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.
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
Week 1: Computation
Week 2: Competence and performance
Week 3: Complexity
Week 4: Heuristics and rationality
• 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
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.
Week 7: Production systems
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
Week 10: Connectionism II
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.