PhD, Indiana University
Postdoctoral research, Syracuse University and Vanderbilt University
Just as a symphony unfolds over time from the interplay between individual members of an orchestra, cognition arises from an array of complex interacting processes: Our gaze flits between points of interest in our environment, allowing us to identify and inspect objects around us; in a split second, information about these objects is retrieved from memory, allowing us to recognize when we have experienced something similar before; this recognition enables us to learn from our past, helps us choose appropriate actions in the present, and shapes our expectations about the future.
My research takes a dynamic approach toward understanding how these myriad processes of perception, memory, learning, and decision-making jointly give rise to knowledge and action. The dynamic approach manifests in both the types of data we collect and the types of theories and models we develop. In the lab, our experiments collect detailed data about behavioral dynamics, including response times, speed-accuracy trade-off, motor trajectories, and eye movements. We use this data to develop detailed theories about how cognitive processes unfold and interact across time in order to yield these behavioral dynamics. We instantiate these theories as mathematical/computational models that allow us to make precise predictions and reproduce the details of behavior produced by individuals. In some of our collaborations, we explore how these models connect cognitive dynamics with neural dynamics.
- The dynamics of encoding and retrieval from memory
- The relationships between event memory (aka, "episodic" memory) and knowledge (aka, "semantic" memory), especially how robust knowledge representations arise out of individual experiences
- Memory for associations and other relationships between items and events
- Integrating dynamics of decision making with the dynamics of other ongoing cognitive processes
- Statistical methods for comparing models, particularly Bayesian methods