All data and code for my published studies can be accessed through the “Github” link on the right, or click HERE.
Examples of Current Projects
For a comprehensive list, please see my “Pubs” page for a standard listing of papers/projects, or click the links on the sidebar on the right.
Interpersonal Coordination and Coregulation During Collaborative Problem Solving
Collaborative problem solving (CPS) is an essential 21st century skill in our increasingly connected and globalized world. Yet, there is a paucity of knowledge on how to define, measure, and develop this skill, especially in the context of STEM learning. Thus, the goal of this project is to gain fundamental knowledge on how interpersonal interactions influence CPS processes and outcomes in digital STEM learning environments. The hypothesis is that CPS is fundamentally about communication and coordination among people who have thoughts, feelings, and behaviors and who both react to and influence each other’s thoughts, feelings, and behaviors. Increasing basic understanding on how these interpersonal processes emerge and influence outcomes is a critical step towards designing next-generation STEM learning environments that make CPS more enjoyable, engaging, and effective.
Coordination Dynamics During Deception and Other High-level Interactions
When two people interact, complex patterns of behavior emerge quite spontaneously. These patterns are organized around multiple types of movement that simultaneously co-occur with little to no conscious awareness. Nevertheless, they form a stable network of associations that guide how people converge on meaning and respond to higher-level communicative goals. In this research, we take a dynamical systems approach to understand how various components of behavior, including acoustic, linguistic, and movement components, interact over time.
This focus on the interdependence of behavior can also be characterized as a focus on behavioral coordination, whereby individual’s cognitive processes and behavioral patterns are integrated into a coupled system. How this coordination changes under various high-level conditions, such as when people lie to each other, persuade each other, or attempt to understand each other, is currently being explored in the lab. To do so, we use automated motion analysis from video, motion tracking tools like the Microsoft Kinect, and high-powered infrared motion tracking cameras.
Duran, N. D., Dale, R., & Richardson, D. (2013). A mass assembly of associative mechanisms: a dynamical systems account of natural social interaction. Behavioral and Brain Sciences (commentary).
Dale, R., Fusaroli, R., Duran, N. D., & Richardson, D. C. (2013). The self-organization of human interaction. In B. Ross (Ed.), Psychology of Learning and Motivation, vol. 59 (pp. 43-95). Elsevier, Inc: Academic Press.
Action Dynamics of False Responding and Deception
A convincing deceiver must act in discordance with their knowledge of the truth. To do so requires the deceiver to resolve competition between what is known to be true and what is intended to be false. In this line of research, we investigate the temporal signatures of this competition by examining the action dynamics of arm movement while participants respond falsely or truthfully to various sources of information. To do so, we use a simple computer mouse and Nintendo Wiimote to track a continuous stream of x,y arm movement coordinates. This information not only extends traditional approaches that have collapsed deceptive behavior into ballistic reaction time movements, but provides a more sensitive measure to detect deception in the real-world.
Duran, N. D., Dale, R., & McNamara, D. S. (2010). The action dynamics of overcoming the truth. Psychonomic Bulletin & Review, 17, 486-491.
Duran, N. D. & Dale, R. (2012). Increased vigilance in monitoring others’ mental states during deception. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 1518-1523). Austin: TX: Cognitive Science Society.
The Dynamics of Perspective-taking
This research explores how people represent and comprehend another’s perspective in social communication. This line of inquiry has contributed to many insights in the areas of pragmatics, embodiment, and social cognition. We are attempting to extend this research in two major ways. The first is to interpret perspective-taking behavior as a nonlinear dynamical process. Here we argue that the fundamental dynamical properties that exist throughout nature also exist at the level of high-level, human cognition. The second way is to use action dynamics techniques, as in capturing arm movement trajectories, to better understand the factors and cognitive processes that underlie egocentric and allocentric (“other-centric”) response choices.
Duran, N. D., & Dale, R., & Kreuz, R. J. (2011). Listeners invest in an assumed other’s perspective despite cognitive cost. Cognition, 121, 22-40.
Duran, N. D., & Dale, R. (2013). Perspective-taking in dialogue as self-organization under social constraints. New Ideas in Psychology.
Language and Deception
Even amongst the most talented of deceivers, their ability to conceal verbal lies can be compromised by subtle linguistic features that have been shown to reveal hidden cognitive states. These features include the literal meaning of the words used, as well on how words are arranged and structured in discourse. For this project, we take advantage of advances in technology and linguistic theory to analyze hundreds of features of language using natural language processing tools, ranging from word information variables to sentence and discourse level variables. Thus, language serves as a window into cognition, addressing a diverse set of interrelated questions, including a) What are the qualitative and quantitative linguistic features of deception, b) How does the relationship between language and cognition vary from individual monologue to conversational deception?, and c) Are there consistent patterns of language that cut across context?
Duran, N. D., McCarthy, P. M., Hall, C., & McNamara, D. S. (2010). The linguistic correlates of conversational deception: Comparing natural language processing technologies. Applied Psycholinguistics, 31, 439-462.
One of my primary interests is in the area of psycholinguistics. Specifically, I am most interested in how nonlinear dynamical systems and cognitive dynamics can offer new insights into the study of human language. Both approaches are unique strands of research, but are nevertheless closely related. In dynamical systems, the general thesis is that language operates as a self-organizing system, and thus signatures of nonlinear dynamics should be evident in language representation and use. In cognitive dynamics, the emphasis is on the covariance of cognition and action as it unfolds in real-time. Both strands offer powerful methodological tools, including arm trajectory analysis, cross-recurrence analysis, 1/f analysis, and neural networks. In collaboration with, and using techniques pioneered by Dr. Rick Dale, I explore the psychological study of language as a dynamic psycholinguistics.
Discourse Analysis for Educational Practices
With Drs. Danielle McNamara and Phil McCarthy, I have been involved in several projects (and papers) that are aimed at better understanding reading and writing comprehension through discourse analysis. In addition to gaining basic insights on cognitive processes, the ultimate goal is to apply our findings to the design of educational technologies.
Distributional Statistics / Semantic Models
Much of my early undergraduate training was on assessing high dimensional semantic models like HAL (Hyperspace Analogue to Language) and LSA (Latent Semantic Models). I have not lost any of my early appreciation for their explanatory power.