Queen's School of Computing

COGS-300/3.0 Programming Cognitive Models

Original author: Roger Browse
Last Revised: March 14, 2015

Calendar Description

Systems and techniques for developing computational models of human cognitive processes. Symbolic artificial-intelligence and neural-network approaches. Students will become familiar with programming technology suitable for the implementation of aspects of cognitive models.

Prerequisites (COGS 201/3.0 or COGS 200/6.0 or PSYC 220/6.0) and CISC 352/3.0, or permission of the School
Learning Hours 120 (36L;84P)

Objectives

Computational cognitive models are different from artificial intelligence systems in that the objective is to perform specific tasks as humans do, including making errors in the same way that humans do. Though usually more complex, and more difficult to evaluate, cognitive models often use problem solving techniques that are simar to those used in artificial intelligence. As almost all existing cognitive models are programmed in LISP, this course starts off with instruction that will enable the students to become proficient in that language, and to be able to take advantage of the language capabilities that have led to LISP's popularity for cognitive modelling. Students gain practical experience by implementing some typical artificial techniques as assignments. Several existing cognitive modelling systems are examined in detail, including the testing, and modification of the models' source code, when available. The evaluation of cognitive modelling systems is somewhat different that the typical evaluation of program performance because it involves statistical relations with results from human experimental studies. Within the course, students become familiar with these evaluation techniques.

Topics
Symbolic programming:
Learning to take advantage of the power of LISP
Implementation of modelling tools:
e.g., decision trees, constraint satisfaction
Survey of, and experimentation with, cognitive models:
EPAM, SOAR, MINERVA, ACT-R, EPIC, INTERACTIVE ACTIVATION, SPREADING ACTIVATION
Techniques for the evaluation of cognitive modelling systems
References
  • Robert Wilensky: Common LISPcraft, 2nd edition (1986), Norton.

  • Newell, Allen. (1972) You can't play 20 questions with Mother Nature and win, in Visual Information Processing. New York, N.Y.: Academic Press.

  • Gilden, D. L. (2001) Cognitive emissions of 1 / f noise. Psychological Review, 108, 33 - 56.

  • Pitt, M. A., Myung, I. J., & Zhang, S. (2002) Toward a method of selecting among computational models of cognition. Psychological Review, 109, 472 - 491.
  • Anderson, J. R. & Lebiere, C. (1998) The Atomic Components of Thought. Mahwah, NJ: Lawrence Erlbaum & Assoc.
  • Richman, H. B., Staszewski, J. J., & Simon, H. A. (1995) Simulation of expert memory using EPAM IV. Psychological Review, 102, 305 - 330.

  • Dougherty, M. R. P., Gettys, C. F., & Ogden, E. E. (1999) Minerva-DM: A memory processes model for judgments of likelihood. Psychological Review, 106, 180 - 209.

  • Seidenberg, M. S. & McClelland, J. L. (1989) A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523 - 568.

  • Murdock, B. B. (1997) Context and mediators in a theory of distributed associative memory (TODAM2). Psychological Review, 104, 839 - 862.