Introduction to Cognitive Science

Instructor: Péter Érdi
Office: Olds/Upton 208B
Phone: (269)337-520
email: perdi@kzoo.edu
URL: http://cc.kzoo.edu/~perdi

Topic: Cognitive science is the interdisciplinary study of mind and the nature of intelligence. It is a rapidly evolving field that deals with information processing, intelligent systems, complex cognition, and large-scale computation. The scientific discipline encompasses the overlapping areas of neuroscience, psychology, computer science, linguistics and philosophy. Students will learn the basic physiological and psychological mechanisms and computational algorithms underlying different cognitive phenomena.

Goal: The first goal is to teach WHY cognitive science became a popular and efficient discipline to investigate natural and artificial information processing devices. The second goal is to give an introduction to the historical development of the field. The third goal is to show HOW to analyze the performance of cognitive systems. The course is designed mostly for psychology and computer science students, but other students interested in interdisciplinary thinking might take the class.

Prerequisite: PSYC 101 and COMP 105 or permission.

Course Structure: Each week a topic will be discussed. There will be some weekly reading assignments. During the term it will be possible to give reports on readings.

Exams: There will be a sixty minute long written midterm and a final oral examination. Active participation in class activities are strongly encouraged. Class discussion is a vital part of learning in this style of course. Reports on readings and discussion forums might determine the grades up to 40%. Written and oral reports on a group project are a pre-requirement for taking the final examination. Active participation in events organized by the Center for Complex System Studies is encouraged, and will also be considered in assigning the final grade.

Special Excuse: I have been invited to give a talk on the International Symposium BRAIN, VISION and ARTIFICIAL INTELLIGENCE in Naples, Italy, http://biocib.cib.na.cnr.it/BVAI2005/, so I will off on the 5th week. One lecture will be held by Dr. Nathan Sprague, and two lectures will be rescheduled for evening hours.

Book required: Paul Thagard: Mind : Introduction to Cognitive Science. Cambridge, MIT Press. (MIT Press, 1996), (2nd edition, 2005). (I will refer to it as T:M)

Supporting literature:

  • M.A. Arbib, P. Érdi, and J. Szentágothai Neural Organization: Structure, Function, and Dynamics. MIT Press (Bradford Books), Cambridge, MA, 1997/1998
  • H. Eichenbaum The Cognitive Neuroscience of Memory: An Introduction. Oxford University Press, 2002
  • P.N. Johnson-Laird Mental Models. Cambridge: Cambridge University Press. Cambridge, Mass.: Harvard University Press, 1983
  • T. Polk and C.M. Seifert (Eds.) Cognitive Modeling. MIT Press, 2002
  • L.M. Ward Dynamical Cognitive Science. MIT Press, 2001
  • Paul Thagard (Ed.) Mind Readings: Introductory Selections on Cognitive Science by Paul Thagard MIT Press, 1998
  • MIT Encyclopedia of Cognitive Science
  • Cognitive Science Resources

Weekly Topics:

  1. Introduction: What is Cognitive Science?

    2. A very good summary can be found at: http://plato.stanford.edu/entries/cognitive-science/ or http://www.science.uva.nl/~seop/entries/cognitive-science/.

    On Friday there will be a group discussion about the main sections:
    • History
    • Methods
    • Representation and computation
    • Theoretical approaches
    • Philosophical relevance
    (this article was prepared for philosophers)

    Why we are NOT using a single textbook?
    Two different approaches exist: I. Assumptions about the representation of the knowledge in the mind (logic, rules etc.)
    II. Cognitive functions (learning, memory, emotions etc.)
    Reading: T:M Chapter 1.

  2. Knowledge Representation: Logic, Rule-based systems and others

    3. Mind might contain mental representations:
    • Formal logic
    • Rules
    • Concepts
    • Analogies
    • Images
    • Connections

    Formal logic

    • People have mental representations similar to sentences in predicate logic.
    • People have deductive and inductive procedures that operate on those sentences.
    • The deductive and inductive procedures, applied to the sentences, produce the inferences.
      You certainly should know the most important inferences:
      Modus ponens: 1. If P, then Q . 2. P. Therefore, Q.
      Modus tollens: 1. If P, then Q. 2. Q is false. Therefore, P is false. (indirect proof)

    The scope and limits of the approach of formal logic (and deductive reasoning):
    Philip N. Johnson-Laird: http://webscript.princeton.edu/~psych/PsychSite/~phil.html
    A taxonomy of thought: (daydream, calculations, creation, induction, deduction).
    Readings: T:M Chapter 2 and 3.
    Further reading:
    Introduction to Logic: http://people.hofstra.edu/faculty/Stefan_Waner/RealWorld/logic/logicintro.html

    Rules

    • People have mental rules.
    • People have procedures for using these rules to search a space of possible solutions, and procedures for generating new rules.
    • Procedures for using and forming rules produce the behavior.
    Rule-based systems: manipulation and transformation of symbols
    Rule-based programes for AI and cognitive science:
    1. Newell and Simon, GPS 1950s-60s
    2. Expert systems, 1970s-90s. Most corporations.
    3. ACT 1983. John Anderson.
    4. SOAR, Newell and his students, 1980s, Jojn E. Laird (Univ. Michigan)
    5. Prolog: logic programming
    Strength and weakness of the rule-based systems
    Language and rules: from Chomsky to Pinker
    Chomsky

    Pinker 1991:

    "Language and cognition have been explained as the products of a homogeneous associative memory structure or alternatively, of a set of genetically determined computational modules in which rules manipulate symbolic representations. Intensive study of one phenomenon of English grammar and how it is processed and acquired suggest that both theories are partly right. Regular verbs (walk-walked) are computed by a suffixation rule in a neural system for grammatical processing; irregular verbs (run-ran) are retrieved from an associative memory."

  3. Connectionism: Parallel distributed processing

    4. Logic and rules are the main forms of mental representations of the classical, symbolic approach.

    Connectionism offered and alternative to this symbolic approach. The most celebrated book of the connectionist alternative is:

    Parallel Distributed Processing: Explorations in the Microstructure of Cognition - Volume 1 (foundations) & Volume 2 (Psychological and Biological Models), by James L. McClelland, David E. Rumelhart, and the PDP Research Group.

    A first reading on connectionism: http://en.wikipedia.org/wiki/Connectionism
    Read also: T:M Chapter 7.

  4. The Brain

    • Experimental methods and disciplines
    • Levels
    • Neural representation: cells, networks, modules
    • Neural computation versus computational neuroscience
    • Brain states, mental states and the effect of molecules
    • October 14th: Guest lecture: Dr. George Kampis (Director of the Budapest Semester in Cognitive Science): Cognitive Science in Central Europe.

  5. Memory and Learning: Concepts and Models

    • Cognitive neuroscience and memory
    • Where are memories stored?
    • Multiple memory systems
    • Declarative and non-declarative memory systems
    • The role of the medial temporal lobe
    • Molecular and cellular basis of memory and learning
    • Beyond molecules and cells: system level approach
    • Machine learning (Dr. Nathan Sprague
    • Milner B, Squire LR, & Kandel ER. (1998). Cognitive neuroscience and the study of memory. Neuron 20:445-468.
    • T:M, Chapter 9

  6. Language: Acquisition, Understanding and Evolution

    7. Read Pinker's paper: Language Acquisition

    Natural language understanding is a sub-field of artificial intelligence research devoted to making computers "understand" statements written in human languages

    Natural Language Understanding

    James Schwartz: Oh My Darwin! Who's the Fittest Evolutionary Thinker of Them All? Lingua Franca, November 1999, Vol. 9, No.8.

    MIRROR NEURONS and imitation learning as the driving force behind "the great leap forward" in human evolution by V.S. Ramachandran

  7. Consciousness and Emotions

    8. Consciousness: from philosophy to experiments

    Crick, F. and Koch, C A framework for consciousness. Nature Neuroscience (2003) 6, 119-126.

    CONSCIOUSNESS EXPLAINED By Daniel C. Dennett read a review from the New York Times: George Johnson: http://www.santafe.edu/~johnson/reviews.dennett.html What are emotions?
    How to represent emotions?
    A useful website on emotion

    The next two papers from from here: http://emotion.bme.duke.edu/Publications.html#confs

    • Fellous J.M., Armony J.L., LeDoux J.E.: Emotion and Computational Neuroscience In 'The handbook of brain theory and neural networks' Second Edition. M.A. Arbib (editor), The MIT Press.
    • Arbib MA and Fellous JM.: Emotions: From Brain to Robot. Trends in Cognitive Science, 8(12):554-561

    Affective computing in the MIT:
    Picard, R. W. (2003), "Affective Computing: Challenges," Int. Journal of Human-Computer Studies, Vol. 59, Issues 1-2, July 2003, pp. 55-64.

    T:M Chapter 10 and 11.

  8. Computational Models of Cognition

    • Architectures and Approaches
    • Symbolic Models
      Rosenbloom, P., Laird, J., Newell, A., McCarl, R. (1991). A preliminary analysis of the Soar architecture as a basis for general intelligence. Artificial Intelligence, vol. 47, pp. 289-235.
    • Connectionist Models

  9. The Mind as a Dynamical System

    • Cognitive agents are dynamical systems
    • Cognitive agents can be understood by dynamical system theory
      van Gelder, T.J. (1998) The dynamical hypothesis in cognitive science. Behavioral and Brain Sciences, 21, 1-14
    • Dynamical cognitive neuroscience: experiment and models

    T:M Chapter 12

  10. Summary and Outlook

    • Cognitive science is an interdisciplinary study of mind and intelligence.
    • Levels, methods, the need of integration.
    • Open problems
    • Institutions, graduate programs

    T:M Chapter 14.