Cognitive Science 200 Spring 2005

Cognitive Science
University Of California, San Diego

COGS 200: Probabilistic Functionalism

Spring 2005

Professor Gary Cottrell, CSE

Course Description

Probabilistic functionalism refers to attempts to model cognition as being in some sense probabilistically optimal. Some popular examples of probabilistic functionalism are models of early visual representations as minimizing redundancy (e.g. Bell & Sejnowski, 1997), models of perception as Bayesian inference (Knill & Richards, 1996), and models of hypothesis testing as maximizing expected information gain (Oaksford & Chater, 1994). While the scope of such models is quite broad, they share an appreciation that the tasks of intelligence are fundamentally noisy, ambiguous, yet statistically structured. Furthermore, the models are based on a common set of tools from probability and information theory.

The goal of this class is to bring together researchers to present and evaluate probabilistically optimal models of various levels of cognition. Is it true that probabilistic functionalism is a research paradigm "that may unify the cognitive sciences" (Movellan & Nelson, 2001)? Or are these models better suited for some domains than others, if the models are successful at all?

Course Information

Discussion Section: Fridays 2-2:50

Lecture: Fridays 3-4:30

Location: CSB 003

Instructor: Gary Cottrell

Discussion Board

Discuss Probabilistic Functionalism

Mailing list

To receive emails about upcoming speakers & readings, you can subscribe to the COGS 200 mailing list by emailing:
majordomo@cogsci.ucsd.edu

and putting in the body of the message:

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where ADDRESS is the email address that you want to have the cs200 mail sent to (e.g. johndoe@ucsd.edu).

Assignments

Paper Topics and Requirements

Meeting Schedule & Readings

DATE	SPEAKER	PAPERS	SPEAKER SLIDES & RESOURCES
4/1/05	Javier Movellan & Virginia de Sa: Probability Tutorial and Introduction to Probabilistic Functionalism	Edelman, S. & Vaina, L. A brief biography of David Marr in The International Encyclopedia of Social and Behavioral Sciences	MacKay, D. (2003) Information Theory, Inference, and Learning Algorithms
		Movellan, J., Tanaka, F., Fortenberry, B., & Aisaka, K.The RUBI Project: Origins, Principles, and First Steps. Submitted to ICDL	Probabilistic Functionalism: A Paradigm for the Scientific Study of Human Nature
		From campus: Chapter 2 of Manning, C. & Schutze, H. (1999) Foundations of Statistical Natural Language Processing also available in the Cognitive Science Building kitchen
4/8/05	Terry Sejnowski: Probabilistic models in neuroscience	Hinton, G. & Sejnowski, T. (1999) Introduction. In Hinton, G. & Sejnowski, T. (eds.) Unsupervised Learning MIT Press pg: viii-xvi.	Ghahramani, Z. Unsupervised Learning
		Jordan, M. & Sejnowski, T. (2001) Introduction. In Jordan, M. & Sejnowski, T. (eds.) Graphical Models: Foundations of Neural Computation MIT Press pg: xi-xxiv.	Assignment of Multiplicative Mixtures in Natural Images
4/15/05	Bruno Olshausen: Intermediate level sensory coding	Nakayama, K., He, Z., & Shimojo, S. (1995) Visual surface representation: A critical link between lower-level and higher level vision. In Kosslyn, S. & Osherson, D. (eds.) Vision: An Invitation to Cognitive Science MIT Press pg: 1-70
		Optional: Olshausen, B. (2003) Principles of image representation in visual cortex. In Chalupa, L. & Werner, J. (eds.) The Visual Neurosciences MIT Press pg: 1603-1615
		Optional: Olshausen, B. & Field, D. (in press) How close are we to understanding V1? Neural Computation
4/22/05	Pamela Reinagle: Early sensory coding	Rieke, Bodnar & Bialek (1995) Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory afferents. Proc R Soc Lond B Biol Sci. Dec 22; 262(1365):259-65.
		Dan, Atick, & Reid (1996) Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory.
		Optional: Gould, S. J. and Lewontin, R. C. (1979) The Spandrels of San Marco and the Panglossian Paradigm: a Critique of the Adaptationist Programme. Proceedings of the Royal Society of London, Series B. Vol. 205, No. 1161 pg: 581-598.
		Optional: Reinagel, P. & Reid, R. (2000) Temporal coding of visual information in the thalamus. J Neurosci. 20(14):5392-400.
		NEW: Barlow, H. (2001) Redundancy reduction revisited. Network: Computation in Neural Systems. 12:241-253.
4/29/05	Jonathan Nelson: Eye movements for concept learning	Nelson, J. D. (in press). Finding useful questions: on Bayesian diagnosticity, probability, impact and information gain. Psychological Review. --pages 1-11 and 42-44 only--
		Nelson, J. D., Cottrell, G. W., & Movellan, J. R. (2004). Explaining eye movements during learning as an active sampling process. International Conference on Development and Learning.
		Optional: McKenzie, C. R. M. (2003). Rational models as theories-not standards-of behavior. Trends in Cognitive Sciences, 7(9), 403-406.
		Optional: Baron, J. (2004) Normative models of judgment and decision making. In D. Koehler & N. Harvey (Eds.), Blackwell Handbook of Judgment and Decision Making Chapter 2, pp. 19-36.
		Optional: Oaksford, M. & Chater, N. (2001). The probabilistic approach to human reasoning. Trends in Cognitive Sciences, 5(8), 349-357.
5/6/05	Javier Movellan: Infomax approaches to behavior organization	Movellan, J. (2003). Infomax control as a model of real time behvaior. INC Machine Perception Lab Technical Report
5/13/05	Emo Todorov: Optimality principles in sensorimotor control	Scott, S. (2004). Optimal feedback control and the neural basis of volitional motor control. Nature Reviews Neuroscience
		Todorov, E. (2004). Optimality principles in sensorimotor control. Nature Neuroscience 7(9) 907-915.
5/20/05	Pierre Baldi: Surprise and eye movements	Itti, L. & Baldi, P. (2005). A principled approach to detecting surprising events in video. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
		Baldi, P. (2004). Surprise: A Shortcut for Attention? In L. Itti, G. Rees, & J. Tsotsos (Eds.) Neurobiology of Attention. Academic Press.
5/27/05	Craig McKenzie: Framing effects and information leakage	Sher, S. & McKenzie, C.R.M. Information leakage from logically equivalent frames.
6/3/05	Jeff Elman: Probabalistic accounts of language comprehension	Elman, J.L., Hare, M., & McRae, K. (2005). Cues, constraints, and competition in sentence processing. In M. Tomasello and D. Slobin (Eds.) Beyond Nature-Nurture: Essays in Honor of Elizabeth Bates. Mahway, NJ: Lawrence Erlbaum.
		Hare, M., McRae, K., & Elman, J.L. (2002). Sense and structure: Meaning as a determinant of verb subcategorization preferences. Journal of Memory and Language, 48, 281-303.
		Optional: Hare, M., McRae, K., & Elman, J.L. (2004). Admitting the admitting verb sense into corpus analyses makes sense. Language and Cognitive Processes, 19, 181-224.

Acknowledgement: Special thanks to Serge Belongie, who kindly provided me with the web site template for his highly successful CSE291 seminar, based upon the web site template kindly provided him by Charles Elkan, from his highly successful CSE 254 seminar!

And special thanks to Patrick Gallagher for some of the pdfs.

Most recently updated on April 23rd, 2005.