3 Introduction

We make choices every day. What to eat, whether to take one route to work, and when to go to sleep, are just a few examples. Every decision is associated with consequences. Consequences are inherently rewarding (appetitive), punishing (aversive), or both. To understand the processes involved in human decision-making, we use quantitative frameworks to study choice behavior. These analytic approaches are primarily formalized in three disciplines: economics, psychology, and neuroscience.

In the experimental literature, there are two types of decision contexts described. In “decisions under risk,” the decision-maker knows with precision the probability distribution of possible outcomes. For “decisions under uncertainty,” the decision-maker must infer the probabilities of potential outcomes. In this thesis, I combine insights from economics, psychology, and neuroscience to propose a neurobiologically-plausible theory of decision-making under risk.

Over the next few chapters, I outline the evolution of decision-theory from classical economic models to behavioral economic ones. I then describe the influences of behavioral economic theories on neuroscience research and discuss potential neural mechanisms of decision-making. With a neurobiological inspiration, I propose a new theory of decision-making under risk called ‘Counterfactual Predicted Utility Theory.’ Next, I detail the concepts, methods, and computational modeling results as I estimate my theory’s validity on human choice data from a sure-bet or gamble task. Lastly, I discuss the results and propose next steps for continuing my work.