Computer Science and
Engineering 258A
Cognitive Modeling
Spring Quarter 2013
Tuesday/Thursday 2:00-3:30
Thurgood Marshall 102
Professor: Gary Cottrell
Office: CSE 4130
Phone: 858-534-6640
e-mail: gary@ucsd.edu
Course Description
CSE 258A is a graduate
seminar
devoted to recent research in Cognitive Modeling. This is not an introductory
course, so the
prerequisite is at least one graduate-level course (at UCSD or
elsewhere) in
some aspect of AI, Cognitive Science, Computational Cognitive
Neuroscience, or
closely related areas such as machine learning, statistics or pattern
recognition. Appropriate courses at UCSD include CSE 250A/B (Principles of AI:
Learning),
CSE 291 (Probabilistic Methods
in AI and
Machine Learning), Cognitive
Science 260 (Pattern Recognition), BGGN
246 -
Reinforcement Learning and Decision Making. If you have
no
familiarity with at least one of neural nets, machine learning, or
Bayesian modeling,
you
should probably not take this course.
This course will explore
cognitive modeling with an emphasis on the student interests. It is
intended
for CSE, Cognitive Science and Interdisciplinary PhD in Cognitive
Science
Program students. The emphasis will be on models that attempt to match
psychological data about the process in question. Depending on student
interest, we will look at models pertaining to language processing,
decision-making, memory, concept formation, sequential processes, and
vision.
On
the first day, we will have an organizational meeting and the
instructor will
lecture on cognitive modeling with reference to his own work. By the
end of the
first week, the students should each pick a paper to give a
presentation on.
Then, in each class meeting, one (or two, given the number of students
in this class! Meaning, two people can talk on one paper...) student
will give a talk lasting about
45-60
minutes presenting a recent technical paper in detail. In
questions
during the talk, and in the final 20 minutes, all seminar participants
will
critically discuss the paper and the issues raised by it. Again, given
the number of students in the class, some of these will have to be done
by a team of two.
The
student should read at least two papers: A classic paper in the field
that can
be considered antecedent to the recent research paper (if none can be
found,
discuss this with the professor). Often it is necessary to read more
than one
antecedent paper. The more recent paper should be chosen from a
high-quality
conference (in this category, prize-winning papers are preferred!) or
journal. If you choose a conference paper, since they
are
often only 6-8 pages, more reading beyond that paper may be required to
understand what was done. Suggested papers are
listed here.
In addition to presenting
recent research, there will be a final project. The project guidelines
and schedule, a lot of which was cribbed from Charles Elkan's project
guidelines are here.
Projects should be done in pairs or small
groups. The
more people involved, the more involved the project should be! The
project is
worth 50% of your grade.
The talk I gave in class
is here.
This is an example of
the class schedule from a previous iteration of this course. It will be
updated when I get your paper choices and dates.
| DATE | PRESENTER | TITLE |
|
DISCUSSION PAGE |
SLIDES |
| April 2-9 |
Gary Cottrell |
Introduction to some Cognitive Models |
What can computational models tell us about face processing? |
Discuss any paper
here... |
ppt |
| April 16 |
Nathan Feldman & Rick Shih |
The PDP approach to semantic
cognition. |
McClelland, J.L., & Rogers,
T.T. (2003). The parallel distributed processing approach to semantic
cognition. Nature Reviews Neuroscience, 4, 310-322. pdf Cree, G. & McRae, K. (2003)Analyzing the Factors Underlying the Structure and Computation of the Meaning of Chipmunk, Cherry, Chisel, Cheese, and Cello (and Many Other Such Concrete Nouns). JEP: General 132(2):163–201 pdf |
pdf |
|
| April 18 |
Gary Cottrell |
Backprop tutorial |
Rumelhart, D.E., Hinton, G.E.,
and Williams, R.J. (1986) Learning internal representations by error
propagation. In David E. Rumelhart, James L. McClelland, and the PDP
Research Group: Parallel
Distributed Processing: Explorations of the Microstructure of
Cognition, Vol 1: Foundations. MIT Press: Boston. pdf Additional reading for practical tips: Lecun, Y., Bottou, L., Orr, G., and Muller, K-R. (1998) Efficient Backprop. In G. Orr and K-R Muller (Eds.) Neural Networks: Tricks of the Trade. Springer-Verlag: Berlin. pdf |
ppt |
|
| April 23 |
Michael Kirby |
Learning and Evolution |
Hinton, G.E., and S.J. Nowlan,
1987. How learning guides evolution, Complex
Systems, 1:495-502. pdf Nolfi, S., J.L. Elman, and D. Parisi, 1994. Learning and evolution in neural networks, Adaptive Behavior, 1:5-28. pdf Nolfi, S., and D. Floreano, 1999. Learning and evolution, Autonomous Robots, 1: 89-113. pdf OPTIONAL (background on the GA): Belew, R.K., J. McInerney J., and N.N. Schraudolph N.N., 1992. Evolving networks: using the genetic algorithm with connectionist learning, in Proceedings of the Second Conference on Artificial Life, (C.G. Langton et. al eds.), Reading, MA: Addison-Wesley. pdf |
||
| April 25 |
Gary Cottrell |
An example of a Bayesian Model |
Tenenbaum, J.B. (1999). Bayesian
modeling of human concept learning. In M.S. Kearns, S.A. Solla, &
D.A. Cohn (Eds.), Advances in Neural Information Processing Systems,
11. Cambridge, MA: MIT Press. pdf |
||
| April 30 |
Pardis Miri |
||||
| May 2 |
Clifford Champion |
Temporal Coordination and
Adaptation to Rate Change in Music Performance |
Loehr, J.D., Large, E.W., and Palmer, C. (2011) Temporal Coordination and Adaptation to Rate Change in Music Performance. JEP:HPP 37(4):1292–1309 pdf | ||
| May 7 |
Eric Morgan |
||||
| May 9 |
NO MEETING: Gary out of town. | ||||
| May 14 |
NO MEETING: Gary out of town. | ||||
| May 16 |
Zeynep
Kurultay |
||||
| May 21 |
Qiheng Wang |
Models of Similarity |
Tenenbaum, J.B., &
Griffiths, T.L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain
Sciences, 24, 629-640. pdf Tversky, A. (1977). Features of Similarity. Psychological Review, 84(4), 327-352. pdf |
||
| May 23 |
.Albert Chu/Charlie
Chen |
||||
| Daniel Gallagher | |||||
| May 28 |
Gary Cottrell |
Understanding Normal and
Impaired Word Reading: Computational Principles in Quasi-Regular
Domains. |
Plaut, D.C., McClelland, J.L., Seidenberg, M.S., & Patterson, K. (1996). Understanding Normal and Impaired Word Reading: Computational Principles in Quasi-Regular Domains. Psychological Review, 103, 56-105. pdf | ||
| May 30 |
Gary Cottrell |
Learning to learn causal models. | Charles Kemp, Noah D. Goodman, Joshua B. Tenenbaum (2010) Learning to learn causal models. Cognitive Science 34:1185–1243 pdf | ||
| June 4 |
Gary Cottrell |
Rational approximations to category learning. | Sanborn, A.N., Griffiths, T.L., & Navarro, D.J. (2010). Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117(4), 1144-1167. pdf | ||
| June 6 |
Gary Cottrell |
||||