Q550 Models in Cognitive Science, Prof. Kruschke

Q550 Models in Cognitive Science, Prof. John K. Kruschke
Spring 2005, Section 25490, Tu & Th 11:15-12:30, Room 111 Psych

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Instructor: Professor John K. Kruschke, e-mail: kruschke@indiana.edu, voice: 855-3192, office: 336 Psychology, hours: W 2:30-3:30 and by appointment. Web page: http://www.indiana.edu/~kruschke/

Class web page: This syllabus and all related class info are posted at http://www.indiana.edu/~jkkteach/Q550/. It will be frequently updated, so reload it every time you check it.

Course content: As of Spring 2003, Q550 is intended to be a "capstone" course that integrates Q530 (programming methods) and Q560 (empirical methods) with cognitive modeling. The course is also intended to be a "hands on" experience for students to actually do some modeling, not just read about it.

The current format and content of Q550 is very different than previous renditions of Q550. Old versions were golden:
  • Spring 2001 (and previous since 1997). A survey of models in cognitive science.
  • Spring 1997 (and previous since 1990). A course covering exclusively connectionist networks.
  • Pre-requisites: Students should have previously taken Q530 (programming methods) and Q560 (empirical methods), or have equivalent experience and knowledge. We will be using the programming language MATLAB, which you can learn quickly if you have previous computer programming experience. You should also have some experience with collecting and analyzing data from biologically animate cognitive organisms such as humans. If you don't have much experience with these programming and empirical methods, take this course at your own risk!

    Exemptions: If you already have extensive experience in conducting experiments, analyzing data, and especially fitting models, then the instructor will consider granting you an exemption from the course. That means you will have satisfied the course requirement for Cognitive Science, but you do not get credit hours. Please contact the instructor if you believe that you may qualify for an exemption.

    Goals: Along with gaining understanding of concepts of modeling, students will get experience doing four things:

    1. Designing and running a simple experiment relevant to their research interests.
    2. Analyzing the data using standard inferential statistics.
    3. Programming a simple cognitive process model for the experiment task.
    4. Fitting the model to the data.

    Readings and materials: No required textbook. Selected articles online. MatLab documentation online. See links below.

    Web bulletin board: Announcements and discussion will appear frequently on the web bulletin board in Oncourse.

    Grading: There will be various homework assignments throughout the course. Students will also make brief presentations describing their projects. A final poster and paper are also required. There will be no exams, but the final exam period will be devoted to a poster presentation session. The individual project will count for one half of the grade; the preceding homework assignments will count for one half of the grade.

    Disclaimer: Any information provided here is subject to change. Changes will be announced in class.

    Schedule of topics:

    The order of topics is listed below. The exact dates for each topic will be established en route. Check this page frequently for updates.
    1. Introduction: Examples of modeling; the meta-structure of modeling.

      Homework Assignment:
      Four exercises, on the web.
      Assigned Tue Jan 11.
      Due Tue Jan 18.

    2. Detailed example of modeling with MatLab.
      Homework Assignment:
      Anderson (1965) Homework.
      Assigned Thu Mar 3, 2005.
      Due Thu Mar 10, 2005.
    3. Detailed example of conducting an experiment and analyzing data in MatLab.

    4. Individual projects:
      • Follow this link for details and suggestions regarding individual projects.
      • Each student selects a topic of personal interest, and proposes a simple experiment and simple model for programming in MatLab. Students are encouraged to base their proposals on existing published research. The experiment must be short enough that it can be completed by a participant in 15 minutes. The model must be simple enough that the student can clearly explain it to the class and be confident that it is accurately programmed in MatLab. The model should also have a clear interpretation in terms of cognitive processes; purely "descriptive" models do not qualify. The proposal must be approved by the instructor An example of all this is the N. H. Anderson work studied in the previous section of the course.
      • Each student programs his/her experiment in MatLab, and other students are participants. Ethical treatment of participants will be emphasized.
      • Each student conducts a statistical analysis of the effects of interest in his/her data.
      • Each student programs the corresponding model, and fits the model to his/her data.
      • Periodic progress reports are submitted and presented in class, and a final report. The final exam period will be devoted to a poster session in which students display their project.

    MatLab resources:

    We will be using the computer software called "MATLAB" quite extensively, for programming models, experiments, and data analysis.

    Where to find it: MatLab and many accompanying "Toolboxes" are available on all I.U. public computers except Apple Macintoshes. If you want to install MatLab on your own machine, I.U. students can buy an annual lease through the StatMath Center, or buy a personal copy directly from MathWorks (the manufacturer of MatLab).

    This is also a free Matlab-alike program called Octave. It is supposed to match Matlab's syntax and functionality. It is not, however, as extensive as Matlab, and is not guaranteed to be fully compatible. So use Octave at your own risk. I require that all projects turned in --especially experiments that are distributed to all class members-- have been fully tested in Matlab itself.

    For programming experimients, you might consider using the Psychophysics Toolbox for MatLab by Brainard and Pelli. It is free. I have not yet used it myself.

    Learning about MatLab: