Cog 166 - 3 Semester Hours

Introduction to Cognitive Science

This course will introduce the fundamental questions, findings, and methods of cognitive science. The computational approach to cognition and the notion of abstract mental representation are introduced within the interdisciplinary framework of the field. Elements of cognition, computation, neural networking, and Darwinean processes are introduced. Logic and linguistics are formally introduced, computationally considered, and applied to issues of representation and reasoning. Theoretical and practical aspects of human/machine communication are explored. High level aspects of vision are discussed. Creativity is investigated within the realms of painting, music composition, and story generation. The topics of emotion, embodiment, dynamical systems, and emergent behavior are mentioned in connection with conjectures pertaining to thinking and consciousness. Important philosophical questions are considered. Symbolic computations in Prolog are featured.

None

There is a generally acknowledged need to give Cognitive Science majors a common experience in an introductory course. This couse will be a required foundational course in the core of our Cognitive Science programs. Additionally, an introductory Cognitive Science course contributes richly to the objectives of authentic general education.

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

1. Articulate the computational assumption which underlies the field of cognitive science.
2. Discuss the interdisciplinary nature of the field and define the contributing fields.
3. Describe the concept of a mental representation and describe several of the standard knowledge representations.
4. Represent arguments in terms of logic, manipulate logical formulae in truth preserving ways, apply inferencing mechanisms of standard logic, and discuss various perspectives on the psychological plausability of logic.
5. Represent knowledge in Prolog, perform symbolic computations in prolog, and describe the Prolog language in terms of the logic of resolution.
6. Explain elements of cognition in terms of basic models of memory, attention, problem solving, planning and learning.
7. Define and illustrate the higher level cognitive phenomena of conceptualization and classification, rule generation and application, analogical reasoning, and metaphorical thinking.
8. Define grammars for language generation, perform derivations, discuss the language instinct, and describe language in basic semiotic terms.
9. Craft a question answering system in Prolog by coupling a definite clause grammar with a very small relational knowledge base.
10. Explain the phenomenon of visual processing at a very high level of abstraction.
11. Describe the basic structure and operation of natural and artificial neural networks.
12. Define Darwin Machines and abstractly describe their manifestation in genetic algorithms, genetic programming, and the science of memetics.
13. Describe a model of creativity inspired by darwinean theory and portrayh its role in computationally creating paintings, music, and narratives.
14. Explain the relevance of emotion, embodiment, dynamic systems, and emergent behaviour to the field of cognitive science.
15. Debate the relevance of the Turing Test, the value of Chinese Room argument, and the implications of Godel's Incompleteness Theorem.

1. Introduction
1. Opening Definitions
2. History and Prehistory
3. Computational/Representational Assumption
4. Interdisciplinary Assumption and the Contributing Disciplines
2. Spatial and Temporal Knowledge Representations
1. Semantic Nets
2. Relational Representations
3. Frames
4. Scripts, Mops, and Tops
3. Logic
1. Elements of Propositional Calculus
2. Elements of Predicate Calculus
3. Normal Forms, Resolution, and Horn Clause Problem Solving
4. Psychological Plausability
4. Symbolic Computation in Prolog
1. Physical Symbol System Hypothesis
2. Fundamental Computational Concepts
3. Terms and Relational Knowledge Bases
4. Prolog as a Horn Clause Problem Solver
5. Prolog Idioms of Iteration and Recursion
6. Programming Project
5. Elements of Cognition
1. Memory
2. Attention
3. Problem Solving (State Spaces and Reduction Systems)
4. Planning
5. Learning
6. High Level Cognitive Operators
1. Conceptualization and Classification
2. Rules
3. Analogy
4. Metaphor
7. Linguistics
1. Vocabularies, Languages, Grammars
2. The Language Instinct and the Universal Grammar
3. Language as a Semiotic System
8. Natural Language Processing in Prolog
1. Definite Clause Grammars
2. Interpreters
3. Question Answering Systems
4. Programming Project
9. Vision
1. The Eyes and the Mind
2. Gestalt Principles
3. Optical Illusions
10. Human/Machine Communication
1. Meaning
2. Context
3. Cultural Knowledge
4. Structure of Conversation
5. Situation Theory
11. Brains and Neural Networks
1. Elements of Neuroscience
2. Neural Networks
3. Concepts in Cognitive Neuroscience
12. Evolutionary Programming
1. Darwinean Basics
2. Genetic Algorithms
3. Genetic Programming
4. Self Organization
5. Memetics
13. Courting Creativity
1. Painting
2. Music
3. Narrative
14. Potpourri of Perspecitives on Cognition and Consciousness
1. The Emotional Brain
2. The Embodied Mind
3. Dynamic Systems
4. Emergent Behavior
15. Philosophical Questions and Challenges
1. The Turing Test
2. Searle's Chinese Room
3. Godel's Incompleteness Theorem

1. Lecture
2. Reading
3. Writing
4. Programming
5. Discussion
6. Oral presentation and evaluation
7. Exam preparation, execution and review

Take exams. Write programs. Write papers. Make and evaluate oral presentations. Attend class and engage in classroom discussions.

1. Writing
2. Programming
3. Discussion
4. Oral presentation and evaluation
5. Exam preparation, execution and review

Computing machines and software.

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.

Harnish, R. M. Minds, brains, computers: An historical introduction to the foundations of cognitive science., Blackwell, 2002.

Halpern, D. F. Thought & knowledge: An introduction to critical thinking (4th ed.), Erlbaum, 2003.

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

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

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

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

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

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

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.

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

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

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.