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  Travis Seymour

Travis Seymour

Associate Professor




831-459-3519 (Fax)


Social Sciences Division

Psychology Department

Associate Professor


Social Sciences Division

Regular Faculty

Cognitive Science
Computational Modeling
Scientific Method

Social Sciences 2

Social Sciences 2, Room 357

(Winter 2024) Frid., 1pm - 3pm; by appointment

Psychology Faculty Services

M.A., Ph.D., University of Michigan
B.A., Northwestern University

Role of working memory, consciousness, and executive control of the human performance of laboratory and applied tasks; cognitive processes amenable to strategic control and how they influence the way in which we maintain situational awareness; high levels of performance in complex and cognitive tasks; computational modeling of cognitive processes; human-computer/machine interaction; implications for use of technology in education

Travis Seymour's research involves theoretical and empirical investigations into the role of memory on human performance. The representation and access of perceptual memory, working memory and long-term memory constrain how and when we can use relevant information to complete many cognitive tasks. For example one line of work focuses on how working memory, situational awareness and executive control play a role in the multiple-task performance of automobile drivers and military jet pilots. A similar study focuses on how air-traffic controllers keep track of and remember critical visual-spatial information that dynamically changes over time.

In addition to these human-performance issues, Professor Seymour explores the role of conscious and unconscious executive processes in recognition memory. This project examines how constraints on memory access can allow us to detect the presence of privileged knowledge, despite explicit strategies to conceal this information. Because, under certain circumstances, we cannot control our response to information we recognize, results from this research have led to the formulation of a new mechanical "lie detector" potentially more accurate and reliable than previous candidates.

To test the various theoretical claims associated with each project, Professor Seymour employs the use of symbolic computational models constrained by robust a priori cognitive architectures such as the EPIC architecture (Meyer and Kieras, 1997a, 1997b). By comparing the results of these computer simulations to the data produced by human subjects, theories instantiated in the models can be verified in a precise and constrained manner.

For more information see Professor Seymour's lab website.

Psych 1: Introduction to Psychology
Psych 129: Human Learning and Memory
Psych 139D: Modeling Human Performance
Psych 138: Computer Programming for the Cognitive Sciences
Psych 20: Cognitive Processes
Psych 139N: Diversity in Cognitive Psychology
Psych 130B: Deception, Brain, and Behavior
Psych 252: Special Topics in Cognitive Psychology

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