Introduction to Cognitive Modeling

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.