Monday, December 11, 2017
10:00 am

PhD Thesis Defense

Gabriela de Oliveira Penna Tavares, Graduate Student, Computation and Neural Systems, Caltech

Thesis title: "Computation and comparison of value signals in simple perceptual and economic choices"


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 thesis, I describe three projects which aim to advance our understanding of the systems and algorithms involved in the process of human decision making.

Chapter 1 investigates the application of the attentional drift diffusion model (aDDM) to a perceptual decision making task. We used two psychophysical tasks with human subjects to investigate the extent to which visual attention influences simple perceptual choices, and to test the extent to which the aDDM provides a good computational description of how attention affects the underlying decision processes. We found that the aDDM provides a reasonable quantitative description of the relationship between fluctuations in visual attention, choices and reaction times. We also found evidence for the sizable attentional choice biases predicted by this model, and that exogenous manipulations of attention induce choice biases consistent with these predictions.

Chapter 2 compares two methods for fitting the parameters of DDMs using experimental data. A difficult step in the study of this family of models is the estimation 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 aDDM, 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 3 studies simple choices involving two distinct classes of valuation systems: experiential systems, which assign value based on the history of previous reward experiences with similar options, and descriptive systems, which compute values using information about the options and environment at the time of decision. Although these 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.

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