Timothy J. Pleskac - US grants

We rely on the probability judgments of experts and laypeople every day. Space shuttles are launched not only on the basis of weather forecasts, but also on an engineer's subjective opinion that a part will or will not fail. Military missions and political policies are put in place using intelligence analysts' beliefs that an event will occur or has occurred. Without doubt the use of probability judgments to make decisions makes the accuracy of subjective probabilities of utmost importance. Indeed the accuracy of subjective probabilities has been well studied in the cognitive and decision sciences. Yet, an equally valuable aspect of subjective probabilities is the amount of time it takes judges to formulate their estimates. Clearly, the time a judge takes to make a probability judgment has external costs to both the judge and the decision maker. Yet, little is known about the internal time course of subjective probability judgments. Consequently, the impact these external costs have on subjective probabilities and their accuracy is not known.

In this project the Principal Investigator pursues research examining how variables external to the judge (e.g., time pressure; rewards and penalties) and internal to the judge (e.g., attention and sequential effects) impact the time course and accuracy of subjective probabilities. A general framework called Judgment Field Theory will integrate how these internal and external variables impact probability judgments. Moreover, the framework offers a cognitive account of how different descriptions of the same event (e.g., Lance Armstrong will win the race vs. Lance Armstrong won?t lose the race) change how judges evaluate the likelihood of an event occurring and how this evaluation changes as a function of time.

The broader impacts of the research are three-fold. First, the research will help in the development of methods to evaluate the accuracy of subjective probabilities. These methods can ultimately be used to improve the accuracy of judges. Second, the theoretical framework will be used in the development of an undergraduate psychological methods course curriculum that infuses a traditional methods course with techniques of cognitive modeling. Finally, a broader impact is the outreach to and integration of a diverse group of undergraduate and graduate students into cognitive science at MSU.

DESCRIPTION (provided by applicant): Impaired judgment and decision making is a key factor contributing to drug abuse. Research on the source of these impairments has focused largely on cognitive and neural deficits during two distinct periods. Before making a choice, drug abusers are overly sensitive to potential rewards and insensitive to long-term losses. After making a choice, drug abusers are hyper-reactive to rewards and show poor ability to learn from experience. Much less is known about the intervening process of deliberation when beliefs and desires are integrated over time to form a preference leading to a choice. This deliberation process can determine whether a person appears risk seeking or risk averse, impulsive or cautious, and slow or fast in responding. While these are characteristics directly relevant to drug abuse, the basic mechanisms of the process are not well understood. We conceptualize deliberation as a sequential sampling process where decision makers evaluate possible payoffs forming a subjective valence. These valences are accumulated over time forming a preference over the alternatives until a threshold is reached initiating a choice. In this application, we develop a theoretical and experimental framework that integrates computational modeling and cognitive neuroscience to characterize this deliberation process. Experimentally, we create a novel gambling task called the flash gambling task (FGT) in which participants choose between a sure payoff and a lottery that offers a draw from a distribution of payoffs. Instead of receiving verbal descriptions of the lottery, subjects watch simulated draws from this lottery that flash by every 50 ms (like watching a stock ticker run by). Thus, the FGT requires active integration of payoffs allowing more precise control over the deliberation process. Theoretically, we develop a framework that integrates computational models of decision making, neural studies of reward processing and perceptual decision making, and analytic models of hemodynamic response. Our model makes specific predictions regarding the fMRI BOLD signatures of different aspects of deliberation process during risky decision making. In this application, we use this model-based imaging approach to delineate the neural circuitry underlying the valuation and preference formation process in an fMRI experiment on a normal college sample. In a second study, we investigate the link between behavior and deliberation in the FGT and measures of risky drug use and impulsivity using a larger community sample. Results from these studies will offer new insights on the basic cognitive and neural mechanisms of risky decision making and establish potentially important links between process-level measures of choice behavior and drug use, thereby setting the stage for a greater understanding of the neural and computational basis of drug abuse.