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Explanation in Cognitive Science

The purpose of the seminar is to review explanatory methods used in cognitive science. Cognitive science is an interdisciplinary enterprise, with various research agendas and profiles, so to understand various explanatory methods it is indispensable to look at different approaches used to explain cognitive phenomena. We will start with classical computational and symbolic theories of cognition, and then look at parallel distributed processing (PDP) models, dynamical, sensorimotor and embodied theories of the mind, and behavioral robotics. It will be also important to see what is the role of mental representation in explaining cognition, on the various approaches to computer simulation and models. In a way, this seminar can be treated as an introduction to the methodology of cognitive science. This introduction stresses the explanatory pluralism in contemporary research rather than argues for a single model that would fit all needs.

 

The model of explanation used in cognitive science that will be reconstructed based on actual and classical research papers. Yet, we will also look at programmatic manifestos and briefly review the accounts of explanation used from philosophy of science (the covering-law model and mechanistic explanation).

Requirements

The students will be required to prepare short handouts that summarize the main explanatory approach in the paper analyzed, including:

· what phenomena are to be explained

· what is the recommended level of description (neural, symbolic, ecological...)

· what is the way the phenomenon was described (verbal, as an equation, on a diagram, as a computer program...)

· is there a role for representations to play

· is there a general theory behind the explanation.

 

The handouts are 50% of the final mark. Students will also have to pass the final, multiple-choice test in the last meeting.

 

Previous course in Philosophy of Science is an advantage, though it is not strictly required, as various courses in cognitive psychology, cognitive neuroscience, etc.

 

Detailed course description

Recommended (not obligatory) readings for the whole seminar:

 

· Pinker, Steven. 1999. How the mind works. WW Norton & Company.

· Clark, A. 2001. Mindware: An introduction to the philosophy of cognitive science. Oxford: Oxford University Press, USA.

· Hardcastle, Valerie Gray, 1996. How to Build a Theory in Cognitive Science. State University of New York Press.

· Boden, Margaret A. 2008. Mind as Machine: A History of Cognitive Science. Oxford: Oxford University Press.

· Sun, Ron, ed.. 2008. The Cambridge Handbook of Computational Psychology. Cambridge: Cambridge University Press.

 

Seminar 1. Explanation in psychology: general perspective

Organizational matters and introductory lecture.

Reading:

Wright, Cory, and William Bechtel, Mechanisms and Psychological Explanation, in: Thagard, P. 2007. Philosophy of psychology and cognitive science. North Holland.

Seminar 2. Symbolic computation

Reading:

Newell, Allen; Simon, H. A. (1963), “GPS: A Program that Simulates Human Thought”, in Feigenbaum, E.A.; Feldman, J. (eds.), Computers and Thought, New York: McGraw-Hill.

 

Seminar 3. Brain-inspired computation: Marr's levels

Reading:

Marr, David, and T. Poggio. 1976. Cooperative Computation of Stereo Disparity. Science 194, no. 4262: 283-287.

Additional reading for volunteers:

Marr, David. 1982. Vision. A Computational Investigation into the Human Representation and Processing of Visual Information. New York: W. H. Freeman and Company (excerpts)

Seminar 4. Brain-inspired computation: PDP models

Reading: Rumelhart, D. E., and McClelland, J. L. 1986. “On learning the past tenses of English verbs”, in J. McClelland, D. E. Rumelhart, and the PDP Research Group (eds.), Parallel Distributed Procesing, volume 2, MIT Press: Cambridge.

Additional readings for volunteers:

McClelland, James L. 2009. The Place of Modeling in Cognitive Science. Topics in Cognitive Science 1, no. 1: 11-38.

Churchland, Patricia Smith, and Terrence J. Sejnowski. 1992. The Computational Brain. Cambridge, Mass.: MIT Press.

Gluck, Mark A., and Catherine E. Myers. 2001. Gateway to memory: An introduction to neural network modeling of the hippocampus and learning. Cambridge, Mass.: MIT Press (chapter 3 for introduction, chapter 6 for a model)

Seminar 5. Dynamic systems approach

Reading:

Esther Thelen. 1995. Time-Scale Dynamics and the Development of an Embodied Cognition, in: R. F. Port, and T. van Gelder (eds.), Mind as Motion. Explorations in the Dynamics of Cognition, MIT Press.

 

Additional readings for volunteers:

R. F. Port, and T. van Gelder (eds.), Mind as Motion. Explorations in the Dynamics of Cognition, MIT Press (chapter 1 and 2 for introduction to dynamic systems).

 

Seminar 6. Probabilistic models

Reading:

Griffiths, Thomas L, Nick Chater, Charles Kemp, Amy Perfors, and Joshua B Tenenbaum. 2010. Probabilistic models of cognition: exploring representations and inductive biases. Trends in cognitive sciences 14, no. 8 (June): 357-364.

 

Seminar 7: Logical models

Reading:

Keith Stenning, Michiel van Lambalgen.2008. Human Reasoning and Cognitive Science. MIT Press, chapter 3 (a discussion of Wason's task and its logical models).

Additional readings for volunteers:

Selmer Bringsjord, Declarative/Logic-Based Cognitive Modeling, in: Sun, Ron, ed.. 2008. The Cambridge Handbook of Computational Psychology. Cambridge: Cambridge University Press, chapter 5 (slightly technical in section 4 but otherwise rich in information).

 

Seminar 8. Behavioral robotics

Reading:

Barbara Webb. 2008. Using Robots to Understand Animal Behavior, in:Brockmann, H. Jane, Timothy J. Roper, Marc Naguib, Katherine E. Wynne-Edwards, Chris Barnard, and John C. Mitani, (eds.). 2008. Advances in the Study of Behavior. Vol. 38. Elsevier.

 

Additional readings for volunteers:

Arkin, R.C. 1999. Behavior-based robotics. Cambridge, Mass.: MIT Press (background on robotics).

Seminar 9. Explanatory role of representation: Classical approach

Reading:

Pylyshyn, Zenon. 1986. Computation and Cognition, MIT Press, chapter 2.

Additional readings for volunteers:

Newell, Allen. 1980. Physical symbol systems. Cognitive Science: A Multidisciplinary Journal 4, no. 2: 135-183.

Seminar 10. Explanatory role of representation: Imagery debate

Kosslyn, S. M. (1994). Image and brain: The resolution of the imagery debate. Cambridge, MA: MIT Press, chapter 1.

Additional readings for volunteers:

Pylyshyn, Zenon. 1986. Computation and Cognition, MIT Press, chapter 8.

Kosslyn, S. M. 2005. Mental images and the brain. Cognitive Neuropsychology, 22, 333-347.

Kosslyn, S. M., Ganis, G., and Thompson, W. L. 2003. Mental imagery: Against the nihilistic hypothesis. Trends in Cognitive Science, 7, 109-111.

 

Seminar 11. Explanatory role of representation: Connectionism

Hinton, Geoffrey E. 2007. Learning multiple layers of representation. Trends in cognitive sciences 11, no. 10 (October): 428-34.

Additional readings for volunteers:

Churchland, Patricia Smith, and Terrence J. Sejnowski. 1992. The Computational Brain. Cambridge, Mass.: MIT Press, chapter 4.

Seminar 12. Explanatory role of representation: Behavioral robotics.

Reading:

Brooks, Rodney A. 1991. Intelligence without representation. Artificial Intelligence 47 (October): 139-159.

Additional readings for volunteers:

Brooks, Rodney.A. 1991. Elephants don’t play chess. Robotics and Autonomous Systems 6: 3–15.

 

Seminar 13. Simulation, computation and modeling: Chinese Room

Reading:

Searle, John (1980), "Minds, Brains and Programs", Behavioral and Brain Sciences 3 (3): 417–45.

Additional readings for volunteers:

Chalmers, David (1996), The Conscious Mind: In Search of a Fundamental Theory, Oxford University Press, chapter 9.

 

Seminar 14. Final evaluation and debate.

General discussion and debate on modeling paradigms and explanation. Ideally, we should have students defending their own stance against others.

Reading:

McClelland, James L. 2009. The Place of Modeling in Cognitive Science. Topics in Cognitive Science 1, no. 1: 11-38.

 

Seminar 15: Final test.

Multiple-choice test on general aspects of explanatory models in cognitive science. Note: the technical details of models will not be covered in the test, only the general principles of methodology.