Computer Science and
Engineering 258A
Cognitive Modeling
Fall Quarter 2011
Tuesday/Thursday 2:00-3:30
CSE Building, Room 2154
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 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.
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. Here are some
ideas for projects that I gave to the undergrads in CSE 190.
Obviously, yours should be a bit more cutting-edge!!
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 |
| September 27-Oct 08 |
Gary Cottrell |
Introduction to some Cognitive Models |
Discuss any paper
here... |
||
| October 11 |
Yajaira Gonzales |
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 |
||
| October 17 |
Gary Cottrell |
Backprop tutorial |
|||
| October 18 |
Vivek R |
Deep dyslexia: A case study of
connectionist neuropsychology |
Plaut, D.C., & Shallice, T.
(1993). Deep dyslexia: A case study of connectionist
neuropsychology. Cognitive Neuropsychology, 10(5), 377-500. pdf |
||
| October 20 |
Akshay Balsubramani |
Predicting Human Brain Activity
Associated with the Meanings of Nouns. |
Mitchell, T.M., Shinkareva,
S.V., Carlson, A., Chang, K.-M., Malave, V.L., Mason, R.A., & Just,
M.A. (2008). Predicting Human Brain Activity Associated with the
Meanings of Nouns. Science, 320, 1191-1195. pdf
(paper) pdf
(supplement) website (supplement) |
||
| October 25 |
Anukool Junnarkar | Reassessing working memory |
MacDonald, M.C. &
Christiansen, M.H. (2002). Reassessing working memory: A comment on
Just & Carpenter (1992) and Waters & Caplan (1996) |
||
| October 27 |
Andrew Heiberg |
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 |
||
| November 1 |
Chris Fariss |
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 |
||
| November 3 |
Joonleng Tan |
On the Nature and Scope of
Featural Representations of Word Meaning |
McRae, Ken and De Sa, Virginia
1997 On the nature and scope of featural representations of word
meaning. Journal of
Experimental Psychology: General, 126(2), 99-130. pdf
|
||
| November 8 |
Qihua Wu |
How children learn to value
numbers |
Ramscar, M., Dye, M., Popick,
H.M. & O’Donnell-McCarthy, F. (In press) How children learn to
value numbers: Information structure and the acquisition of numerical
understanding. PLoS ONE.
pdf |
||
| November 10 |
Liam Kavanagh |
Structural aspects of face
recognition and the other-race effect. |
O’Toole, A.J., Deffenbacher,
K.A., Valentin, D., & Abdi, H. (1994). Structural aspects of face
recognition and the other-race effect. Memory and Cognition, 22(2), 208-224. pdf |
||
| November 15 |
NO MEETING: Gary out of town. |
||||
| November 17 |
Ryland Fallon |
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 | ||
| November 22 |
|||||
| November 24 |
Thanksgiving |
||||
| November 29 |
|||||
| December 1 |