Computation and Comparison of Value Signals in Simple Perceptual and Economic Choices

Abstract

How do we choose between different foods from a restaurant menu, or between a vacation overseas and more money in our savings account? Certain mechanisms in our brains allow us to make these and many other kinds of decisions effectively and efficiently. In this dissertation, I describe three projects which aim to advance our understanding of the systems and algorithms involved in the process of human decision making. Chapter 2 investigates the application of the attentional Drift-Diffusion Model to a perceptual decision making task. Perceptual decisions requiring the comparison of spatially distributed stimuli that are fixated sequentially might be influenced by fluctuations in visual attention. We used two psychophysical tasks with human subjects to investigate the extent to which visual attention influences simple percep- tual choices, and to test the extent to which the attentional Drift-Diffusion Model provides a good computational description of how attention affects the underlying decision processes. We found that this model provides a reasonable quantitative description of the relationship between fluctuations in visual attention, choices, and response times. We also found evidence for the sizable attentional choice biases predicted by the model, and that exogenous manipulations of attention induce choice biases consistent with these predictions. Chapter 3 compares two methods for fitting the parameters of the Drift-Diffusion Model using experimental data. A large number of studies have proposed that sequential integrator models of decision making, such as the Drift-Diffusion Model and its variants, provide a simple computational description of the algorithms used to make a large number of simple decisions. This is based on the fact that this class of models has been able to produce reasonably accurate descriptions of how choices, response times, and fixations are related to each other and to exogenous trial parameters, in a wide range of tasks. A difficult step in those studies is the estimation of a small number of free parameters to find the ones that explain the observed data best. The estimation method used in most studies is computationally very expensive since it approximates the likelihood of the observed data by simulating the model thousands of times and then counting the frequency with which the outcomes match the observed data. This problem is exacerbated with more complex models, such as the attentional Drift-Diffusion Model, or models with collapsing bounds, which contain a larger number of free parameters. We propose an alternative method for estimating the free parameters which relies on computing only the probability of the actual observed data, bypassing the need for the additional simulations. We present the results of simulation tests which show that our approach provides two key advantages over the alternative widely used method: a smaller number of experimental trials is needed in order to obtain comparable estimation accuracy, and the execution time of the estimation algorithms is substantially reduced. Finally, Chapter 4 studies simple economic choices involving two distinct classes of valuation systems: an experiential system, which assigns value based on the history of previous reward experiences with similar options, and a descriptive system, which computes values using information about the options and environment available at the time of decision. Although these two systems often assign similar relative desirability to the different options, they do not always do so. When conflict arises with the experiential system favoring one option and the descriptive system favoring another, the brain needs to resolve the conflict to select a single option. We present the results of a psychometric study designed to characterize the basic interactions of these two valuation systems, with and without conflict.