UC Irvine

In this dissertation, we consider ways to incorporate ``meaningful'' variability in our inferences about human decision making under uncertainty and memory. In particular, we use response time modeling to incorporate theoretically and empirically motivated sources of variability to develop cognitive models of decision making. In the first two chapters, we motivate and demonstrate the importance of explicitly considering sequential effects in decision making under uncertainty. We show that, even in experiments designed to maximize the perceived independence in successive choices an individual makes, individuals are sensitive to previous trials. Importantly, some individuals are substantively sensitive to the point that our qualitative interpretations about their behavior changes (e.g. an individual may be re-classified from risk seeking to risk averse). In the next chapter, we develop a model of choice and response time to allow for more psychologically nuanced interpretations of individual abilities in memory discrimination. We apply this model across several datasets and use it to further test the validity of the current gold standard measure, derived from choice behavior alone. In the final chapter, we combine our previous analyses and explore how sequential effects and memory discrimination may change in aging populations. Together, we demonstrate how response time models can be easily adapted to incorporate psychologically important considerations of and novel inferences about human decision making.