Fall 2004 Cognitive Science 207 Introduction to Cognitive Modeling

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Fall 2004 Cognitive Science 207Introduction to
Cognitive Modeling

• Praveen Paritosh

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Overview

• Who we are
• Course mechanics
• What is cognitive modeling?
• Syllabus
• Homework Zero

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Who we are

• Praveen Paritosh
• Brian Kyckelhahn
• Kate Lockwood

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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

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Communications

• Class web site http//www.cogsci.northwestern.ed
  u/courses/cg207/
• To contact Brian, Kate or Praveen re class
  matterscogsci207-staff_at_cs.northwestern.edu
• For class discussions, we will use the discussion
  forums in Blackboardhttps//courses.northwestern.
  edu/webapps/login

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Grading

• Midterm 20
• Final exam 30
• Reading/Modeling Assignments 50

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Reading papers

• No textbook, but a collection of research papers.
  
• We want you to READ the papers.

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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.

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Classes

• Thursday
• Lecture
• Readings assigned
• Tuesday
• Critiques due before class
• Discussion based on critiques and readings
• Modeling homework assigned, due following
  Tuesday.

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Modeling Assignments

• Turned in via email to cogsci207-staff_at_cs.northwe
  stern.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.
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What is mind?

• One of the deepest questions humanity has asked
• Many fields have tried to answer it
• Philosophy
• Psychology
• Linguistics
• Biology (evolutionary, neuroscience, )

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Its probably a computation

• A key insight
• Productive, since it raises many questions
• Whats a computation?
• What kind of computation?
• Operating over what kinds of data?
• On what sort of system is it being carried out?

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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,

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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

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Can a machine think?
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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 arent going to become one
• Good start to becoming a computational modeler,
  if thats what you want to do.

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Methodology

• What does it mean to model thinking?
• Turing test and its limitations
• Chatterbots

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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

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Naïve physics

• How can we model our everyday understanding of
  the physical world?
• Qualitative representations as formalization of
  conceptual knowledge
• Vmodel software

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Natural language processing

• How can we model the understanding of language?
• Guest lecturer Chris Riesbeck

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Music Cognition

• Representations of how we understand/ interpret
  music.
• Guest Lecturer Bryan Pardo

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Analogy and similarity

• How do we reason and learn from analogies and
  metaphors?
• Gentners structure-mapping theory
• Computational simulations of it

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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

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Emotions and Consciousness

• How can we study them as scientists?
• Norman et als model of emotions in cognitive
  architecture
• McDermotts analysis of consciousness

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Homework Zero

• Due Tue, Sep 28, noon. Email to
  cogsci207-staff_at_cs.northwestern.edu as always.
• Questions
• Why are you taking this course?
• What cognitive phenomena would you most like to
  model?
• 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

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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.