CS397 - Introduction to Cognitive Science and Cognitive Modeling
Time
2:30 PM - 3:45 PM, Tuesday and Thursday
Location
Lovejoy 211
Textbooks
- Stillings, N. A., Weisler, S. E., Chase, DC. H., Feinstein, M. H.,
Garfield, J. L., & Rissland, E. L. (1995). Cognitive science: An
introduction. Cambridge, MA: MIT Press.
- Thagard, P. (1998). Mind readings: Introductory selections on
cognitive science. Cambridge, MA: MIT Press.
Course description
Cognitive science is a multi-disciplinary approach to the study of
intelligence and the mind, bringing together the fields of psychology,
computer science, philosophy, education, linguistics, anthropology,
neuroscience, and logic, among others. The science derives its strength
from a view of the mind as an information system, including processes
that implement perception, memory, reasoning, language, learning, and
consciousness. Cognitive modeling takes this view to an extreme, using
computer programs to simulate these cognitive processes. In this
course, you will read papers on a variety of aspects of cognition,
and engage in critical
discussion of different frameworks for understanding these processes.
Additionally, you will learn about empirical methods for studying the
mind, and will explore some computer systems that model
various components of human behavior and learning.
Course content
Most of the course will be in a lecture and discussion format, centered
around the course readings. Additionally, there will be a few small
projects allowing you to get hands-on experience with some real cognitive
models. Here is a rough list of the topics I hope to cover during the course
of the semester:
- Basics of cognitive science
- Building explanations of behavior
- The architecture of the mind
- Computational architectures for cognition
- Philosophical disagreements about cognitive science
- Knowledge representation
- Logic
- Rule-based representations
- Basics of linguistics
- Basics of neuroscience
- Connectionist models
- Analogical reasoning
- Problem solving and learning
- Situated models and complex tasks
- Case studies
Class discussion
Most class sessions will involve presentation and discussion of one or
more readings from one of the textbooks or other sources. Every student
will have responsibilities associated with each reading.
For some readings, pairs of students
will be assigned to lead and/or manage the discussion.
In keeping with the college-wide desire to integrate the development of
writing skills into the curriculum, and in order to keep the class discussions
at a high quality level, each student must prepare some written material for
each reading.
At the beginning of each class session, every student must bring a writeup
and list of questions pertaining to the day's reading. The writeups should
include discussion of at least the following questions:
- What are the main points of the reading?
- Are there any ways the authors could have improved the presentation
of their points?
- There are a variety of ways we might hope to create a computational
model of the mind.
What does this paper say about how that model should look?
The writeups should be written in a professional style, using grammatical
and correctly spelled English sentences. The writeups should be in essay
format (not a ``laundry list'' of sentences), and approximately
1 to 2 pages long. They should use a standard bibliography and refernce
style, when appropriate.
The list of questions should include 1 to 3 (or more)
questions the student has about
the reading, and may be presented in list format. The questions should
be used by the student during class discussion, and may be turned in at
the end of class.
For selected readings, the student discussion leaders will present a summary
and analysis of the reading (roughly along the lines of the writeups),
and will lead a discussion involving the student questions about the writeup.
Discussion leaders are encouraged to do extra work to find topics or answers
to questions related to the reading, and may use whatever presentation
materials they feel are most effective.
Other homework and small projects
I will assign occasional additional homework, usually asking questions
to compare and analyze some of the variety of concepts and methods we
will explore in class. In addition, there will be a few small projects
where you use (and sometimes extend) existing computational models,
in order to understand them better.
Semester project
Each student will also complete a medium-sized semester project. The
specifics of the project depend on your tastes, knowledge, and interest.
The project should explore (in more detail than we do in class) some specific
aspect of cognition or cognitive science. This nature of your exploration
can be philosophical, psychological, computational, or some combination of
those or other approaches. The result of your project will be a
report of at least a few pages in length (well written and complete with
references and bibliography). You may also include data from a small
experiment you have done, or perhaps a running computer program you have
built. There are a wide variety of options, but you must clear your
project with me. I encourage you to meet with me rather soon to discuss
project possibilities. Group projects (involving 2 or 3 people)
are encouraged.
Examinations
There will be a mid-semester examination during one class session, and
a take-home final examination that you will receive on the last day of
class and will turn in at the end of the scheduled final examination
period for this class (final exam code 16).
Grading
I will compute final grades for the course using the following proportions:
- 10%: Class participation, including attendance at lectures and
participation in discussion
- 10%: Discussion leadership for assigned readings
- 30%: Small projects, writing assignments, homework, etc.
- 20%: Semester project
- 10%: Midterm examination
- 20%: Final examination
In my grading policy, I generally consider that if you do everything I ask you
to do (and do it well),
that is worth about 90% of the total points, which would be
somewhere around the A- or low A range. Earning a high A requires extra
effort or especially creative approaches to the assignments.
Late assignments
It is very inconvenient for me to accept work past the deadline, because
it complicates my grading. Late assignments will receive a penalty of
10% of the total points for each day late (so you will receive 0 for
anything more than 9 days late).
Absences
Because we only meet twice a week, and because discussion is an important
part of this class, it is very important not to miss any class sessions.
College policy allows each student two ``unexcused'' absences without
affecting the students grade. If you have more than two unexcused absences,
it will mean you have missed a substantial percentage of the class, and it
will significantly affect your grade.
Working together
Some of the work you turn in will be individual assignments,
meaning the final work you turn in must be substantially your own. Some
of the work you turn in will be
group assignments, meaning the final
work you turn in must contain substantial contributions from each group
member.
In either event, I encourage you to use outside sources, papers, fellow
class members, and others
to help you complete your assignments. Relying on other
people's help does not consititute cheating. However, presenting the
results of other people's efforts as your own does constitue
cheating. Thus, you must credit the contributions of other sources
in the presentation of your work. The best way to do that is through a
standard reference style. For this course I strongly recommend the APA
reference style, because that is what is overwhelmingly used in cognitive
science research presentations.
If you have questions about using and citing
references, please ask me and/or look at examples in the readings for this
class.
Randolph M. Jones
(rjones@colby.edu)
