Every day, decisions need to be made where time is a limiting factor. Regardless of situation, time constraints often place a premium on rapid decision-making. Researchers have been interested in studying this human behavior and understanding its underlying cognitive processes. In previous studies, scientists have believed that the cognitive processes underlying decision-making behavior were consistent with dual-process modes of thinking. Critics of dual-process theory question the vagueness of its definition, and claim that single-process accounts can explain the data just as well.

My aim is to elucidate the cognitive processes that underlie decisions which involve some level of risk through the experimental manipulation of time pressure. Using this method, I hope to distinguish between competing hypotheses related to the origin of the effect. I will explore three types of decisions that illustrate these concepts: risky decision-making involving gambles, intertemporal choice, and one-shot public goods games involving social cooperation. In our experiments, participants made decisions about gambles framed as either gains or losses; decided upon intertemporal choices for smaller but sooner rewards or larger but later rewards; and played a one-shot public goods game involving social cooperation and contributing an amount of money to a group. In each case, we experimentally manipulated time pressure, either within subjects or among individuals.

Results showed under time pressure, increased framing effects under in both hypothetical and incentivized choices; and greater contributions and cooperation among individuals, lending support to the dual process hypothesis that these effects arise from a fast, intuitive system. However, our intertemporal choice experiment showed that time constraints led to increased selection of the larger but later options, which suggests that the magnitude of the reward may play larger role in choice selection under cognitive load than previously studied. This diverges from the current dual-process interpretation that myopic choices under time pressure favor smaller but sooner rewards, and suggests that more studies are needed in this realm to disentangle the intuitive from the deliberative system through the manipulation of cognitive load.

Many experimental and statistical paradigms collect and analyze behavioral data under steady-state assumptions. Such paradigms may have low external validity, since real-world decisions are often made in situations when people are still learning and adapting, or their beliefs and preferences are in a state of flux, or where the decision environment is constantly changing. I focus on experimental and real-world paradigms that represent some form of adaptive behavior. Under such situations, factoring in structural adaptivity into cognitive modeling frameworks can improve their descriptive and predictive performance, and allow us to make superior inferences about the underlying latent cognitive processes. Towards this endeavor, first, a novel probabilistic framework is proposed for representation of heuristic strategies of information search and multi-attribute choice within a model of learning. Second, a novel adaptive reference point mechanism is introduced, and I show how this can be used in a variety of different tasks and applications, and be incorporated into cognitive frameworks to structurally capture the adaptive process. This mechanism provides superior predictive performance along with psychologically meaningful parameter inference in tasks ranging from signal detection, bandit problems, judgments about estimating true values, consumption behavior, probability tracking, and expectation formation. Third, I show how cognitive modeling can be applied to adaptive population behavior in the real world. Real world data is used to make inferences about the latent processes involved in aspects such as the changing nature of cycles of violence, and the impact of tax policies on changing consumption patterns. Incorporating cognitive modeling frameworks and psychological insights to constrain econometric models yields possibly simpler models, but with directly interpretable parameter inferences. Fourth, cognitive modeling approaches are used to design choice architecture frames to create behavioral nudges within a risk allocation paradigm, where people change their behavior based on representational, rather than meaningful changes in their environment.