Introduction to Cognitive Science

Rules & Connections

Last Update: 20 October 2008

Note: NEW or UPDATED material is highlighted

For instructions on how to access articles from certain journals (notably J. Exp. Psych., Psych. Bulletin, and Psych. Rev.) from buffalo.edu machines,
link to: "Classic (Online) Readings in Cognitive Science"

  1. Artificial Intelligence as a Cognitive Science

    1. What is AI?

    2. Jordan, Michael I.; & Russell, Stuart (2001), "Computational Intelligence", in Wilson, Robert A.; & Keil, Frank C. (eds.) (2001), The MIT Encyclopedia of the Cognitive Sciences (Cambridge, MA: MIT Press).

    3. McCulloch, Warren S., & Pitts, Walter H. (1943), "A Logical Calculus of the Ideas Immanent in Nervous Activity", Bulletin of Mathematical Biophysics (Chicago: University of Chicago Press) 5: 115-133.

      • A classic paper.

    4. Gigerenzer, Gerd; & Goldstein, Daniel G. (1996), "Mind as Computer: Birth of a Metaphor", Creativity Research Journal 9(2-3): 131-144.

      • A good historical overview.

  2. Rules

    1. Overviews from MITECS:

      1. Horgan, Terence; & Tienson, John (2001), "Rules and Representations".

      2. Lewis, Richard L. (2001), "Cognitive Modeling, Symbolic".

    2. The Newell-Simon approach:

      1. Newell, Allen; & Simon, Herbert A. (1956), "The Logic Theory Machine—A Complex Information Processing System", IRE [now, IEEE] Transactions on Information Theory 2(3) (September): 61-79.

      2. Newell, Allen; Shaw, J.C.; & Simon, Herbert A. (1958), "Elements of a Theory of Human Problem Solving", Psychological Review 65(3): 151-166.

      3. Newell, Allen; & Simon, Herbert A. (1961), "Computer Simulation of Human Thinking", Science 134(3495) (22 December): 2011-2017.

      4. Newell, Allen, & Simon, Herbert A. (1976), "Computer Science as Empirical Inquiry: Symbols and Search", Communications of the ACM 19(3) (March): 113-126.

      5. GPS & Soar

        • GPS is a direct descendent of Newell, Shaw, & Simon's Logic Theorist; Soar is a direct descendent of GPS.

      6. Anderson, John R. (2007), How Can the Human Mind Occur in the Physical Universe? (New York: Oxford University Press).

        • The latest description of Anderson's ACT-R theory of cognitive architecture (arguably another descendent of GPS), showing how closely it models the human brain.

        • The title of his book is taken from a lecture by Allen Newell. I believe that the question posed in the title is equivalent to the question I propose as the fundamental question of cognitive science: How is cognition possible?

    3. The SNePS Approach:

      1. Essential SNePS Readings

      2. The Cassie Conversation

    4. The Chomsky approach:

      1. Colorless Green Ideas Sleep Furiously

      2. Chomsky, Noam (1967), "Recent Contributions to the Theory of Innate Ideas", Synthese 17: 2-11; as reprinted in Cummins, Robert; & Cummins, Denise Dellarosa (eds.) (2000), Minds, Brains, and Computers: The Foundations of Cognitive Science, an Anthology (Malden, MA: Blackwell): 452-457.

      3. Chomsky, Noam (1969), "Linguistics and Philosophy", in Sidney Hook (ed.), Language and Philosophy (New York: NYU Press); as reprinted in Cummins, Robert; & Cummins, Denise Dellarosa (eds.) (2000), Minds, Brains, and Computers: The Foundations of Cognitive Science, an Anthology (Malden, MA: Blackwell): 464-483.

      4. Chomsky, Noam (1980), "Rules and Representations", Behavioral and Brain Sciences 3(1): 1-61.

      5. Chomsky, Noam (1980), Rules and Representations (New York: Columbia University Press).

      6. Pylyshyn, Zenon (1988), "Rules and Representations: Chomsky and Representational Realism".

  3. Connections

    1. Overviews from MITECS:

      1. Horgan, Terence; & Tienson, John (2001), "Rules and Representations".

      2. McClelland, James L. (2001), "Cognitive Modeling, Connectionist"

    2. McCulloch & Pitts neural nets

    3. To play with some toy connectionist neural networks, link to: "The Mind Project: Curriculum"

    4. Classic papers on connectionism:

      1. Rosenblatt, F. (1958), "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain", Psychological Review 65(6): 386-408.

        • This reports work done at what was then the Cornell Aeronautical Laboratory, now known as Calspan, located down the road from UB, on Genesee St. opposite the Buffalo-Niagara International Airport!

      2. McClelland, James L.; & Rumelhart, David E. (1981), "An Interactive Activation Model of Context Effects in Letter Perception: Part 1. An Account of Basic Findings", Psychological Review 88(5) (September): 375-407.

      3. Rumelhart, David E.; & McClelland, James L. (1982), "An Interactive Activation Model of Context Effects in Letter Perception: II. The Contextual Enhancement Effect and Some Tests and Extensions of the Model", Psychological Review 89(1) (January): 60-94.

      4. Smolensky, Paul (1988), "On the Proper Treatment of Connectionism", Behavioral and Brain Sciences 11(1): 1-23.

      5. Fodor, Jerry A.; & Pylyshyn, Zenon (1988), "Connectionism and Cognitive Architecture: A Critical Analysis", Cognition 28: 3-71.

  4. On Unconscious Cognition (a.k.a. "Implicit Learning", "Tacit Knowledge", "Intuition", or "Instinct"):

    1. Reber, Arthur S. (1989), "Implicit Learning and Tacit Knowledge", Journal of Experimental Psychology: General 118(3) (September): 219-235.

      • This is the lead article of a special issue, with 2 commentaries and a reply by Reber.

    2. Clark, Andy; & Karmiloff-Smith, Annette (1993), "The Cognizer's Innards: A Psychological and Philosophical Perspective on the Development of Thought", Mind & Language 8(4) (Winter): 487-519.

    3. Seger, Carol Augart (1994), "Implicit Learning", Psychological Bulletin 115(2): 163-196.

    4. Berry, Dianne C. (ed.) (1997), How Implicit Is Implicit Learning? (New York: Oxford University Press).

      • Contains, inter alia:
        Cleeremans, Axel, "Principles for Implicit Learning"

    5. Stanovich, Keith E.; & West, Richard F. (2000), "Individual Differences in Reasoning: Implications for the Rationality Debate", Behaviorial and Brain Sciences 23: 645-665.

    6. Rantala, Veikko (2001), "Knowledge Representation: Two Kinds of Emergence", Synthese 129: 195-209.

      • On the relation of connectionism to GOFAI.

    7. Kahneman, Daniel (2002), "Maps of Bounded Rationality: A Perspective on Intuitive Judgment and Choice" (Nobel Prize Lecture).

    8. Sun, Ron; Xhang, Xi; & Mathews, Robert (2006), "Modeling Meta-Cognition in a Cognitive Architecture", Cognitive Systems Research 7: 327-338.

    9. UPDATED On "automatic" vs. "propositional" learning:

      1. NEW
        Penn, Derek C., & Povinelli, Daniel J. (2007), "Causal Cognition in Human and Nonhuman Animals: A comparative, Critical Review", Annual Review of Psychology 58: 97-118.

      2. Mitchell, Chris J.; De Houwer, Jan; & Lovibond, Peter F. (2008, in press), "The Propositional Nature of Human Associative Learning", Behavioral and Brain Sciences

  5. The Dynamic Systems Approach

    1. van Gelder, Tim (1995), "What Might Cognition Be, if not Computation?", Journal of Philosophy 92(7): 345-381.

      • Note: The article says it's Vol. 91, but it isn't!

    2. van Gelder, Tim (1998), "The Dynamical Hypothesis in Cognitive Science", Behavioral and Brain Sciences 21: 1-14.

    3. van Gelder, Tim (1999), "Dynamic Approaches to Cognition", in R. Wilson & F. Keil (eds.), The MIT Encyclopedia of the Cognitive Sciences (Cambridge, MA: MIT Press): 244-246.

    4. Chalmers, David (ed.) (2005), "Dynamical Systems bibliography"


Copyright © 2007-2008 by William J. Rapaport (rapaport@cse.buffalo.edu)
http://www.cse.buffalo.edu/~rapaport/575/F08/rules-connections.html-20081020