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 21st, 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 Blackboardhttps://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.