Decision Making under Uncertainty: Modeling, Revealing and Characterizing Individual Differences in the Iowa Gambling Task

Decision making, the process of choosing among a set of options, is a fundamental aspect of everyday mental life. Decisions are often made under conditions of uncertainty, when the payoffs are probabilistic and unknown. Two important challenges in the study of decision making are to understand how decision making processes are instantiated computationally and to reveal and characterize differences in decision making processes across individuals. In this dissertation I used computational and behavioral methods to study decision making under uncertainty in the context of the Iowa Gambling Task (IGT). The main contributions are: (i) A biologically-grounded computational model that provides a better account of IGT behavior than the most widely accepted model; (ii) The identification of three fundamentally different decision making styles in the IGT; (iii) An improved conceptualization of decision making that offers a more comprehensive approach to analyzing performance and that has important implications for the study of normal and clinically impaired decision making; (iv) An empirical challenge to the widely held belief that IGT performance is associated with impulsive and risky decision making; (v) A demonstrated association between decision making in the IGT and cognitive abilities as measured by the Wisconsin Card Sorting Task; and (vi) the introduction and successful demonstration of a robust data clustering methodology that offers significant advantages over methods that are currently used in the field of Psychology.