1. 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.

2. 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. Bayes’s 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.

3. 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
Spring’04 Midterm grade distribution
Mid Term Break
Segment 6: Individual Differences in Human Machine Interactions -- Professor
J. Cooper
Week 7

4. 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: Chapters 6 & 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

5. 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 Frog’s Eye Tells the Frog’s 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 ansd 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
alaink@princeton.edu 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

6. 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:
1. Benchmark Random Event Generators; Technical and Procedural Variants
2. 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 ("Experiments in Remote Human/Machine Interactions"), #8 ("Count
Population Profiles in Engineering Anomalies Experiments"), #10 ("Series Position Effects in
Random Event Generator Experiments"), and #11 ("Gender Differences in Human/Machine
Anomalies"). Class Notes
Wed 4/21. Princeton Engineering Anomalies Research (continued). Human/Machine Experiments
II: Field Applications. Information Acquisition and Remote Perception.
Readings Margins of Reality, Section III; Two Decades of PEAR: An Anthology of Selected
Publications, Article #7 ("FieldREG II: Consciousness Field Effects, Replications and
Explorations"); Information and Uncertainty in Remote Perception Research (Available on-line).
Class Notes Part A; Class Notes Part B
Prof. Jahn Homework #6 Due Wednesday, April 28, 2004
Week 12
Mon 4/26. Theoretical Modeling and Review of Science History.
Readings Margins of Reality, Sections I, IV, V; Two Decades of PEAR: An Anthology of Selected
Publications, Articles #12 ("Anomalies: Analysis and Aesthetics") and #15 ("Science of the
Subjective"). 20th and 21st Century Science: Reflections and Projections (available on-line) and The
Challenge of Consciousness (available on-line). Secondary References: Heisenberg, Physics and
Philosophy, Harper/Collins, 1962 Hoffman, The Strange Story of the Quantum, P. Smith 1963, W.
Pauli, Writings on Physics and Philosophy, Springer-Verlag, 1994; ; S.C. Florman, The Existential
Pleasures of Engineering, St. Martin, 1976; W. James, The Will to Believe and other Essays in

7. Popular Philosophy, Dover, 1956; Jahn and Dunne, Collected Thoughts on Role of Consciousness
in the Physical Representation of Reality, PEAR, 1984. Class Notes
Wed 4/28. Theoretical Modelling II: Quantum Mechanics of Consciousness; A Modular Model of
Mind/Matter Interactions, Sensors, Filters and the Source of Reality.
Readings Margins of Reality, Section IV; Two Decades of PEAR: An Anthology of Selected
Publications, Article #2 ("On the Quantum Mechanics of Consciousness, with Application to
Anomalous Phenomena"); "A Modular Model of Mind/Matter Interactions" (Available on-line).
Class Notes
Thursday evening 4/29 7:30-8:50pm Room 016 WWS SECOND-HOURLY EXAM (covers weeks
7-12)
Reading Period
Term Project Symposium
Monday, May 10, 2004 11:30am-4:00pm
Formal oral presentation of term project 6 minutes per student Use of visual aids is highly
recommended. Students are REQUIRED to attend ALL presentations in their group in order to
contribute to the evaluation of the presentations in their group!
Picnic at Kornhauser’s immediately afterwards (4pm-7pm - weather permitting!)
Directions to Kornhauser’s

8. © Princeton University Prof. Alain L. Kornhauser
E407 E-Quad, x8-4657