I study the mathematical and computational principles underlying human cognition using a combination of approaches from mathematical psychology, computational cognitive science, machine learning and artificial intelligence. I tend to focus on the cognitive processes and mental representations involved in different aspects of high-level cognition such as learning, memory, generalization, categorization and social cognition—all described further in the links below. My work is highly quantitative and I use non-traditional methods that take advantage of current technology. For example, I run experiments remotely over the internet in which I collect large datasets very rapidly and efficiently using crowdsourcing platforms such as Amazon Mechanical Turk; I use ideas from the emerging areas of data science and big data in order to manage and analyze large datasets; and I use state of the art computational modeling techniques and Bayesian methods.
My work on Bayesian cognitive modeling addresses recent debates in the psychological literature over Bayesian models of cognition. This work provides a foundation for new methods of exploring psychological theory that are increasingly being adopted by researchers in the field. More details can be found here.
My current and future plans for research are focused on explaining the cognitive foundations of social cognition using mathematical and computational models. This work utilizes new computational methods developed in my previous work on Bayesian modeling and builds off of some of my early research in social cognition. In addition to new empirical and theoretical findings, this research has practical applications in areas of human-computer interaction such as gaming, robotics, virtual reality and user interfaces.
More detailed descriptions of my research areas are provided in the links below.