Introduction to Cognitive Modeling

Prerequisites

No prior knowledge of any particular cognitive model will be assumed. Knowledge of the ideas germane to cognitive modelling, generally those of the information processing point of view, will be assumed. For students not familiar with those ideas, readings will be made available. Furthermore, while no knowledge of a specific programming language will be required, familiarity with programming concepts, and computers in general, will be beneficial.

Course Objective

This course is meant to introduce you to the concept of modeling cognitive skills. A cognitive model is a a representation, generally a on a computer, of how people solve problems or learn a particular skill. The course is divided into two components. One component takes place in a classroom, and will be an overview of the cognitive modeling field, during which we will discuss the basic idea behind it, early cognitive models, and end with a series of discussions on current cognitive architectures. Some of these cognitive architectures will be made available to you to actually try. The second component is designed to be hands-on. You will learn the fundamentals of how to construct a cognitive model using Anderson's ACT-R system.

Grading

For the hands-on ACT-R weeks, there is an assignment associated with each lesson. There will be an in-class final at the end of the semester based on the classroom readings and discussions.

Text and Readings

This one required text will be used to support the ACT-R exercises:

Anderson, J. R. (1993). Rules of the Mind. Hillsdale, NJ: Erlbaum.

During the first half of the course, we will be using papers and articles found in various books and journals. These are listed in the course outline below, and will be on reserve. The last part of this syllabus contains a list of additional readings. I will be making reference to these readings as we go through the course, and I provide this list to you in case you are interested in gaining a more in-depth understanding of any particular topic.

Course Outline

Odd numbered weeks will be the classroom component part of the course, where we will meet and discuss some aspect of cognitive modeling. You are expected to have read the readings before class. Even numbered weeks will introduce you to a new part of ACT-R. I will lecture on that new part, and then associated with that lecture will be a new unit within the ACT-R Lessons web page, with an assignment for you to complete within the ACT-R Environment, to be turned in within two weeks (i.e., before the start of the next unit).

Week 1

What is Cognitive Modeling and Why Do It?

No readings. For next week, however, you should work through Unit 0--Interpreting Production Rules, to gain some familiarity with ACT-R.

Week 2

Introduction to ACT-R (Unit 1)

Week 3

The Early Years I

Simon, H. A. (1991). Climbing the mountain: Artificial intelligence achieved. Models of My Life. New York, NY: BasicBooks, 198- 214.

Simon, H. A. (1975). The functional equivalence of problem solving skills. Cognitive Psychology, 7, 268-288.

Week 4

Knowledge Representation in ACT-R (Unit 2)

Week 5

The Early Years II

Cohen, H. (1973). Parallel to perception: Some notes on the problem of machine-generated art. Computer Studies, 4.

McCorduck, P. (1991). How does Aaron work? Aaron's Code: Meta-art, Artificial Intelligence, and the Work of Harold Cohen. New York, NY: W. H. Freeman and Company, 201-208.

Neves, D. M. (1978). A computer program that learns algebraic procedures by examining examples and by working test problems in a textbook. Proceedings of the Second National Conference of the Canadian Society for Computational Studies of Intelligence. 191-195.

Week 6

Parameters and Conflict Resolution (Unit 3)

Week 7

EPAM

Simon, H. A., & Feigenbaum, E. A. (1984). EPAM-like models of recognition and learning. Cognitive Science, 8, 305-336.

Simon, H. A. (1972). What is visual imagery? An information-processing interpretation. In L. W. Gregg (Ed.), Cognition in Learning and Memory, New York, NY: Wiley. 183-204.

Week 8

Activation and Latency (Unit 4)

Week 9

PDP

McClelland, J. L., Rumelhart, D. E., & Hinton, G. E. (1986). The appeal of parallel distributed processing. Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1). Cambridge, MA: The MIT Press. 3-45.

Rumelhart, D. E. & McClelland, J. L. (1986). PDP models and general issues in cognitive science. Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Vol. 1). Cambridge, MA: The MIT Press. 110-146.

Week 10

Partial Matching and Accuracy (Unit 5)

Week 11

Soar

Newell, A. (1990). Symbolic processing for intelligence. Unified Theories of Cognition. Cambridge, MA: Harvard University Press. 158-234.

A good Soar model to be found later.

Week 12

Learning Activation Parameters (Unit 6)

Week 13

ACT-R

Anderson, J. R. (1996). ACT: A simple thoery of complex congition. American Psychologist, 51, 355-365.

Anderson, J. R., Reder, L. M., & Lebiere, C. (199?). Working memory: Activation limitations on retrieval. Cognitive Psychology, ?, ???-???.

Week 14

Learning by Analogy (Unit 8)

Additional Readings

Information Processing Pscyhology

Simon, H. A., (1978). Information-processing theory of human problem solving. In W. K. Estes (Ed.) Handbook of learning and cognitive processes (Vol. 5). Hillsdale, NJ: Erlbaum.

Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall. [This is The Book on IPS. I suggest Chapters 2 and 14.]

Miscellaneous Models

Klahr, D., Langley, P., & Neches, R. (1987). Production System Models of Learning and Development. Cambridge, MA: The MIT Press.

Simon, H. A. (1979). Models of Thought: Volume 1. NewHaven, CT: Yale University Press.

Simon, H. A. (1989). Models of Thought: Volume 2. NewHaven, CT: Yale University Press.

Simon, H. A., & Sikl—ssy, L. (1972), Representation and Meaning. Englewood Cliffs, NJ: Prentice-Hall.

EPAM

Both the 1979 and 1989 Simon volumes listed above have articles with EPAM models and discussions.

Richman, H., & Simon, H. A. (1989). Context effects in letter perception: Comparison of two theories. Pscyhological Review, 96, 417-432.

PDP

Plaut, D. C., & Shallice, T. (1994). Word reading in damaged connectionist networks: Computational and neuropsychological implications. In R. J. Mammone (Ed.), Artificial Neural Networks for Speech and Vision, London: Chapman & Hall, 294-323

Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1995). Understanding normal and impaired word reading: Computational Principles in quasi-regular domains. Psychological Review, ?, ???-???.

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