Psychology 411-301: Modeling Cognition and Perception ...

Psychology 411-301: Modeling Cognition and Perception
Description
This course will provide an overview of computational models of perception, memory,
language, knowledge representation, learning, and the effects of brain damage on cognition.
These models provide a concrete way to understand how cognition and perception arise from
the coherent engagement of distributed populations of neurons and how learning and
development occur through the adjustment of the connections between the neurons. The
course will consist of lectures describing the theory behind the models and their
implementation and hands-on lab exercises in which students will get experience designing and
running simulations.
Prerequisites: at least one course in cognitive psychology, cognitive science, or cognitive
neuroscience. Familiarity with linear algebra and basic programming is recommended.
Course Structure
This course combines lecture and hands-on lab exercises. A typical class meeting will begin with
a lecture portion during which empirical and theoretical motivations for a model and model
architecture will be presented, followed by a hands-on portion during which students will work
on building and running simulations. Students will write up the results of the hands-on
exercises as lab reports and there will be a final project.
Grading
Lab reports (60%): For each of the hands-on lab exercises students will hand in a lab report
assignment that summarizes the empirical issue(s) of interest, model design, simulation results,
and simple conclusions. Reports can include graphs, tables, etc. as necessary and should be
approximately 2-6 single-spaced pages (brief, but complete). There will be 7 such assignments,
each one will be worth 10% of the final grade. The lowest lab report grade will be dropped. Lab
reports will be due on the day of the next class meeting.
Final project (40%): For the final project students will use computational modeling to explore a
research question of their own. The research question must be approved by the instructor and
it is recommended that students check in periodically with the instructor while working on the
final project. The final project report should include a review of behavioral data and relevant
theories, motivation for the model, model design, results, and conclusions. The final project
report must be submitted before the first day of final exams (Dec. 15).
Extra credit: Find a primary research article that uses computational modeling and write a
summary of the article (1-2 pages) that covers the relevant theoretical, empirical, and technical
issues. The article must be approved in advance by instructor. The summaries will be scored on
a 0-5 scale and the points added to the student’s final grade. Summaries can be submitted any
time before the end of classes (Dec. 10), but each student can submit only one summary per

week. Tip: you’ll need to read and understand articles for your final project anyway, so writing
summary paper(s) for those articles would be an easy way to get bonus points.
Readings
The primary reading assignments will be articles describing behavioral experiments and/or
computational models that will serve as the basis of hands"on lab assignments. In addition
there will be assignments from the online PDP Handbook
(http://www.stanford.edu/group/pdplab/pdphandbook/).
Software for simulations
1. Matlab: write your own simulation code “from scratch” using Matlab.
2. PDPtool (http://www.stanford.edu/group/pdplab/resources.html#pdptool): Matlab-based
toolbox designed for just this sort of class. The Stanford PDP group provides support and
development for this tool, including user’s guide etc.
3. Lens (http://www.stanford.edu/group/mbc/LENSManual/index.html): Powerful, standalone
simulation program. No longer under development, somewhat less support, but has a user’s
guide.

Schedule
Week Date Topic(s)
2 Sept. 13 Course introduction
PDP Basics:
Neural inspiration
The Perceptron
Interactive Activation & Competition
Technical
Linear algebra
Software
3 Sept. 20 Visual search
LAB
4 Sept. 27 Memory:
Content-Addressable Memory
Graded Constraint Satisfaction
LAB
5 Oct. 4 Language production
Computational cognitive neuropsychology
LAB
6 Oct. 11 No class – more time to finish language production lab report
7 Oct. 18 Basic learning
Delta rule
Generalized delta rule: Back propagation
XOR
LAB
8 Oct. 25 No class – more time to finish XOR lab report
9 Nov. 1 Semantics
LAB
10 Nov. 8 Simple recurrent networks
Grammar learning
LAB
11 Nov. 15 Word learning
LAB

Week Date Topic(s)
12 Nov. 22 Distributed vs. localist representations
Emergent vs. stipulated structure
13 Nov. 29 Unsupervised (Hebbian) learning
Final project work/discussion time
14 Dec. 6 Final project work/discussion time