Grant Number:	5R21CA167691-02 Interpret this number
Primary Investigator:	Hinson, John
Organization:	Washington State University
Project Title:	Information Throughput in Risky Decision Making Underlying Self-Regulation
Fiscal Year:	2012

DESCRIPTION (provided by applicant): Behavioral self-regulation is often based on decision making processes that involve risk and ambiguity. Further, risky decision making can depend on non-affective, deliberative processes (cold cognition), affective, automatic processes (hot cognition), or the interaction of these processes. The role of hot cognition is paradoxical in that affect sometimes appears to interfere with good decision making and sometimes seems to facilitate good decision making. Because of this, it is unclear whether in a given situation suboptimal decision making is due to insufficient or unutilized cold cognitive information, or due to inappropriately weighting of cold cognitive information along with hot cognitive information. The proposed research will examine when cold cognitive information is available and when it is properly used in risky decision making tasks in three specific aims. First, we will assess the fate of cold cognitive information and determine the impact on frame-induced decision making bias in a traditional risky decision making task with experimental challenges to hot and cold cognition. Second, we will assess the fate of cold cognitive information and determine the impact on frame-induced decision making in the newly developed Framed Gambling Task, which allows better analysis of risk and ambiguity, also with challenges to hot and cold cognition. Third, we assess the fate of cold cognitive information and determine the impact on frame-induced decision making in a sleep deprivation challenge. This research will help to identify factors that impede decision making processes necessary for good self-regulation. Extending the research from artificial laboratory challenges to a realistic sleep deprivation challenge will assist in designing interventions and environments to reduce the impact of suboptimal decision on safety, health and well-being in daily life.