Psy/Orf PsyOrf322S02Syllabus
322 : Human - Machine Interaction ~ Spring 2004
Lectures
Lectures: 1:30 2:50 Mon. & Wed. Robertson 016, (WWS)
Class Schedule
Overview and Course Organization
Week 1
Mon 2/2 Broad Overview of the Course by Course Participants. Presentation of the
framework of human-machine interaction in a problem solving environment.
Segment 1: Engineering Description of the Mind - Professor A.L. Kornhauser
Wed 2/4 Models of Human Information Processing -- A. Kornhauser Skill-rule-and-
knowledge-base approaches, semiotic interpretation of human acts, mental models
of aggregation, abstraction and analogy.
Readings: Card, Moran & Newell, The Psychology of Human-Computer Interactions,
1983, Ch1,2. Class Notes
Homework #1 Due Wed. Feb 11, 2004 Feedback on Homework #1
Segment 2: The Mind as a Machine -- Professor G.H. Harman
Week 2
Mon 2/9. The Mind-Body Problem: Dualism. Descartes' argument for two distinct
substances, body and mind. Various forms of dualism---substance, events,
properties, phenomena. Possible relations between two distinct realms: dualistic
interaction, epiphenomenalism. Rejections of dualism: idealism, physicalism.
Readings: Rene Descartes, Meditations on First Philosophy (II and VI) and
excerpt from Passions of the Soul. Class Notes
Homework #2 Due Wed. Feb 18, 2004 Feedback on Homework #2
Wed 2/11. Mind as a Computer Program. Mind not a substance but a certain functional organization of matter. Computers as thinking machines. Computers as aids in thinking.Readings: Eric B. Baum, Chapter 1, Introduction from What is Thought, MIT
Press, 2004 Class Notes
Segment 3: Human and Machine Thinking -- Professor P. N. Johnson-Laird
Week 3
Mon 2/16. Deductions by Machines - P. N. Johnson-Laird. How do we get machines
to think? One answer: get them to think logically. Formal logic can be
implemented in various computer programs. Another answer: get machines to use
rules with specific contents. Expert systems. The problems of these approaches:
intractability, and lack of decision procedure, and need to make inferences that
undo previous conclusions.
Readings: Sections 6.2 to 6.4 of Ch 6. Agents that Reason Logically, in S. J.
Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Englewood
Cliffs, NJ: Prentice-Hall, 1995, pp. 153-174.[N.B. The more recent edition of
this book is not so good, alas.] Class Notes
Homework #3 Due Wed. Feb 25, 2004
Wed 2/18. Deductions by Humans - P. Johnson-Laird. Are human beings rational? Do
they make deductions in the same way as machines, i.e. by deriving conclusions
using rules of inference? Demonstrations of typical patterns of performance in
deductive reasoning, including illusory inferences that everyone gets wrong. How
human reasoning is semantic rather than a syntactic process; it appears to
depend on constructing mental models of situations.
Readings: Johnson-Laird, P.N. (2003) Mental models and reasoning. In Leighton,
J.P., and Sternberg, R.J. (Eds.) The Nature of Reasoning. Cambridge: Cambridge
University Press. Pp. 169-204. Class Notes
Feedback on Homework #3
Week 4
Mon 2/23. Probabilistic Thinking by Humans and Machines - P. Johnson-Laird.
Representing uncertainty: the advantages of the probability calculus.
Extensional vs. nonextensional reasoning about probabilities. Common errors in
human reasoning about probabilities. Bayess theorem in expert systems, and in
human thinking. A theory of naive probabilistic reasoning.
Readings: Sections 14.2 to 14.6 of Ch 14. Uncertainty, on S. J. Russell and P.
Norvig, Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ:
Prentice-Hall, 1995, pp. 420-433. Class Notes
Wed 2/25. Creativity in Humans and Machines - P. Johnson-Laird. Can machines be
creative? A working definition of creativity. A taxonomy of creative processes:
three computational architectures. Non-determinism. Some algorithms for
creativity in science and art.
Readings: Ch.'s 13-15, and Appendices 1 and 2 of P. McCorduck, Aaron's Code:
Meta-Art, Artificial Intelligence, and the work of Harold Cohen. New York:
Freeman,1991. Pp. 85-110; 199-208 Class Notes
Segment 4: Machine Learning - Professor G.H. Harman
Week 5
Mon 3/1. Basic Principles of Statistical Learning. Pattern recognition, function estimation, probability, noise, criteria. Balance error against complexity, parameters, capacity of a set of functions, VC dimension, shattering.Readings: Vladimir Vapnik, "Introduction: The Problem of Induction and
Statistical Inference," from Vapnik, Statistical Learning theory (1998), pp.
1-15.Class Notes (cover week 5 & 6)
Wed 3/3. Methods of Machine Learning. Nearest neighbor: curse of dimensionality. Perceptron learning, linear separations, multi-layer nets, problem of local minima. Support vector machines, transduction. Giving up classical philosophy of science.Readings: Vladimir Vapnik, "Conclusion: What Is Important in Learning Theory,"
Chapter 9 of Vapnik, The Nature of Statistical Learning Theory (Springer, 2000),
pp, 291-299.Class Notes
Week 6
Mon 3/8. Integration of the First Half of the course A. L. Kornhauser, P.
Johnson-Laird & G. H. Harman
Readings: Review of the Readings, Lectures and Class Notes
Midterm Examination
Wed 3/10. MID-TERM HOURLY EXAM (covers everything through Monday 3/8, Segments
1-3)
Last Year's Mid-term
Spring04 Midterm grade distribution
Mid Term Break
Segment 6: Individual Differences in Human Machine Interactions -- Professor J.
Cooper
Week 7
Mon 3/22. Computers in the Social Environment - J. Cooper. Principles of social
interaction, e.g., social comparison, social influence. The computer as a
participant in the social system.
Readings:
Lepper & Malone, "Making Learning Fun: A Taxonomy of Intrinsic
Motivation for learning," in Aptitude Learning and Instruction, edited by Snow
and Farr, 1987, Vol. III, Ch. 10, p 223-253 and
Chapters 1 & 2 Cooper, J. and K. Weaver Gender and Computers:
Understanding the Digital Divide, Lawrence Erlbaum Associates (2003) Class
Notes (cover week 7 & 8)
Wed 3/24. Motivational Issues in Computer Education for Children -- J. Cooper.
Achievements in learning from computers. Intrinsic motivation: wanting to learn
more in computer education.
Readings: Chapters 3 & 4 Cooper, J. and K. Weaver Gender and Computers:
Understanding the Digital Divide, Lawrence Erlbaum Associates (2003)
Homework #4 (Cooper) Due Monday April 5, 2004
Week 8
Mon 3/29. Gender and the Computer - J. Cooper. Understanding anxiety and
motivation as a function of gender. How do males and females differ in their
approach to avoidance of computers? Are gender differences a function of
software, of hardware? To what extent are gender differences in computing a
function of social content?
Readings Chapters 5 Cooper, J. and K. Weaver Gender and Computers: Understanding
the Digital Divide, Lawrence Erlbaum Associates (2003)
Wed 3/31. Personality differences in Computing J. Cooper Study of research
findings on personality differences in computing.
Readings: 6 Chapters & 7 Cooper, J. and K. Weaver Gender and Computers:
Understanding the Digital Divide, Lawrence Erlbaum Associates (2003)
Segment 7: Learning and Doing with Machine - Professors A.L. Kornhauser
Week 9
Mon 4/5 Views of Viewing: The Anatomy of Vision and the Modeling of Visual
Cognition -- A. Kornhauser. A look at the human vision system; its anatomy, its
operation and the modeling of the system. Overview of the human anatomy of the
retina and the visual pathways and comparison with the vision system of the
Frog. Focus on the information processing structure of the retina and the
visual cortex. Computational models of low-level and high-level vision.
Approaches to the modeling of the human vision system and visual cognition.
Computational models of low-level and high-level vision.
Readings: Lettvin, J.Y., et al what the Frogs Eye Tells the Frogs Brain,
Proc. Of the IRE,Nov. 1959 pp 1940-1951
from J.H. Schwartz, Principles of Neural Science,
Ch 27, "The Retina and Phototransduction,"
Ch 28, "Anatomy of the Central Visual Pathways." Class Notes
PEAR lab Session times - R.G. Jahn & B. Dunne
Wed 4/7 Models of Visual Cognition -- A. Kornhauser.. Computational models of
low-level and high-level vision. Approaches to the modeling of the human vision
system and visual cognition. Computational models of low-level and high-level
vision.
Readings: From D. L. Osherson, Visual Cognition and Action, Vol 2, Ch 1,
"Computational Theories of Low-Level Vision," Ch 2, "High-Level Vision," Ch 3
"Mental Imagery". Class Notes
Week 10
Homework #5 (Kornhauser) Due Tues. April 13, 2004 (see Readings for Wed 4/14)
Mon 4/12 Learning with Machines and Artificial Neural Networks A. Kornhauser.
Foundations of artificial neural systems. Comparison of real ans\d artificial
neural systems. The evolution of highly parallel distributed processing models
known as neural networks. Presentation of various mathematical frameworks,
different approaches to learning; choices of training sets. Specific examples
using back-propagation networks.
Readings: Simpson, Artificial Neural Systems, Ch 1-1. Kornhauser Neural
Network Approaches for Lateral Control of Autonomous Highway Vehicles. Proc. Of
VNIS Conf., Dearborne, Mich. Oct. 1991, p 1143-1152 Class Notes
Wed 4/14 Helping Humans Make Better Everyday Decisions -- A. Kornhauser. With
vast amounts of real-time information available, what kind of machines will help
the individual make better decisions? What are the communication, computing and
interface requirements? How will the supporting information be gathered and
distributed. What about quality? A pragmatic example: getting from A to B, how
to navigate, guide and control. Class Notes
Readings: To be selected by you as assignment#5 in preparation for this class.
Please email to [e-mail] the author/title/source (citation) and a
100 word summary focusing on why/how this reading is pertinent to "Helping
humans make better everyday decisions" by midnight Tuesday April 13. Last Year's
student selected readings. Turn in a copy of the reading after class on Monday,
April 14 (Limited to 15 pages). The reading MUST be about EVERYDAY decisions
(decisions worth only a few dollars) NOT monumental decisions such as how
do I deal with cancer, or should I buy IBM stock!!! Class Notes
Final Project Descriptions Due Monday 4/19
Spring 03 Final Project Proposals
Spring 04Final Project Proposals
The project combines a term paper with a brief visual presentation. Your plan
for the project should be discussed by one or two of the faculty in this course
well in advance of Monday 4/19.
Segment 8: Consciousness and Human-Machine Interactions -- Professor R.G. Jahn
Week 11
Mon 4/19. Princeton Engineering Anomalies Research. Purpose, history, style,
agenda. Human/Machine Experiments I:
Benchmark Random Event Generators; Technical and Procedural Variants
Results and Interpretations; Implications and Applications
Readings Jahn and Dunne, Margins of Reality, Section II; Two Decades of PEAR: An
Anthology of Selected Publications, Articles #14 ("Correlations of Random Binary
Sequences with Pre-Stated Operator Intention"), #6 ("...