GVU Center Brown Bag: Rick Thomas - "Memory Constraints on Hypothesis Generation and Decision Making"

Abstract:

Hypothesis generation is the process people use to generate explanations for patterns of data, which is an act vital to everyday problem solving. It is the basis for decision making in many professions, such as medicine, intelligence and reconnaissance analysis, auditing, and fault detection in nuclear power plants. Even lay people's impressions of acquaintances' personalities based on behavioral patterns can be considered a case of hypothesis generation. This talk will provide an overview of my research program elucidating the cognitive processes that underlie hypothesis generation and decision-making.

Speaker Bio:

Dr. Rick Thomas is an associate professor in Engineering Psychology at Georgia Tech. The fundamental premise of my work is that computational models from cognitive psychology and cognitive science can be adapted to provide testable process models of decision-making phenomena and optimized to support the decision-making of professionals. I direct the Decision Processes Laboratory (DPL). The DPL utilizes a range of experimental methodologies (behavioral, eye-tracking, EEG) and computational techniques (statistical, mathematical, neural networks) to investigate decision-making phenomena. Much of my applied work concerns the study and measurement of expertise; primarily in the areas of performance evaluation and the development of decision support tools. One area of specialization is the development of computational models that describe how people, generate hypotheses to explain patterns of data, which is common in everyday problem solving; and it is the basis for decision-making in many disciplines, such as medical diagnosis, criminal investigation, intelligence sense making, software debugging, and scientific discovery. We also seek to optimize models of human hypothesis generation to serve as decision support tools to aid the diagnostic decision-making of professionals and to improve the robustness of existing applications of artificially intelligent classification systems.