Research
Interests
I am interested in the cognitive and computational bases of human decision making, and more specifically in individual differences in decision making under risk and uncertainty. My research approach involves: (i) identifying key dimensions of variation in a decision task; (ii) revealing important clusters of decision makers based on these dimensions; (iii) characterizing these clusters based on measures of cognitive performance, personality, and biological correlates; and (iv) developing computational models to better understand decision mechanisms through simulation and prediction. I am also interested in understanding the environmental, task-specific, and person-specific factors that might be manipulated to positively influence decision performance. As a secondary interest, I have also studied the development and structure of object representations in ventral visual cortex using biologically-inspired computational models.
In recent work I have been studying individual differences in decision making in the Iowa Gambling Task (IGT). In this work I have: (i) applied machine learning methods to reveal clusters of decision styles in IGT performance data; (ii) conducted a behavioral study to characterize differences in decision style based on cognitive and personality correlates; and, (iii) used computational reinforcement learning models to study how IGT decision styles differ in terms of decision mechanisms and parameters.
As a secondary interest, I have studied the development and structure of object representations in ventral visual cortex using biologically-inspired computational models.
Fellowships, Awards & Honors
- Marquis Award for Best Dissertation in Psychology, University of Michigan, 2009. link
- Allen Newell Student Paper Prize, International Conference on Cognitive Modeling, 2007.
- Rackham Predoctoral Fellowship, Rackham School of Graduate Studies, University of Michigan, 2006. link
- Regents Fellowship, University of Michigan, 2002. link
