Term Project Proposals
85-711/411
Autumn, 1999

	A major part of what you will learn in this course comes from carrying out your term project.  The project is a piece of research, albeit a rather small piece.  In previous years, term projects have often led, after further work, to a journal article or a conference paper for the student, and in a number of cases, eventually to a thesis.  However, regardless of how far you ultimately carry your project, a report on it is due on the last day of class in this course, and you will receive an incomplete in the course if your project report is not handed in on that day.  An incomplete at CMU turns automatically into a falling grade (an R) unless the work is completed before the end of the Spring semester, 2000.  Students sometimes ask for an extension of time with the intention of finishing the project over the Christmas holidays, but this is usually a poor idea, as they discover when the project is still not finished as the end of the Spring term approaches.  Plan to turn yours in at the scheduled time.

	A major factor in the success of a research project is the choice of topic.  Your project proposal will allow me to review the appropriateness and doability of the one you have proposed, and to discuss with you possible modifications in it.  A good project is based (1) on a question whose answer you and others will care about, and (2) one or more weapons (research methods) for approaching it.  If you are interested in a big question (e.g., the nature of creativity) divide it into small parts, and pick one of them, as definite and concrete as possible, for your project.

	The proposal should state clearly (1) the objective  of the research (i.e., what question will it answer?), (2) the methods you will employ, and (3) the scientific relevance of the research.  This is a standard format for research proposals, one that you will often use should you decide to do research professionally.  The proposal document can be fairly short: two pages is usually enough.  On review of the proposal, I will tell you that it is acceptable as it stands or make suggestions for improving it.  In the latter case, you must submit a revised version.

	Any topic that bears on the material in the course is potentially acceptable.  Most projects in the past have either involved protocol analysis, computer simulation or both.  It is a good idea to choose a project that will develop those skills where you are weakest.  If you are already a good programmer, then choose an experimental project rather than a computer simulation.  On the other hand, if you've never done a computer simulation, this is a fine opportunity to learn how.  Although it may seem easier to do a project that uses the skills you already have, that will short-change you as far as learning and broadening your skills is concerned (and isn't that why you are here?).

	In the past, students have often selected tasks from the field in which they were majoring or specializing.  For instance, a graduate student in physical education did a fine (now published) study of differences between expert and novice coaches of shot-putting.  In general, this is a fine idea.  The projects should be meaningful to you and not just make-work.

	In order to help you select a topic, some possible problems that we have dreamed up are listed below.  Their main purpose is not to propose a specific topic, but to help jog your thinking about one that makes sense for you.  If one of these arouses your curiosity, feel free to modify it, apply it to a different domain, or otherwise fit it to your learning goals and interests.  On the other hand, you may already have in mind a problem that is unrelated to any of those listed, but relevant to the course; in that case, you are free to choose it.  Note that some of the problems listed here refer to topics we haven't yet discussed in the course.  Other projects require some familiarity with design and statistical analysis of standard kinds of experiments.  Brad Best and I will be glad to assist you in all of these matters.

	After settling on a tentative topic, check the psychological literature to see what is known about it.  If you have no experience in literature searches,  Brad Best and I can help you.  Or raise the question of searching in class, and we will discuss it.

	If the project you choose includes an experiment, it will usually be best if you plan to carry out only some preliminary runs during the semester, and not a complete experiment with a full complement of subjects.  In most science, the important experiments are exploratory — looking for new and unexpected phenomena, and often motivated by somewhat vague but significant objectives, not aimed at providing strict tests for fully formulated theories.  Such experiments don't look at all like the final finished projects (complete with t-tests and ANOVAS) that you see published in the journals.

	The great physicist, Michael Faraday, discovered that magnets could induce electric currents.  His experiment was motivated only by the idea that, since Ørsted had shown that currents induced magnetic forces, the reverse was probably also true!  I could cite many similar historical examples.   So run an exploratory experiment, but as carefully thought through and as carefully analysed as you can make it.

	You will learn more from doing a thorough analysis of a couple of good protocols than from a superficial analysis of five or ten.  In any event, in my experience, a large part of what is learned in experiments is learned from the unexpected phenomena that appear in the trial runs if you look hard at your data — even though you have to carry out more extensive and formal work before the findings can be published.

A.  Memory

1.  Capacity of visual memory.  A widely accepted hypothesis about short-term acoustic memory is that subjects retain the information by rehearsing it (silently or aloud).   Capacity is explained by the length of the internal loop as well as the time required to rehearse.  It is not known whether visual STM can be explained in the same way.   This might be tested by using as stimuli diagrams (line drawings) of varying degrees of complexity and composed of chunks of various sizes and various degrees of familiarity to different subjects, to see how much and what kinds of information can be retained, after what length of exposure to the stimuli.

2.  Organization of a memory to hold information about specific events and information about general concepts.  People are able to retain information about specific events in their lives (episodic memory), and also about general concepts and information relevant to them (semantic memory).  Design a program that will accommodate both kinds of memory in a way that is consistent with experimental and observational evidence.  In particular, are there ways of accommodating both kinds of memory in an EPAM-like structure? 

3.  Differences among professions in ways of representing and solving problems.  It has been hypothesized that people trained in different professions use different problem-solving styles in tackling the same problem.  Ask sets of subjects from different professions (e.g., law, engineering, medicine,  economics, physics) to construct a set of public policy recommendations on a topic like disposal of nuclear wastes, unemployment or public education.  There seem to be quite striking differences in the ways these professions reason about risk taking and assignment of liabilities for social costs of various kinds.  Take thinking-aloud protocols while they are planning and writing their answers to problems.  Analyze the protocols for differences in the aspects of the situation they take into consideration and in their styles of reasoning.  The latter — styles of reasoning, is an especially promising area. (See also other topics on problem representations, below.)

4.  Memory storage of information about "how things work."  Instruct subjects to plan a set of annotated diagrams and drawings that will explain to a layman how a telephone (an automobile, a can-opener, a radio, canal locks) operates.  From protocols infer the extent of subject's information about the device, and how that information is organized in memory  

B. Methodology of Thinking Aloud Procedures

1.  Effects of thinking aloud on problem solving.  Some studies have shown that think-aloud instructions may improve, rather than hurt, problem-solving performance, and, in particular facilitate transfer of skill from the 4-disk Tower of Hanoi problem to 5-disk and 6-disk problems (Gagné & Smith, 1962; Wilder & Harvey, 1971).  These studies have generally measured only amount of talking, and have not done thorough analyses of the protocols.  One hypothesis is that transfer will be assisted only for subjects who try to reflect on their moves to discover why they do or don't work (Anzai & Simon, 1979).  Test this hypothesis, using protocol analysis.

C. Effective and efficient learning

1.  Teaching and learning problem-solving strategies.  Subjects who learn to solve the Chinese ring puzzle, or any of its many isomorphs, are seldom able, even afterwards, to give a coherent account of just how they go about solving it — what their strategies are.  The effects of teaching them specific strategies explicitly have not been studied.  Devise three or four different strategies that will solve the puzzle reliably and test how long it takes to train subjects to use the strategies effectively.  Explain the differences in trainability that are revealed.  [A similar project could be undertaken for many tasks besides the Chinese ring puzzle.  What other tasks — perhaps a little more complicated than tying shoelaces — can you think of that people perform without being able to describe what they do and why?]

2.  Structure of text and ease of memorization.  On the basis of the memory theories we have studied, we would predict that the speed with which people can memorize poetry would depend upon the presence or absence of rhyming and regular rhythms, the complexity of grammatical structures, and perhaps other factors that would determine chunk size and redundancy.  Formulate a set of more specific (and quantitative) predictions, and test them experimentally.  [This need not be done with poetry; how about memorizing cooking recipes; or directions for getting to a restaurant on Mt. Washington?]

3.   Textbook presentation for effective learning.  In a textbook for one of the courses you are taking, select a section of a chapter that deals with a specific learning goal.  After analysing the structure of the task, and what learning it requires, write an alternative version of that section which you think will be more effective in helping students reach the learning goal, and test your product by taking and analysing think-aloud protocols of students using each of the versions of the text.

D. Problem representation

1.  Effectiveness of alternative problem representations.  Pick a problem in a subject familiar to you (e.g., electric circuits, Gothic Revival architecture, football strategy, . . .) and design two quite different computer displays for presenting the problem to subjects who are asked to solve it.  The different presentations might use different kinds of diagrams, or one with and one without diagrams, or diagrams versus equations, or moving versus static computer displays.  Use protocols to determine and explain the effects of these differences in presentation upon behavior in understanding and solving the problems.

2.  Processes for understanding problems prior to solving them.  Describe and program an improved version of the UNDERSTAND program, a theory constructed by Hayes & Simon to show how a human problem solver can construct an internal representation of a problem that is presented verbally, and how the internal representation varies with changes in the precise language of presentation.

3.  How are effective problem representations discovered?  Some tasks are easy if formulated and represented in the right way, difficult if they are not represented appropriately.  (The mutilated checkerboard is a classical example.)  Select such a task and (1) take protocols of subjects who arrive at a good representation (with or without hints), then (2) write a program that is capable of detecting the need for a change in representation, gathering information for designing it, and constructing it.  (See Kaplan & Simon for the mutilated checkerboard example.)  Chemistry, with its different notations for representing molecules and reactions might be a suitable domain.  Examples of good problems might be found in textbooks on object-oriented programming, which generally emphasize selection of appropriate problem representations, even if they don't yet have much of a theory about how to do it.

4.  How students find mathematical representations for problems that are presented verbally.  Replicate the findings of Paige and Simon (Simon I:4.4) using "impossible" problems (problems that violate real-world conditions) in physics or chemistry instead of algebra.

5.  Selecting the objects and representations in object-oriented programming.  Research by  Kim, Leech and Simon has shown that success in creating an effective program for solving a class of problems may depend critically upon the choice of objects and representation.  This was demonstrated on materials as simple as isomorphs of the Tower of Hanoi problem.  How could programmers be trained to discover and choose problem representations and objects that would facilitate OOP. (HAS has a set of problems that could be used in this project.)

E. Problem solving

1.  Relative power of different problem-solving methods.  You are probably familiar with water-jug problems: there are N jugs, each with a different (given) capacity.  You may fill or empty these jugs, or pour from one to another.  The problem is to construct a sequence of operations that will produce a specific quantity of water in one of the jugs.  (E.g., given jugs of 2, 6 and 11 gallons; measure out exactly 7 gallons).  Write three programs (production systems) embodying different methods for solving such problems.  Evaluate these alternatives for the psychological demands they make (e.g., demands on STM) and their advantages and disadvantages for transfer to more or less similar problems; and predict the circumstances under which humans will learn to use one or another of the methods.

2.  Individual differences in problem-solving strategies.  Construct a computer program that solves cryptarithmetic problems and matches closely the behavior of the subjects whose protocols are reported in Newell & Simon.  The program will need to have several variants to match the behavior of different subjects.  Construct these variants in a psychologically plausible manner that reveals the underlying similarities in strategy as well as the differences.

3.  Effect of instructions upon problem solving strategies.  How easy or hard is it to alter the strategies people use in solving a problem by varying the problem instructions?  (We can provide relevant materials to elaborate on this theme, deriving from the work of Medin and Smith on pattern classification and recognition).

4.  Describing student skills as systems of productions.  Describe (and construct, if possible) a production system that will solve the problems at the end of one of the chapters in one of your textbooks.  Analyze the text of the chapter to determine whether the information in it is appropriate in order that a student may acquire the requisite productions by studying it.  If it is deficient in any respects, show how the deficiencies can be remedied.

F. Scientific discovery

1.  Processes for discovering new scientific instruments.  Construct a computer model to simulate the process of using observed phenomena to invent new measuring instruments.  (E.g., a model that, on observing the expansion of certain materials on heating, would invent a thermometer.)  The main thing here is to specify what the system would have to know in order to do this; what observational capabilities you would have to postulate, and what goals.

2.  Processes for finding lawful patterns in pictorial data.  The data from some experiments in biology consist of microscope slides with certain kinds of objects (cells, chromosomes, bacteria) distributed over them.  Detection of patterns in these distributions can sometimes lead to the discovery of important laws.  Write a computer program capable of detecting interesting patterns in these kinds of data.  The program should use only processes that, on the basis of our psychological knowledge, we believe people would be capable of using.

3.  Representations of magnetic induction of electrical current.  Several researchers have sought to model the successive representations that Michael Faraday used to hypothesize about electromagnetic induction.  Considerable additional work needs to be done to program the representations so that their effectiveness can be tested. (HAS can supply relevant reprints.)

4.  Extension of the BACON system by addition of exponential, logarithmic, and trigonometric functions.  The BACON scientific law discovery system finds laws by looking for patterns in data sets it is given, without knowledge of any theory relating to the data.   At present it only uses rational functions in its search.  What additional examples of discovery could it handle if its stock of functions were increased, with appropriate measures taken to prevent the amount of search from exploding?  Construct such an extension of BACON and demonstrate its ability to solve some historically important scientific problems.

5.  Sources of representations used in quantum theory and quantum mechanics.  Quantum theory has gone through a whole series of changes in representation from the early formulation of Planck, in 1900, through Bohr's planetary model of the hydrogen atom, in 1912, to the wave mechanics of Shrödinger and the matrix mechanics of Heisenberg, in 1926.  How could any or all of these representations be generated from experimental evidence and/or analogies?

	A final word of advice.  Some past projects — not the most interesting ones — have consisted simply of taking one or more protocols of someone(s) doing a task and analyzing them.  Some of the analyses were rather superficial:  they did not explain how the task is performed in general, but merely re-described the activity of the subject in a formal notation.  To avoid this, either abstract the essence of the protocol and represent it as a simulation model (thus generalizing the explanation of how the task was performed), or design your experiment so that the "how" question or some other interesting question can be answered directly from the data.   One way to proceed is to take a protocol before submitting your project proposal (it involves just an hour of your time and a tape cassette), listen to it to get some impression of what interesting processes might be going on, and then design your project to investigate and tests the initial ideas you gleaned from the tape.