In the area of transportation policy, congestion pricing has been used to alleviate traffic congestion in metropolitan areas. The focus of Chapter 1 is to examine drivers’ perceived risk of traffic delay as one determinant of reactions to congestion pricing. The experiment reported in this essay recruits commuters from the Atlanta and Orlando metropolitan areas to participate in a naturalistic experiment where they are asked to make repeated route decisions in a driving simulator. Chapter 1 examines belief formation and adjustments under an endogenous information environment where information about a route can be obtained only conditional on taking the route. If the subjects arrive to the destination late, i.e. beyond an assigned time threshold, they are faced with a discrete (flat) penalty. In contrast, Chapter 2 examines subjective beliefs in a setting where the penalty for a late arrival is continuous, such that a longer delay incurs additional penalty on the driver. The primary research question is: does belief formation differ when the late penalty is induced as a continuous amount compared to when it is induced as a discrete amount? In particular, will we observe a difference in learning across the range of congestion probabilities under different penalty settings? In the continuous penalty setting, we do not observe a difference in learning across the range of congestion probabilities. In contrast, in the discrete penalty setting we observe significant belief adjustments in the lowest congestion risk scenario. In Chapter 3 the “source method” is used to examine how uncertainty aversion differs across events that have the same underlying objective probabilities but are presented under varying degrees of uncertainty. Subjects are presented with three lottery tasks that rank in order of increasing uncertainty. Given the choices observed in each task a source function is estimated jointly with risk attitudes under different probability weighting specifications of the source function. Results from the Prelec probability weighting suggest that, as the degree of uncertainty increases, subjects display increased pessimism; in contrast, the Tversky-Kahneman (1992) and the Power probability weightings detect no such difference. Thus, the conclusion regarding uncertainty aversion are contingent on which probability weighting specification is assumed for the source function.

How is information organized in the brain during natural reading? Where and when do the required processes occur, such as the perception of individual words and the construction of sentence meanings. How are semantics, syntax and higher-level narrative structure represented? Answering these questions is core to understanding how the brain processes language and organizes complex information. However, due to the complexity of language processing, most brain imaging studies focus only on one of these questions using highly controlled stimuli which may not generalize beyond the experimental setting. This thesis proposes an alternative framework to study language processing. We acquire data using a naturalistic reading paradigm, annotate the presented text using natural language processing tools and predict brain activity with machine learning techniques. Finally, statistical testing is used to form rigorous conclusions. We also suggest the use of direct non-parametric hypothesis tests that do not rely on any model assumptions, and therefore do not suffer from model misspecification. Using our framework, we construct a brain reading map from functional magnetic resonance imaging data of subjects reading a chapter of a popular book. This map represents regions that our model reveals to be representing syntactic, semantic, visual and narrative information. Using this single experiment, our approach replicates many results from a wide range of classical studies that each focus on one aspect of language processing. We extend our brain reading map to include temporal dynamics as well as spatial information by using magnetoencephalography. We obtain a spatio-temporal picture of how successive words are processed by the brain. We show the progressive perception of each word in a posterior to anterior fashion. For each region along this pathway we show a differentiation of the word properties that best explain its activity.