Essays on Sequential Sampling in Value-Based Choice

Citation

Eum, Brenden (2025) Essays on Sequential Sampling in Value-Based Choice. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/rwy1-ry63. https://resolver.caltech.edu/CaltechTHESIS:09102024-194957107

Abstract

This dissertation comprises three chapters related to the fields of psychology, computational neuroscience, and experimental economics. Chapters 1 and 2 use experimental and computational methods to study the role of attention in simple, value-based choices. Chapter 3 examines risky choices from experience and tests some of the underlying assumptions of sequential sampling models.

A growing body of research has shown that simple choices involve the construction and comparison of values at the time of decision. These processes are modulated by attention in a way that leaves decision makers susceptible to attentional biases. In Chapter 1, co-authored with Stephanie Dolbier and Antonio Rangel, we studied the role of peripheral visual information on the choice process and on attentional choice biases. We used an eye-tracking experiment in which participants (N = 50 adults) made binary choices between food items that were displayed in marked screen ``shelves'' in two conditions: (a) where both items were displayed, and (b) where items were displayed only when participants fixated within their shelves. We found that removing the nonfixated option approximately doubled the size of the attentional biases. The results show that peripheral visual information is crucial in facilitating good decisions and suggest that individuals might be influenceable by settings in which only one item is shown at a time, such as e-commerce.

In Chapter 2, co-authored with Stephen Gonzalez and Antonio Rangel, we studied the role of attention in aversive risky choices where all outcomes were unpleasant. We used two eye-tracking experiments in which participants made binary choices between two lotteries in two conditions: (a) a gain condition where outcomes for lotteries were weakly positive, and (b) a loss condition where outcomes were weakly negative. Contrary to the predictions of the standard aDDM, we found that attentional choice biases in the loss condition were identical to those found in the gain condition, suggesting that attention nudges choices towards the attended option even in losses. To explain these results, we propose a variation of the Attentional Drift-Diffusion-Model (called the Hybrid aDDM) that incorporates (a) both a value-dependent and a value-independent effect of attention on the choice process and (b) reference-dependent value signals. We show that the observed attentional choice biases and other behavioral signatures in the loss condition can only be explained by the Hybrid aDDM with a reference-point rule that sets the reference-point at or below the minimum possible outcome in a given context.

In Chapter 3, co-authored with Antonio Rangel, we establish that sequential sampling models apply to risky decisions from experience and test some of the underlying assumptions of these models. We ran an online study in which participants chose to Play or Skip a slot machine, based on a stream of samples drawn from its outcome distribution. We found evidence for leakage, collapsing decision boundaries, and a delay in sample integration. We also found evidence of non-linear sample weighting depending on when the sample occurred during the trial. As a bonus, we established a link between the fixed decision boundaries in a Drift-Diffusion-Model and a Modified Probit model, allowing for estimation of decision boundaries in cumulative sample space without the need to fit a computational model.