Introduction to Cognitive Science

Rules & Connections

Last Update: Saturday, 28 July 2018

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.

    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.

    5. Critiques of AI/computational cognitive science:

      1. Dreyfus, Hubert L. (1992), What Computers Still Can't Do: A Critique of Artificial Reason (Cambridge, MA: MIT Press).

        • Although published last, this is the 4th version of a critique originally called "Alchemy and Artificial Intelligence" that first appeared as RAND tech report in 1965. Dreyfus is a "continental/phenomenological" philosopher, and this report, now book, prompted many bitter replies as well as much useful AI research, such as better chess-playing programs in order to beat Dreyfus at chess.

        • Dreyfus is still at it:
          Dreyfus, Hubert L. (2012), "A History of First-Step Fallacies", Minds and Machines 22(2) (Summer): 87–99.

      2. Weizenbaum, Joseph (1976), Computer Power and Human Reason: From Judgment to Calculation (New York: W.H. Freeman).

        • Weizenbaum created the ELIZA program in order to show how people could be fooled into thinking that a dumb program could think. This book contains a description of ELIZA, one of the best explanations of Turing machines (the toilet-paper-&-stones model), and a good warning about the improper uses of computers.

      3. Winograd, Terry; & Flores, Fernando (1987), Understanding Computers and Cognition: A New Foundation for Design (Reading, MA: Addison-Wesley).

        • Winograd wrote one of the first and most successful natural-language-processing programs, SHRDLU. His "conversion to the dark side" by Chilean activist and computer scientist Flores was considered disappointing, if not downright treacherous, by many of his former colleagues. Winograd was also a co-founder of Computer Professionals for Social Responsibility.

      4. Searle, John R. (1980), "Minds, Brains, and Programs", Behavioral and Brain Sciences 3: 417–457.

        • This is the original source of the Chinese Room Argument, which some of you are already familiar with and that we will be discussing later in the semester.

  2. What Is Computation? [.ppt]

  3. 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".

  4. Connections

    "A distinctive feature of neural networks is that the designer or programmer does not directly specify all the particulars of a computation. Instead, the neural net is 'trained' by exposure to thousands of examples, and it adjuests its internal parameters to maximize its own success. When the training is complete, we have a machine that can answer questions, but we don't necessarily know how it computes the answers. I find this situation midly frustrating. On the other hand, it's a predicament I am familiar with at the most intimate level. I, too, understand speech and recognize faces—and I can't explain how I do it."

    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.

    5. Sarle, Warren S. (1994), "Neural Networks and Statistical Models", Proceedings of the 19th Annual SAS Users Group International Conference: 1538–1550.

      • A survey of the relationships between connectionist systems and statistical algorithms, with useful references.

    6. Modha, Dharmendra S.; Ananthanarayanan, Rajagopal; Esser, Steven K.; Ndirango, Anthony; Sherbondy, Anthony J.; & Singh, Raghavendra (2011), "Cognitive Computing", Communications of the ACM 54(8) (August): 62–71; doi:10.1145/1978542.1978559.

      • "Unite neuroscience, supercomputing, and nanotechnology to discover, demonstrate, and deliver the brain's core algorithms."

  5. 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. Sloman, Steven A. (1996), "The Empirical Case for Two Systems of Reasoning", Psychological Bulletin 119(1): 3–22.

      • An excellent survey article on the 2 "systems".

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

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

    6. Dienes, Zoltan; & Perner, Josef (1999), "A Theory of Implicit and Explicit Knowledge", Behavioral and Brain Sciences 22: 735–808.

      • A good survey of the implicit-explicit distinction (which may be orthogonal to the System-1/System-2 distinction).

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

    8. Haidt, Jonathan (2001), "The Emotional Dog and Its Rational Tail: A Social Intuitionist Approach to Moral Judgment", Psychological Review 108(4): 814–834.

      • "moral judgment is generally the result of quick, automatic evaluations (intuitions)", whereas "moral reasoning is usually a post hoc construction generated after a judgment has been reached"
      • See esp. Table 1 (p. 818)

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

      • On the relation of connectionism to GOFAI.

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

    11. Evans, Jonathan St.B.T. (2003), "In Two Minds: Dual-Process Accounts of Reasoning", Trends in Cognitive Sciences 7(10) (October): 454–459.

      • A good survey article on "System 1" and "System 2".

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

    13. On "automatic" vs. "propositional" learning:

      1. 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

    14. DeWall, C. Nathan; Baumeister, Roy F.; & Masicampo, E.J. (2008), "Evidence that Logical Reasoning Depends on Conscious Processing", Consciousness and Cognition 17: 628–645; doi:10.1016/j.concog.2007.12.004

    15. Simons, Daniel J.; & Chabris, Christopher F. (2010), "The Trouble with Intuition", The Chronicle [of Higher Education] Review 56(37) (June 4): B13–B15.

    16. Evans, Jonathan St.B.T.; & Stanovich, Keith E. (2013), "Dual-Process Theories of Higher Cognition: Advancing the Debate", Perspectives on Psychological Science 8(3): 223–241.

      • A survey of different versions of the dual-process accounts (a.k.a., "System 1/2", "Type 1/2"), with replies to criticisms.

    17. Shea, Nicholas; & Frith, Chris D. (2016), "Dual-Process Theories and Consciousness: The Case for 'Type Zero' Cognition", Neuroscience of Consciousness 2016(1) (January).

      • Argues for a third category (over and above Systems 1 & 2), one of automatic (roughly, unconscious) processes over unconscious representations, which, to my mind, sounds like philosophical zombie processes.

      • For a symposium on this paper, with replies by the authors, link to "Symposium on Shea and Frith" on "The Brains Blog"

    18. Melnikoff, David E.; & Bargh, John A. (2018), "The Mythical Number Two", Trends in Cognitive Sciences 22(4) (April): 280–293.

      • "[T]he untested and untenable assumption that psychological phenomena can be partitioned into two types, we argue, has the consequence of systematically thwarting scientific progress. … [T]the dual-process typology is a convenient and seductive myth …"

          NEW
          For a rebuttal and reply, see:
        1. Pennycook, Gordon; De Neys, Wim; Evans, Jonathan St.B.T.; Stanovich, Keith E.; & Thompson, Valerie A. (2018), "The Mythical Dual-Process Typology", Trends in Cognitive Sciences 22(8) (August): 667–668.

        2. Melnikoff, David E.; & Bargh, John A. (2018), "The Insidious Number Two", Trends in Cognitive Sciences 22(8) (August): 668–669.
          NEW

    19. On the relation between tacit and explicit knowledge:

        "When vetting a painting, …[Martin Kemp, "a leading scholar of Leonardo"] proceeds methodically, analyzing brushstrokes, composition, iconography, and pigments—those elements which may reveal an artist's hidden identity. ‘The initial think is just that immediate reaction, as when we're recognizing the face of a friend in a crowd,’ he explains. ‘You can go on later and say, "I recognitize her face because the eyebrows are like this, and that is the right color of her hair," but, in effect, we don't do that. It's the totality of the thing. It feels instantaneous.’" (p. 51.)

        "The art historian Bernard Berenson described his talent as a ‘sixth sense.’ ‘It is very largely a question of accumulated experience upon which your spirit sets unconsciously,’ he said. ‘When I see a picture, in most cases, I recognize it at once as being or not being by the master it is ascribed to; the rest is merely a question of how to fish out the evidence that will make the conviction as plain to others as it is to me.’" (pp. 51–52.)

        • Grann, David (2010), "The Mark of a Masterpiece", New Yorker (July 12 & 19): 50–61, 63–71.

    Dilbert.com

    Agnes

  6. 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"


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