Computer Science and
Engineering 258A
Cognitive Modeling
Fall Quarter 2009
Monday/Wednesday 3:30-4:50
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 neural nets or 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. Prize-winning papers are preferred. Since conference papers
are
often only 6-8 pages, more reading beyond that paper may be required to
understand what was done.
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.
Class Schedule:
Some potential papers:
[Some links are included.
For some papers, I have pdf's].
Almost anything by Morten
Christiansen, Nick Chater, Max Reisenhuber, Josh Tenenbaum, Mike Mozer,
Dave
Plaut, Tom Griffiths, Charles Kemp, Jeff Elman, Mark Seidenberg, Jay
McClelland, Sue Becker, Peter Dayan. This is not an exhaustive list!
Suggestions for this list are welcome!
Vision: Object &
Face
recognition, salience, and eye movements.
Itti, L. & Baldi, P. (2006). Bayesian
surprise
attracts human attention. Advances in Neural Information Processing
Systems, 18, 547-554.
Palmeri, T.J., &
Gauthier,
I. (2004). Visual object understanding. Nature Reviews Neuroscience, 5, 291-303. (review paper)
Barlow, H. B. (1961).
Possible principles
underlying the transformation of sensory messages. In Rosenblith, W. A.
(Ed.), Sensory
Communication
(pp. 217-234). Cambridge, MA: MIT Press. (classic)
Cadieu, C., Kouh, M.,
Pasupathy, A., Conner, C., Riesenhuber, M., & Poggio, T.A. (2007). A
Model of
V4 Shape Selectivity and Invariance. J Neurophysiol 98:
1733-1750.
Jiang, X., Rosen, E.,
Zeffiro,
T., VanMeter, J., Blanz, V., & Riesenhuber, M. (2006). Evaluation
of a Shape-Based Model of Human Face Discrimination Using fMRI and
Behavioral
Techniques. Neuron 50: 159-172.
Tong,
M.H., Joyce, C.A., and Cottrell, G.W. (2008) Why is the fusiform face
area
recruited for novel categories of expertise? A neurocomputational
investigation
Brain Research 1202:14-24.
O'Toole, Deffenbacher,
Valentin
& Abdi (1994). Structural aspects of face recognition and the
other-race
effect. Memory and Cognition,
22:208-224.
(classic)
Torralba, Antonio, Oliva,
Aude,
Castelhano, Monica, and Henderson, John. (2006) Contextual guidance of
eye
movements and attention in real-world scenes: The role of global
features on
object search Psychological
Review,
113(4):766-786.
Kanan,
C.M., Tong, M.H., Zhang, L. and Cottrell, G.W. (2009). SUN: Top-down
saliency
using natural statistics. Visual Cognition 17, Issue 6 & 7 pp.
979-1003 (Special Issue on eye
guidance in natural scenes (B. Tatler, Ed.)).
Barrington, L., Marks, T. K.,
Hui-wen
Hsiao, J., & Cottrell, G. W. (2008). NIMBLE: A kernel density model
of
saccade-based visual memory. Journal of Vision, 8(14):17, 1-14 (pre-print) journal version ,
doi:10.1167/8.14.17.
Reading
McClelland and Rumelhart
(1981). An interactive activation model of context effects in letter
perception: Part I and II. Psychological Review, 88. (classic)
Seidenberg, M. S., &
McClelland, J. L. (1989). A distributed developmental model of word
recognition and naming. Psychological
Review, 96(4), 523–568.
(classic)
Hemispheric
processing
Many
papers by
Richard Shillcock and/or Padrhaic Monaghan
Hsiao, Janet Hui-wen,
Shahbazi, Reza, and
Cottrell, Garrison W. (2008) Hemispheric Asymmetry in Visual Perception
Arises
from Differential Encoding beyond the Sensory Level. In Proceedings
of
the 30th Annual Meeting of the Cognitive Science Society.
Schemata, Memory and
Sequential processes
Jordan, M. (1986) Serial
Order:
A parallel distributed processing approach. UCSD Cognitive Science
Technical
Report 8604. (classic)
Botvinick, Matthew &
Plaut,
David (2002) Doing without schema hierarchies: A recurrent
connectionist
approachh to normal and impaired routine sequential action. submitted
to Psych
revew.
Shiffrin, R.M. &
Steyvers,
M. (1997). A model for recognition memory: REM: Retrieving Effectively
from
Memory. Psychonomic Bulletin & Review, 4 (2), 145-166. (classic)
Metcalfe, J. (1993).
Novelty
monitoring, metacognition, and control in a composite holographic
associative
recall model: Implications for Korsakoff's amnesia. Psychological
Review, 100, 3-22. (classic)
McClelland, James L.,
McNaughton, Bruce and O'Reilly, Randy (2001). Why are there
complementary
learning systems in the hippocampus and neocortex: Insights from the
successes
and failures of connectionist models of learning and memory. Psychological
Review.
(warning: long!)
Computational
Psycholinguistics
Plaut, D. C., McClelland, J. L., Seidenberg, M. S.,
and
Patterson, K. (1996). Understanding normal and impaired word reading:
Computational principles in quasi-regular domains. Psychological
Review,
103, 56-115. (classic)
St. John, M. F., &
McClelland, J. L. (1990). Learning and applying contextual constraints
in sentence comprehension. Artificial
Intelligence, 46, 217-257. (classic)
Concept learning
Math-Psych-like papers:
Palmeri, T.J. (1999). Learning
hierarchically structured categories: A comparison of category learning
models.
Psychonomic Bulletin & Review, 6,
495-503.
Nosofsky, R.M., &
Palmeri,
T.J. (1998). A rule-plus-exception model for classify objects in
continuous-dimension spaces. Psychonomic Bulletin & Review, 5, 345-369.