COG 366/CSC 366 - 3 Semester Hours

Computational Models of Cognitive Processes

Introduction to the computational study of human and machine intelligence. Discussion of symbolic computation, neural networking, and genetic computation. Examination of research in language, vision, perception, memory, learning, reasoning, planning, and information processing. Programming in LISP (CLOS) is a featured part of this course. An introduction to the language is integrated into tightly specified programming projects which, upon completion, constitute tools for investigating aspects of cognition from the symbolic, neural, and genetic perspectives.

CSC241

Upper division Computer Science elective. Taken by most Computer Science B.S. Degree students with the Artificial Intelligence concentration. Required by Cognitive Science majors.

Upon successful completion of this course, students will be able to:

1. Have an understanding of computational approaches to intelligence.
2. Have an understanding of important particular models, algorithms, and research in the computational study of symbolic, neural, and genetic processing, vision, memory, learning, reasoning, planning, and information processing.
3. Find, read, understand, critique, and write about current research in cognitive science.

NOTES

1. This outline includes more material than might normally be covered in one semester. While material in each of the major headings should be discussed, the emphasis given to particular topics may differ across semesters.
2. Many of the themes and topics included in this outline are closely related to, if not identical with, themes and topics presented in the COG166 course. The courses are dramatically different, however, in that the emphasis in this course is much more decidedly on technical and theoretical aspects of computation.

1. Introduction
   1. Historical Overview of Research in Cognitive Science
      1. Contributions from computer science, cybernetics, psychology, biology, linguistics, philosophy
   2. Fundamental Concepts of Formal Models
      1. Nature of computational, cybernetic, psychological, and biological models
      2. Introduction/review of automata languages, graphs, networks, predicate logic
      3. Models of parallel computation
2. Symbolic Computation
   1. Physical Symbol System Hypothesis
   2. Survey of Symbolic Knowledge Representations
   3. Programs as Data
3. Neural Information Processing
   1. Biological and Computational Foundations
   2. Finite Automata Models
   3. Neural Networks and Connection Models
   4. General Models of Perception and Motor Control
4. Genetic Computation
   1. Darwinean Principles
   2. Essentials of Evolutionary Computation
   3. Genetic Programming
   4. Genetic Algorithms
   5. Models of Creativity
5. Semantic Information Processing
   1. Models of Semantic Memory
      1. Semantic nets and connection models
      2. Prototype and schema models
      3. Predicate logic models
   2. Semantic Processing
      1. Inference algorithms for deduction and abduction
      2. Probabilistic inference
6. Language
   1. Cartesian Linguistics
      1. Chomsky Hierarchy of Grammars
      2. Universal Grammar
   2. Semantics
      1. Concepts
      2. Compositional Semantics
   3. Context and Culture
      1. The Problem of Context
      2. Situation Theory and Alternatives
      3. Whorfian Investigations
7. Vision
   1. The Nature and Structure of Visual Processing
      1. Biological and computational foundations
      2. Parallelism of visual computations
   2. Algorithms for Low-Level Visual Processing
      1. Edge, line, shape, and feature detection
      2. Stereopsis.
      3. Color and texture analysis
   3. Algorithms and Models of High-Level Visual
      1. Representing objects
      2. Feature and object identification
      3. Scene analysis
8. Learning
   1. Formal Models of Learning Paradigms
      1. Learnability of sequences, concepts, grammars
      2. Probabilistic modes of induction
   2. Inductive Algorithms
      1. Adaptation, perceptions, and connection algorithms
      2. Generalization/specialization algorithms
   3. Psychological Models and Evidence
9. Reasoning, Planning, and Understanding
   1. Production Systems
   2. Models of Problem Solving and Planning
   3. Models of "Common Sense" Reasoning and Understanding

1. Lectures
2. Readings
3. Discussions
4. Assignments
5. Exams

Take exams. Write programs. Attend class and engage in classroom discussions.

1. Written assignments
2. Papers
3. Programming projects
4. Examinations

Computing machines and software.

M. Arbib, The Metaphorical Brain, Wiley, 1972.

D. Ballard and C. Brown, Computer Vision, Prentice-Hall, 1982.

R. Baron, The Cerebral Computer, Erlbaum, 1987.

I. Bratko, PROLOG: Programming for Artificial Intelligence, Addison Wesley, 1990.

P. Churchland, Neurophilosophy: Toward a Unified Science of the Mind/Brain, A Bradford Book: The MIT Press, 1986.

D. Dennett, Darwin's Dangerous Idea: Evolution and the Meanings of Life, Touchstone: Simon and Schuster, 1995.

H. Gardner, The Mind's New Science, Basic Books, Inc., 1985.

M. Gazzaniga, R. Ivry and G. Mangun, Cognitive Neuroscience, W. W. Norton & Company, 1998.

D. Gentner and A. Stevens, Mental Models, Erlbaum, 1983.

G. Hinton and J. Anderson, Parallel Models of Associative Memory, Erlbaum, 1981.

D. Hofstadter, Godel, Escher, Bach, Basic Books, 1979.

D. Hofstadter, Fluid Concepts and Creative Analogies, Basic Books, Inc., 1995.

J. Holland, K. Holyoak, R. Nisbett and P. Thagard, Induction, MIT Press, 1986.

R. Jackendoff, Consciousness and the Computational Mind, A Bradford Book: The MIT Press, 1992.

W. Kintsch, J. Miller, P. Polson, Methods and Tactics in Cognitive Science, Erlbaum, 1984.

S. Levy, Artificial Life: The Quest for a New Creation, Pantheon Books, Inc., 1992.

D. Marr, Vision, Freeman, 1982.

M. Minsky, Society of Mind, Simon and Schuster, Inc., 1982.

M. Mitchel, Genetic Algorithms, The MIT Press, 1992.

D. Osherson, M. Stob and S. Weinstein, Systems that Learn, MIT Press, 1986.

S. Pinker, Visual Cognition, MIT Press, 1985.

S. Pinker, How the Mind Works, W. W. Norton & Company, 1997.

Z. Pylyshyn, Computation and Cognition, MIT Press, 1986.

D. Rummelhart, and J. McClelland, Parallel Distributed Processing, MIT Press, 1986.

N. Sharkey, Advances in Cognitive Science, Wiley, 1986.

J. Sowa, Conceptual Structures, Addison-Wesley, 1984.

M. Spitzer, The Mind within the Net: Models of Learning, Thinking, and Acting, The MIT Press, 1992.

G. Steele, Common LISP, Digital Press, 1984. N. Stillings, M. Feinstein, J. Garfield, E. Rissland, D. Rosenbaum, S. Weisler, and L. Baker-Ward, Cognitive Science: An Introduction, A Bradford Book: The MIT Press, 1995.

T. Winograd and F. Flores, Understanding Computers and Cognition, Addison Wesley, 1986.

P. Winston, Artificial Intelligence, Addison Wesley, 1977.