Fall 2004 Cognitive Science 207
Introduction to
Cognitive Modeling
Praveen Paritosh
Overview
•
Who we are
•
Course mechanics
•
What is cognitive modeling?
•
Syllabus
•
Homework Zero
Who we are
•
Praveen
Paritosh
•
Brian
Kyckelhahn
•
Kate
Lockwood
Mechanics
•
Combination of lectures and discussions
•
Weekly homeworks
•
Midterm will be Thu October 21
st
, in class
•
Final exam will be Fri December 10, 12pm
-
2pm
Communications
•
Class web site =
http://www.cogsci.northwestern.edu/courses/cg207/
•
To contact Brian, Kate or Praveen re class
matters:
cogsci207
-
staff@cs.northwestern.edu
•
For class discussions, we will use the
discussion forums in Blackboard
https://courses.northwestern.edu/webapps/login
Grading
•
Midterm: 20%
•
Final exam: 30%
•
Reading/Modeling Assignments: 50%
Reading papers
•
No textbook, but a collection of research
papers.
•
We want you to READ the papers.
Critiques
•
For each paper, three one sentence long
critiques
–
of what is wrong with the paper.
•
Due at the beginning of the Tue
(Discussion) class.
•
Will be used as a basis for the discussion, so
be prepared to defend your critique!
•
Will account for a third of your grade.
Classes
•
Thursday:
–
Lecture
–
Readings assigned
•
Tuesday:
–
Critiques due before class
–
Discussion based on critiques and readings
–
Modeling homework assigned, due following Tuesday.
Modeling Assignments
•
Turned in via email to
cogsci207
-
staff@cs.northwestern.edu
–
No hardcopies or email to other addresses
–
ASCII or HTML preferred, followed by PDF or
Word. (If HTML, must be self
-
contained:
Broken links will lose you points)
•
Late homeworks will be downgraded
•
All work you turn in must be your own.
Reading assignments due beginning of discussion class
on Tuesdays. Bring hardcopy of critiques to class.
What is mind?
•
One of the deepest questions humanity has
asked
•
Many fields have tried to answer it
–
Philosophy
–
Psychology
–
Linguistics
–
Biology (evolutionary, neuroscience, …)
–
…
It’s probably a computation
•
A key insight
•
Productive, since it raises many questions
–
What’s a computation?
–
What kind of computation?
–
Operating over what kinds of data?
–
On what sort of system is it being carried out?
Artificial Intelligence
•
Goal: To understand the nature of
intelligence
–
In whatever kind of system can exhibit it,
including people
•
Early successes inspired (and inspired by)
comparison with human cognition
–
Solving problems, playing chess, parsing
sentences, seeing in simple scenes, …
Cognitive Science
•
Born out of the computational insight
–
Computation could provide a new theoretical language
for cross
-
discipline communication
•
Meeting ground for fields traditionally concerned
with studying cognition
–
Multidisciplinary
field
–
Each field has theoretical constructs to share
–
Each field has its own empirical methods for testing
ideas
–
Deeper insights come out of their interactions
Can a machine think?
What you will learn
•
A basic understanding of how computation
can be used to model phenomena in
cognitive science
–
Crucial for all cognitive scientists, since
computation is the theoretical language of the
field
–
Facilitate working with computational
modelers, if you aren’t going to become one
–
Good start to becoming a computational
modeler, if that’s what you want to do.
Methodology
•
What does it mean to model thinking?
–
Turing test and its limitations
–
Chatterbots
Knowledge representation
•
How can computers know things?
–
Overview of how reasoning systems work
–
An introduction to predicate calculus
–
A high
-
level tour of the Cyc knowledge base
•
Ontology
•
Microtheories
Naïve physics
•
How can we model our everyday
understanding of the physical world?
–
Qualitative representations as formalization of
conceptual knowledge
–
Vmodel software
Natural language processing
•
How can we model the understanding of
language?
–
Guest lecturer: Chris Riesbeck
Music Cognition
•
Representations of how we understand/
interpret music.
–
Guest Lecturer: Bryan Pardo
Analogy and similarity
•
How do we reason and learn from analogies
and metaphors?
–
Gentner’s structure
-
mapping theory
–
Computational simulations of it
Learning and education
•
How do we learn new theories and skills?
Can we use these models to teach?
–
Production
-
rule models of skill
–
CMU work on intelligent tutoring systems
Emotions and Consciousness
•
How can we study them as scientists?
–
Norman
et al
’s model of emotions in cognitive
architecture
–
McDermott’s analysis of consciousness
Homework Zero
•
Due Tue, Sep 28, noon. Email to
cogsci207
-
staff@cs.northwestern.edu
as always.
•
Questions:
1.
Why are you taking this course?
2.
What cognitive phenomena would you most like
to model?
3.
Have you had any background in programming
or computing more generally?
•
Task:
–
Post a comment to one of the Discussion Boards for
the course in Blackboard
Readings
•
Turing, A. M. "
Computing Machinery and
Intelligence
," Mind, New Series, Vol. 59, No. 236.
(Oct., 1950), pp. 433
-
460. (also available
here
).
•
Minsky, M.
"Why people think computers
can't"
.
AI Magazine
, Fall, 1982.
•
Miller, G. "
The Cognitive revolution: A historical
perspective
", Trends in Cognitive Sciences, 7(3),
March 2003.
Enter the password to open this PDF file:
File name:
-
File size:
-
Title:
-
Author:
-
Subject:
-
Keywords:
-
Creation Date:
-
Modification Date:
-
Creator:
-
PDF Producer:
-
PDF Version:
-
Page Count:
-
Preparing document for printing…
0%
Σχόλια 0
Συνδεθείτε για να κοινοποιήσετε σχόλιο