MC3 Mathematical Cognitive Modeling

Description

Psychology, and particularly cognition, has been notoriously difficult to formalize as a science. This is due in part to the complexity of the interactions between the environment and behavior. In this course I will give an overview of an approach to formalizing the study cognition through mathematical models. The course will follow an overall theme of tracing the flow of information from the environment to a cognitive agent and back through perception and action. There will be an overview session, a session on using cognitive models as a tool for measuring information in the environment and measuring performance, and two sessions covering influential models of various aspects of cognition.

I. Introduction (Session 1)
a .Motivation for mathematical cognitive modelling
b. Overview of methods and applications
c. Introduction/review of basic tools for mathematical modelling

II. Modelling behavioural context (Session 2)
a. Computational constraints
b. Ideal observer analysis

III. Modelling cognition (Session 3)
a. Memory storage and retrieval
b. Categorization
c. Decision-making

IV. Modelling behaviour (Session4)
a. Choice and response time

Objectives

Upon completion of this course, students should understand the role of mathematics in cognitive psychology, understand the basic tools used in mathematical cognitive modeling, and be familiar with applications of mathematical modeling in experimental design and theories of cognition.

Literature

Lewandowsky, S. and Farrell, S. (20110. Computational Modeling in Cognition. London, England: Sage. ISBN: 978-1-4129-7076-1
Busemeyer, J. and Diederich, A. (2010). Cognitive Modeling. London, England: Sage. ISBN: 978-0-7619-2450-0
Ideal observer analysis
Geisler W. S. (2003) Ideal Observer analysis. In: L. Chalupa and J. Werner (Eds.), The Visual Neurosciences. Boston: MIT press, 825-837. http://www.utexas.edu/cola/files/1516300
Mathematical cognitive models
Shiffrin, R. M., and Steyvers, M. (1997). A model for recognition memory: REM-retrieving effectively from memory. Psychonomic Bulletin & Review, 4(2), 145-166. http://www.psiexp.ss.uci.edu/research/papers/memory/REM.pdf
Nosofsky, R. M. (1992). Similarity scaling and cognitive process models. Annual review of Psychology, 43(1), 25-53. http://www.cogs.indiana.edu/nosofsky/pubs/1992_rmn_arp_Similarity.pdf
Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review, 100(3), 432. http://www.indiana.edu/~psymodel/papers/bustow93.pdf
Modeling behavior
Donkin, C., Brown, S., & Heathcote, A. (2011). Drawing conclusions from choice response time models: A tutorial using the linear ballistic accumulator. Journal of Mathematical Psychology, 55(2), 140-151. http://www2.psy.unsw.edu.au/users/cdonkin/publications/jmp11.pdf
Houpt, J. W., Blaha, L. M., McIntire, J. P., Havig, P. R., & Townsend, J. T. (2014). Systems factorial technology with R. Behavior Research Methods, 46(2), 307-330. http://www.indiana.edu/~psymodel/papers/HouBla13.pdf

Course location

Guenne

Course requirements

None

Instructor information.

Instructor
Joseph Houpt

Vita

Dr. Houpt is an associate professor in the Department of Psychology at Wright State University. He received an MSc in artificial intelligence from the University of Edinburgh and a Ph.D. in psychology and cognitive science from Indiana University. His research examines human performance in basic and applied contexts through mathematical cognitive modeling.

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