ORF 105 Science and Technology of
Decision Making
Spring Term 2002-03
Lectures 1:30 p.m. to 2:50 p.m.
Tuesdays and Thusdays
Instructors
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Professors: A. L. Kornhauser (ORF) E-407 EQuad, x8-4657, alaink@princeton.edu
Description and Objectives
This course studies the fundamentals of human-machine interactions from both the human
psychology and philosophy side as well as the machine engineering and design side. This multidisciplinary approach will utilize faculty and readings from psychology, philosophy, physical
sciences and engineering. Starting from a framework of the elements of human-machine
interactions, the course focuses on the following specific issues:
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What are the fundamental differences between humans and machines, specifically: how can
we use machines to study people, what are the functional elements of sensors, memory,
control and actuators in humans? Views of the brain at work.
Philosophical aspects of human-machine interactions. Turing's test for machine intelligence
and possible problems with the test. Searle's Chinese Room Argument.
Thinking by machines and humans. Deduction by machines and humans; a taxonomy of
thought. Are humans rational? Induction by machines and humans. Creativity by machines
and humans.
Computers in the social environment; motivational issues. Individual differences in humanmachine interactions. Issues of gender, age and personality.
The decisions and control by humans and machines. The structure of the human and
machine vision systems including approaches to machine vision and image processing.
Information content in images. Application of computer vision to drive an "auto" mobile,
artificial neural networks for massively parallel computing. Concepts of Decision
Engineering, helping individuals make better real-time decisions.
The role of consciousness in human-machine interactions: Are the information processing in
the human mind and that in the intelligent machines invariably linearly separable, or are
there possibilities of resonant synergism? Design and implementation of experiments on the
question, including: issues of statistical analysis, scale of effects, replicability, and
protection from artifacts. Theoretical models of the anomalous interactions, and their
implications and applications.
Course Requirements
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Readings must be done before class; written assignments are due every other week.
There will be two (2) 1 hour exams.
There will be a term project and an oral presentation of the project.
Each student is expected to participate in at least one laboratory session in the PEAR lab, to
be scheduled midway through the course. Reading preparation for this session will be
indicated, and a written laboratory report will be required. For those especially interested in
this research, the laboratory is available for more detailed projects for the term papers.
In addition, students taking this course as their first or second psychology course must do
four hours of participation in approved psychology department experiments.
Grading: A-F, no pass-fail. Grade based on exams (20% each), course project and oral
presentation (25%), homework and labs (15%) and class participation (20%).
Required Texts
1. Bernstein, David Why is there a Burger King next to every McDonalds, unpublished text
available as pdf
Class Schedule
Overview and Course Organization
Week 1
Tue 2/4 Broad Overview of the Course by Course Participants. Presentation of the framework of
human-machine interaction in a problem solving environment.
Segment 1: Doing your best
Thu 2/6 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: BernsteinCh1,2.
Segment 2: Chances Are
Week 2
Tue 2/10. The Mind-Body Problem. 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.
Thu 2/12. Problems for physicalism. Does everything reduce to or arise from physical phenomena?
Or is there some non-physical principle? Compare the relation between mechanical and electomagnetic phenomena. Reasons to think that the mental does not reduce to the physical: intelligence,
free will, creativity, quality of experience, morality, religion.
Readings: Kwame Anthony Appiah, "Mind," chapter 1 of Thinking It Through, 2003.
Segment 3: Best guess
Week 3
Tue 2/17. 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, e.g., systems based on "natural deduction", or the resolution rule. Another answer: get
machines to use rules with specific contents. Expert systems. The problems with these approaches:
intractability, lack of decision procedures, 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. 153174.
Thu 2/19. 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. (2002) "Logic and reasoning." In Ramachandran, V.S. (Ed.)
Encyclopedia of the Human Brain. San Diego, CA: Academic Press. Vol 2, pp. 703-716.
Week 4
Tue 2/24. Probabilistic Thinking by Humans and Machines - P. Johnson-Laird. Representing
uncertainty: the advantages of the probabilistic calculus. Extensional vs. nonextensional reason
about probabilities. Common errors in human reasoning about probabilities. Bayes' theorem in
expert systems and in human thinking. A theory of na•ve probabilistic reasoning. Belief networks in
artificial intelligence.
Readings: Gigerenzer and Hoffrage (1995) "How to Improve Bayesian Reasoning Without
Instruction: Frequency Formats," Psychological Review, 102, 684-704.
Thu 2/26. 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; 199208
Segment 4: Toys
Week 5
Tue 3/3.
Readings: Thu 3/5
Readings:
Week 6
Tue 3/10. Perception and Thinking by Humans and Machines - J.J. Gelfand
Readings: S. Epstein, J. Gelfand and E. Twersky-Lock, "Learning Game-Specific SpatiallyOriented Heuristics", Constraints, 3, (1998), pp. 239-253.
Midterm Examination
Thu 3/12. MID-TERM HOURLY EXAM (covers everything through Monday 3/11, Segments 13)
Mid Term Break
Segment 6: Individual Differences in Human Machine
Interactions -- Professor J. Cooper
Week 7
Tue 3/24. 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
Thu 3/26. Motivational Issues in Computer Education for Children -- J. Cooper. Achievements in
learning from computers. Intrinsic motivation: wanting to learn more in computer education.
Readings: Lepper & Malone, "Intrinsic Motivation and Instructional Effectiveness in Computerbased Education."
Week 8
Tue 3/31.
Readings J
Thu 4/2.
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Readings
Week 9
Tue 4/7
Readings
Thu 4/9
Readings:
Week 10
Tue 4/14
Readings:
Thu 4/16
Readings:
Final Project Descriptions Due Monday 4/21
Segment 8: Consciousness and Human-Machine Interactions - Professor R.G. Jahn
Week 11
Tue 4/21.
Readings
Thu 4/23.
Readings
Week 12
Tue 4/28..
Readings
Thu 4/30.
Readings
Thursday evening 5/1 SECOND-HOURLY EXAM (covers weeks 7-12)
Reading Period
Term Project Symposium
Tueday May 12, 2002. 9:00am-1:00pm TERM PROJECT SYMPOSIUM Formal 10 minute
presentation of term project. Use of visual aids is highly recommended.
Segment 5: Perception -- Professor J. Gelfand