Computational Cognition (830:547, Prof. Feldman, Fall 2018)

Theme. Modern cognitive science begins with the premise that the brain is essentially a very special kind of computer. This course will explore the following questions: What does this mean, exactly? Why do we think it? Is it true? And if it is true, so what?

Along the way, the course will introduce some of the foundational ideas that underlie the computational study of the mind. The emphasis will be on fundamental mathematical and conceptual elements of computational models of the mind, as well as historical controversies about how the computational structure of cognition is best understood.

Weekly assignment. Each week, each student will be expected to send me a single question or comment (at least a sentence, at most a long paragraph) on each of that week’s readings by e-mail (This email address is being protected from spambots. You need JavaScript enabled to view it.). These do not have to be eloquent pronouncements or summaries. Rather, they should be casual but thoughtful remarks or queries concerning something that struck you or puzzled you.

Graded assignments. Graded assignments will consist of approximately four short (4-5 page) written assignments, on topics to be announced. In some cases, these will be thought experiments; others will be polemical essays concerning a theoretical, meta-theoretical, conceptual, or formal issue discussed in class. The goal of these assignments is to develop clarity of argument while building awareness of the theoretical ideas covered in class. Essays will be due at irregular intervals to be detailed in class.   


Schedule of topics and readings (tentative)

Week 0: Introduction

   • Braitenberg, V. (1984): Getting around; Fear and aggression; Love; Values and special tastes; Logic; Selection, the impersonal engineer; Concepts; Space, things and movements; Shapes; Getting ideas; Rules and regularities. From Vehicles: Experiments in Synthetic Psychology (excerpt). Cambridge, MA: MIT Press

   • Miller, G. (2003): The cognitive revolution: a historical perspective. Trends in Cognitive Sciences, 7(3), 141–144.

Week 1: Computation

    • Turing, A (1950): Computing machinery and intelligence. Mind, 59, 433–460.

    • Sipser (1997): Decidability. Chapter 3 of: Introduction to the theory of computation. Boston: PWS Publishing, 1997

         - Recommended: Sipser Chs. 0,1,2.

Week 2: Competence and performance

     • Marr, D. (1982): The philosophy and the approach. From Vision: A computational investigation into the human representation and processing of visual information. San Francisco: Freeman.

     • Anderson, J. (1990): Introduction. From The Adaptive Character of Thought. Hillsdale, NJ: Erlbaum.

     • Jonas, E. & Kording, K. (2017) Could a neuroscientist understand a microprocessor? PLoS Computational Biology. 13(1): e1005268, 1-24.

Week 3: Complexity

    • Garey, M. R. and Johnson, D. S. (1979): Computers, complexity, and intractability. From Computers and intractability: A guide to the theory of NP-completeness. New York: W.H.Freeman.

    • Feldman, J. (2016). The simplicity principle in perception and cognition. WIREs Cognitive Science, 7, 330–340.

Week 4: Heuristics and rationality

     • Tversky, A, Kahneman, D (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185, 4157:1124-31.

     • Todd, P. M. & Gigerenzer, G. (2012): What Is Ecological Rationality? From Ecological Rationality: Intelligence in the World (Todd, P. M., Gigerenzer, G. and the ABC Research Group). Oxford: Oxford University Press.

Week 5: Bayesian models I

     • Feldman, J. (2015) Bayesian models of perceptual organization. From Handbook of Perceptual Organization (J. Wagemans, ed.), 1008-1026. Oxford: Oxford University Press.

     • Oaksford, M. and Chater, N. (2009): Precis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning. Behavioral and Brain Sciences, 32, 69–120.

Week 6: Connectionism I

      • Rumelhart, D. E. and McClelland, J. L. (1986) PDP models and general issues in cognitive science. In Parallel Distributed Processing (D. Rumelhart, J. L. McClelland, and the PDP Research Group). Cambridge: M.I.T. Press.

      • Minsky, M. and Papert, S. (1988) Epilogue. From Perceptrons: an introduction to computational geometry: expanded edition. Cambridge: M.I.T. Press (original edition: 1969).

Week 7: Production systems

      • Anderson, J. R. and Lebiere, C. (2003): The Newell Test for a theory of cognition. Behavioral and Brain Sciences, 26, 1-53. 

Week 8: Bayesian models II

      • McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T., Seidenberg, M. S., and Smith, L. B. (2010) Letting structure emerge: connectionist and dynamical systems approaches to cognition. Trends in Cognitive Sciences, 14(8), 348–356.

      • Griffiths, T. L., Chater, N., Kemp, C. Perfors, A. and Tenenbaum, J. B. (2010) Probabilistic models of cognition: exploring representations and inductive biases. Trends in Cognitive Sciences, 14(8), 357-64.

Week 9: Causal models

      • Pearl, J. (2000). Epilogue: The art and science of cause and effect. From Causality. Cambridge: Cambridge University Press.

      • Glymour, C. (2003). Learning, prediction and causal Bayes nets. Trends in Cognitive Sciences 7(1), 43-48.

Week 10: Connectionism II

      • LeCun, Y, Bengio, Y & Hinton, G. (2015) Deep learning. Nature, 521, 436-444.

Week 11: Probabilistic computational neuroscience

      • Pouget, A. and Zemel, R. S. (2011). Population codes. From Bayesian brain: probabilistic approaches to neural coding (Doya, K., Ishii, S., Pouget, A. and Rao, R. P. N., eds.). Cambridge, MA: MIT Press.