Cognitive Science 200 Spring 2011

COGS 200: Cognitive Modeling
Interdisciplinary Ph.D. Program in Cognitive Science
UNIVERSITY OF CALIFORNIA, SAN DIEGO

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Cognitive Science 200: Cognitive Modeling
(Section ID: 715072)
Friday afternoons, CSB 003
Student discussion session: 2-2:50PM
Public lecture: 3-4:30
Organizer: Gary Cottrell

Spring 2011

To join the cs200 mailing list to receive announcements of talks, see this instruction page. Cognitive Science 200 is an interdisciplinary seminar of changing topics, and is used as a mechanism for Ph.D. students in the Interdisciplinary Ph.D. Program and in the Cognitive Science Department to achieve breadth. This quarter, the topic is Cognitive Modeling.

The role of cognitive modeling in understanding how the brain works is well-established. Implemented models force researchers to be honest about their assumptions, they are generally much more accurately predictive than verbal theories, they allow exploration outside of realistic parameter space to understand why things are the way they are, they can be used in an exploratory fashion to try to understand what we might see if we could only get the data, they can be analyzed in ways humans (or animals) cannot, they can be manipulated in ways people and animals cannot, and they can make counterintuitive predictions. Thus cognitive models are a tool that is complementary to behavioral experiments, brain imaging, and neurophysiological experiments. Cognitive models can also be informed by and inform such experiments in a mutually beneficial interaction.

We have invited a number of modelers of both the neural network and Bayesian model stripe, with some hope of finding some common ground between the two. The speakers have modeled language, attention & perception, decision making, development, memory, and the lobster stomatogastric ganglion (just seeing if you are paying attention!). Two of our speakers are Rumelhart prize winners, some are probably future prize winners! See if you can guess which ones are which! ;-) Students in the course will be required to read papers and to write a paper at the end of the quarter.

The room for Cogsci 200 is Cognitive Science Building 003. The meeting times are Fridays 2-2:50PM for registered students, and 3:00-4:50PM for the lectures (to which the UCSD Cognitive Science community is invited). This will be followed usually by the cognitive science happy hour in the cog sci building courtyard, where students and speakers can interact in a more relaxed manner.

The graduate student section from 2-2:50 will involve the professor using the dreaded index card method: students will be asked questions about the papers that are intended to generate some discussion and understanding of the material. Students are therefore expected to have done the reading before class. The method involves index cards with every student's name on them. These are shuffled at the beginning of class, and then students are asked questions in order of their appearance on the card. The first question is almost always, "What is the point of this paper?", and is often asked several times until we converge on one or more main themes of the paper.

The requirements for the class are:

0) attendance at all lectures and participation in all discussion sections. That said, if you have a reasonable conflict for one or two talks over the course of the quarter, it is acceptable to miss class.
1) reading the assigned papers (usually 2 per week);
2) being able to answer questions about them in discussion section;
3) asking the speaker a question as often as possible - a necessary academic skill! and
4) writing an approximately 10 page research proposal that is of your own choosing - it could be an extension to one of the topics covered in the lectures, tesing a hypothesis about salience or attention, pitting the various models against one another, etc. It should be specific enough that there are clear criteria for success or failure. The draft of this is due in the 8th week, the final version is due on the Monday of finals week.

REGISTRATION

Students may take the seminar only for four units of S/U credit. Students should register for COGS 200, section id 715072. If you must have a letter grade (because of your departmental requirements), please see me and let me know!

The readings for the first week are below.


*DATE*	*PRESENTER*	*TITLE (click for abstract)*	*PAPER*
04/01/2011	Gary Cottrell, UCSD	Cognitive Modeling, an Introduction using My Favorite Model.	McClelland & Rumelhart (1980) An Interactive Activation Model of Context Effects in Letter Perception: Part 1: An Account of Basic Findings. Pscyhological Review 88(5):375-407. [pdf] This one is not required, but this is where they cash out the predictions of the model, so it is definitely worth a read: Rumelhart & McClelland (1981) An Interactive Activation Model of Context Effects in Letter Perception: Part 2: The Contextual Enhancement Effect and Some Tests and Extensions of the Model. Pscyhological Review 89(1):60-94. [pdf] Also helpful, but more optional, as I doubt I will get to it, is: Dailey, Matthew N., Cottrell, Garrison W., Padgett, Curtis, and Ralph Adolphs (2002) EMPATH: A neural network that categorizes facial expressions. Journal of Cognitive Neuroscience 14(8):1158-1173. [pdf]
04/08/2011	Rosie Cowell, UCSD	Simulating memory: do amnesics forget because old things look new, or because new things look old?	1. Cowell, R.A., Bussey, T.J., Saksida, L.M. (2006). Why Does Brain Damage Impair Memory? A Connectionist Model of Object Recognition Memory in Perirhinal Cortex Journal of Neuroscience26(47):12186 –12197 [pdf] 2. McTighe, S.M., Cowell, R.A., Winters, B.D., Bussey, T.J., and Saksida, L.M. (2011). Paradoxical False Memory for Objects After Brain Damage Science 330: 1408-1410. [pdf] 3. Supplementary Online Material for McTighe, S.M., Cowell, R.A., Winters, B.D., Bussey, T.J., and Saksida, L.M. (2011). Paradoxical False Memory for Objects After Brain Damage Science 330: 1408-1410. [pdf]
04/15/2011	Tom Griffiths, UC Berkeley	Connecting levels of analysis for probabilistic models of cognition	Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) How to grow a mind; Statistics, structure, and abstraction. Science, 331, 1279-1285. [pdf] Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14, 357-364. [pdf] Kalish, M. L., Griffiths, T. L., & Lewandowsky, S. (2007). Iterated learning: Intergenerational knowledge transmission reveals inductive biases. Psychonomic Bulletin and Review. [pdf]
04/22/2011	Dave Huber, UCSD	Immediate Priming and Cognitive Aftereffects	Huber, D.E. & O’Reilly, Randall C. (2003) Persistence and accommodation in short-term priming and other perceptual paradigms: temporal segregation through synaptic depression. Cognitive Science 27:403–430 [pdf] Tian, Xing & Huber, D.E. (2010) Testing an associative account of semantic satiation. Cognitive Psychology 60:267–290. [pdf]
04/29/2011	Ginny de Sa, UCSD	Modeling semantic memory -- the role of feature correlations	McRae, Ken, de Sa, Virginia, and Mark S. Seidenberg (1997) On the Nature and Scope of Featural Representations of Word Meaning. Journal of Experimental Psychology: General 126(2):99-130 [pdf] McCrae, K., Cree, George S., Westmacott, R., and de Sa, V. (1999) Further evideance for feature correlations in semantic memory. Canadian Journal of Experimental Psychology. 53(4):360-373. [pdf] Cottrell, G.W. (2003) Attractor Networks. In Lyn Nadel (Ed.) Encyclopedia of Cognitive Science, London: Nature Publishing Group, pp. 253 – 262. [pdf]
05/06/2011	Jeff Elman UCSD	What do we want from our models?	Paul D. Allopenna, James S. Magnuson, and Michael K. Tanenhaus (1998) Tracking the Time Course of Spoken Word Recognition Using Eye Movements: Evidence for Continuous Mapping Models. J. Memory and Language38:419-439. [pdf] Elman, Jeff (1990) Finding Structure in Time. Cognitive Science 14: 179-211. [pdf]
05/13/2011	Josh Tenenbaum, MIT (via skype)	Modeling the structure, function and origins of common-sense knowledge with probabilistic programs	Baker, Chris, Saxe, Rebecca, and Tenenbaum, Josh (2011) Bayesian Theory of Mind: Modeling Joint Belief-Desire Attribution. In Proceedings of the 2011 Cognitive Science Society Meeting, Boston, MA. [pdf] Hamrick, Jessica, Battaglia, Peter, and Tenenbaum, Josh (2011) Internal physics models guide probabilistic judgments about object dynamics. In Proceedings of the 2011 Cognitive Science Society Meeting, Boston, MA. [pdf]
05/20/2011	Jay McClelland, Stanford	REMERGE: A new approach to the neural basis of generalization and memory-based inference	Here are three relevant papers. They are listed from broadest to most technical. For people who don't know the complementary learning systems theory, they should read the first two. For those who already know it, they can read either of the first two and the third. They are all moderate in length. McClelland, J. L. (2011). Memory as a constructive process: The parallel-distributed processing approach. In S. Nalbantian, P. Matthews, and J. L. McClelland (Eds.), The Memory Process: Neuroscientific and Humanistic Perspectives. Cambridge, MA: MIT Press, pp. 129-151. [pdf] McClelland, J. L. & Rogers, T. T. (2003). The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4, 310-322. [pdf] McClelland, J. L. & Goddard, N. (1996). Considerations arising from a complementary learning systems perspective on hippocampus and neocortex. Hippocampus, 6, 654-665. [pdf]
05/27/2011	Angela Yu, UCSD	Optimal Decision-Making in Inhibitory Control	Pradeep Shenoy, Raj Rao, & Angela Yu (2011) A Rational Decision-Making Framework for Inhibitory Control. In Advances in Neural Information Processing Systems 23 (J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R.S. Zemel and A. Culotta, Eds.) Nips Foundation. [pdf] Angela Yu & Jonathan Cohen (2009) Sequential effects: Superstition or rational behavior? In Advances in Neural Information Processing Systems 21 (D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, Eds.) [pdf]
06/03/2011	Mike Mozer, CU Boulder	Improving human learning and memory via cognitive models	Mozer, M. C., Pashler, H., Cepeda, N., Lindsey, R., & Vul, E. (2009). Predicting the optimal spacing of study: A multiscale context model of memory. In Y. Bengio, D. Schuurmans, J. Lafferty, C.K.I. Williams, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 22 (pp. 1321-1329). La Jolla, CA: NIPS Foundation. [pdf] Lindsey, R., Mozer, M. C., Cepeda, N. J., & Pashler, H. (2009). Optimizing memory retention with cognitive models. In A. Howes, D. Peebles, & R. Cooper (Eds.), Proceedings of the Ninth International Conference on Cognitive Modeling (ICCM). Manchester, UK. [pdf]

The instructor is Professor Gary Cottrell, whose office is CSE Building room 4130. Feel free to send email to arrange an appointment, or telephone (858) 534-6640.

Most recently updated on May 4th, 2011 by Gary Cottrell, gary@ucsd.edu