Fall 2006
Minds, Brains, and
Computers
Cognitive Science 050.109
MW 2:00 - 3:30 Krieger 304
|
Prof.
Paul Smolensky
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OH: Th 11:00 – 12:00
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Krieger 241B
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smolensky@cogsci.jhu.edu
|
|
T.A.s
Manny Vindiola
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OH: W 1:00 – 2:00
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Krieger 234B
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vindiola@cogsci.jhu.edu
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|
Adam
Wayment
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OH: M 3:30 – 4:45
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Krieger 245
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wayment@cogsci.jhu.edu
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Course goal To
understand how modern cognitive science studies the mind as a kind of abstract computer,
and the brain as a kind of physical computer.
Prerequisites The
material is somewhat philosophical/abstract/formal/mathematical. Calculus is
recommended, more for general mathematical experience than for specific
content. (Actual use of calculus will not be required in problem sets or exams,
except possibly for extra credit.)
Be
advised: the class is 100-level because it has no prerequisites, not
because it is easy.
No
knowledge of computer programming is required; only abstract — non-physical —
computers will be employed.
Requirements 20% Four required reading summaries
20% Two problem sets
40% Two take-home mid-term exams
5% Class participation (Attendance
will be taken and is required! Slides will not be posted.)
15% A
final paper [or 3 elective reading summaries]
(Percentages
may be revised slightly.)
Note No
incompletes will be given. Any requests for deviations from course requirements
or due dates must be based on serious, unavoidable problems (e.g., illness)
that are documented in writing by academic advising.
The
course carries N, S, Q, credit (biology; philosophy/psychology/linguistics;
mathematics/computer
science). The course does not carry W credit.
Reading
write-ups Many classes are marked with an
asterisk* on the course schedule below. On four of these days, a write-up on
the reading must be turned in at the beginning of class (these four must
include one write-up for each of the two tracks a student selects; see below).
Each write-up addresses the reading indicated in that row of the schedule.
In a write-up, it
is expected that you will
1.
Explain two or three of the most
important concepts or points of the reading. You should not attempt to cover
the entire reading, or even the majority of the reading; allow yourself the
possibility of getting into each point to a little depth. You might want to
address questions such as: What are the primary questions the reading is trying
to answer? What answers are proposed? What arguments or evidence support the
proposed answers? It’s important to be as precise as you can.
2.
Especially in the second half of
the course (but also in the first half if you can): identify points in the
reading which seem particularly important in relating the computation
architecture of the brain to the computational architecture of the mind. Do the
mind and brain perspectives seem compatible or contradictory?
Answers are
expected to be about 2–3 typed double-spaced pages of prose. Alternatively,
students familiar with argument diagrams may choose that format
(normally, one page per assignment).
Reading summaries
will not be corrected in detail, but will simply be marked T (4 points), T- (2
points), or T--
(1 point). Students can receive more detailed feedback on their written work at
any time by taking copies of their assignments to discuss with the professor or
TA during office hours. Students are advised to keep a copy of all their
assignments (including problem sets).
Problem sets Two problem sets are required; these
address the technical aspects of the course material and are essential
preparation for the two mid-term exams.
Due dates These
are serious. For reading summaries, for each day the assignment is late, a deduction
in credit of 1 point (out of 4) will be assessed; for problem sets, the
deduction is 4 points (out of 10) for each day late. For exams, due dates/times
are inviolable; exams not received by the due date/time will receive no credit.
Exams There are two
mid-term exams; each covers the lectures and reading material assigned since
the previous exam.
Exams will focus
on material discussed in class, but may also relate to any material contained
in the readings.
Exams will be
take-home, open-book, open-notes; they will require original thinking and
problem-solving. Exams will be scrutinized, and suspected cases of cheating
will be referred to academic advising for evaluation.
Out-of-class problem-solving and exam
review sessions will be held.
There is no final
exam.
Tracks Each student will elect two of the following
tracks to pursue:
Ⓐ AI (Artificial
Intelligence)
Ⓛ Linguistics
Ⓜ Math
Ⓝ Neuroscience
Ⓟ Psychology
If a particular track is
selected by a student, then the student must (i) attend the special lecture for that track; (ii) do a
special advanced reading for that track and turn in a reading summary, and
(iii) answer the track’s special exam question (on the first exam following the
track’s special lecture). A student must have committed to two tracks by the
end of the semester, but may pursue multiple ones until then. (Any work
associated with additional tracks beyond the two declared by the student at the
end of the semester will count for some amount of extra credit.) Note: For the
Math track, an extra problem on Problem Set 2 will take the place of a reading
summary — there will be no extra required reading for this track; the special
lecture will contain the additional material.
Final
paper The paper should
explore issues from the course that bear on the student’s two selected tracks;
ideally, the paper should relate those two to each other. Students should
relate any new material they read to material covered in class. Additional
reading material from the texts that relate to the various tracks will be
pointed out in lecture; students are also invited to speak to the professor for
suggested readings relating to a student’s particular interests.
Papers are
expected to run about 8–10 pages (typed, double-spaced, 11-pt font, 1"
margins).
Wuss option: Three extra weekly
assignments may substitute for a paper — these
must be turned in on the relevant due date, not at the end of the semester.
Readings Most
are from the textbooks in the bookstore. All are available (during regular business
hours) for 1-hour borrowing from Krieger 230.
References
Anderson, John
R. 1982. Acquisition of cognitive skill. Psychological Review 89: 369−403.
[see hyperlinks in schedule]
Bechtel,
William, and Adele Abrahamsen. 2002. Connectionism and the mind: Parallel
processing, dynamics, and evolution in networks. Blackwell.
Churchland,
Patricia S. & Terrence J. Sejnowski. 1992. The computational brain.
MIT Press.
Available to the Johns Hopkins
community at:
http://cognet.mit.edu/library/books/view?isbn=0262531208.
Frank, Robert.
1998. Structural analysis. In A companion to cognitive science, eds.
William Bechtel and George Graham, 450−462. Blackwell. [see hyperlink in
schedule]
Haugeland,
John. 1985. Artificial Intelligence: The very idea. MIT Press.
Prince, Alan
& Paul Smolensky. 1997. Optimality: From neural networks to universal
grammar. Science 275: 1604–1610. [reprints provided; also, see hyperlink
in schedule]
Smolensky,
Paul, and Géraldine Legendre. 2006. The Harmonic Mind: From Neural
Computation to Optimality-Theoretic Grammar. Vol. 1. Cognitive
architecture. Vol. 2. Linguistic and philosophical implications. MIT
Press. [see hyperlink in schedule]
Student Questionnaire
Name:
E-mail
address (please print carefully):
Year
(freshman, etc.):
Major
(or interest area, if known):
Current
status in this course (enrolled, etc.):
Estimated
probability of taking the course:
How
did you hear about this course, and what attracted you here?
Have
you taken Cognition, 050.101?
________. Other Cognitive Science Department courses you’ve taken:
For
each of the following general areas, indicate the background you may already
have, what you may be hoping to get from this course in that area, and what, if
any, ultimate goals you may have in that area (you need not repeat information
above). In the box on the left indicate your level of familiarity with the
area, on the following scale:
1 = no familiarity; 2 = little familiarity; 3 = basic coursework; 4 = advanced coursework
Familiarity
level
philosophy:
psychology:
neuroscience:
computer
science:
mathematics:
other
cognitive science:
other
background (e.g., coursework, research) you may wish to mention:
Likely
candidates for your tracks:
Any
comments, questions, requests, etc.: (use reverse side as needed)
ETHICS SYLLABUS INSERT
Cheating is wrong. Cheating hurts our community by undermining
academic integrity, creating mistrust, and fostering unfair competition. The university will punish cheaters with
failure on an assignment, failure in a course, permanent transcript notation,
suspension, and/or expulsion. Offenses
may be reported to medical, law or other professional or graduate schools when
a cheater applies.
Violations can include cheating on exams,
plagiarism, reuse of assignments without permission, improper use of the
Internet and electronic devices, unauthorized collaboration, alteration of
graded assignments, forgery and falsification, lying, facilitating academic
dishonesty, and unfair competition.
Ignorance of these rules is not an excuse.
In this course, you are encouraged to discuss
the readings and problem sets with other students, but you must always: (1)
independently write up the work to be handed in, and (2)indicate on your
write-up who you have worked or consulted with. Often students working together
make a very unusual error and their write-ups all display the same error,
making it obvious they had worked together. This is a problem only if the
students did not indicate who they worked with, or if the work was clearly not
written up independently. If you have questions about this policy, please ask
the professor.
On take-home exams, you may not consult with
any person except the professors and the TAs. Violation of this rule (often
revealed by common, unusual errors) is a serious, prosecutable offense within
the university.
Written work must be turned in as hard copy;
electronic submissions will not be accepted as they have led to many episodes
of cheating.