition has largely been discussed in terms of intraspecific interactions. This conception fails to capture some forms of group-level cognition among human and non-human animals that are not readily measured or explained in mentalistic or verbal terms. In response to these limitations, we argue for a more stringent set of empirically-verifiable criteria for assessing whether a system is an instance of distributed cognition: interaction-dominant dynamics, agency, and shared task orientation. We apply this framework to humans and working dogs, and contrast the human-dog socially distributed cognitive system with humans using non-biological tools and human interaction with draft animals. The human-dog system illustrates three operationalizable factors for classifying phenomena as socially distributed cognition and extends the framework to interspecies distributed cognition.

Visual attention in mixed-gender groups.

A basic principle of objectification theory is that a mere glance from a stranger represents the potential to be sexualized, triggering women to take on the perspective of others and become vigilant to their appearance. However, research has yet to document gendered gaze patterns in social groups. The present study examined visual attention in groups of varying gender composition to understand how gender and minority status influence gaze behavior. One hundred undergraduates enrolled in psychology courses were photographed, and an additional 76 participants viewed groupings of these photographs while their point of gaze was recorded using a remote eye-tracking device. Participants were not told that their gaze was being recorded. Women were viewed more frequently and for longer periods of time than men in mixed-gender groups. Women were also more likely to be looked at first and last by observers. Men spent more time attending to pictures of women when fewer women were in the group. The opposite effect was found for pictures of men, such that male pictures were viewed less when fewer pictures of men were in the group. Female observers spent more time looking at men compared to male observers, and male observers spent more time looking at women than female observers, though both female and male observers looked at women more than men overall. Consistent with objectification theory, women's appearance garners more attention and interest in mixed-gender social groups.