Extensions and applications of stochastic accumulator models in attention and decision making

Abstract

The research presented in this thesis is a collection of applications and extensions of stochastic accumulator models to various areas of decision making and attention in neuroscience.

Chapter 1 introduces the major techniques and experimental results that guide us throughout the rest of the thesis.

In particular, we introduce and define the leaky, competing accumulator, drift diffusion, and Ornstein-Uhlenbeck models.

In chapter 2, we adopt an Ornstein-Uhlenbeck (OU) process to fit a generalized version of the motion dots task in which monkeys are now faced with biased rewards.

We demonstrate that monkeys shift their behaviors in a systematic way, and that they do so in a near optimal manner.

We also fit the OU model to neural data and find that OU model behaves almost like a pure drift diffusion process.

This gives further evidence that the DDM is a good model for both the behavior and neural activity related to perceptual choice.

In chapter 3, we construct a multi-area model for a covert search task.

We discover some new trends in the data and systematically construct a model which explains the key findings in the data.

Our model proposes that the lateral intraparietal area (LIP) plays an attentional role in this covert search task, and suggests that the two monkeys used in this study adapted different strategies for performing the task.

In chapter 4, we extend the model of noise in the popular drift diffusion model (DDM) to a more general Levy process.

The jumps introduced into the noise increments dramatically affect the reaction times predicted by the DDM, and they allow the pure DDM to reproduce fast error trials given unbiased initial data, a feature which other models require more parameters to reproduce.

The model is fit to human subject data and is shown to outperform the extended DDM in data containing fast error reaction times.

In chapter 5, we construct a model for studying capacity constraints on cognitive control using the DDM as a generalized model for a task.

After studying various aspects of the constructed model, large scale simulations demonstrate that a severe capacity constraint does indeed arise out of the need for optimizing overall rewards.

The thesis concludes with some summarizing remarks in chapter 6.