Q550: Models in
Cognitive Science
Spring 2007: Tues/Thurs, 11:15-12:30
Instructor: Dr. Michael Jones
Office: PSY 357
Phone: 856-1490
Email: jonesmn@indiana.edu
Office Hours: By appointment
Class webpage: http://www.indiana.edu/~clcl/Q550_WWW/
Overview
Q550 is intended to be a "capstone" class of cognitive science that integrates skills from Q530 (programming methods) and Q560 (empirical methods) with cognitive modeling. The course is intended to be a "hands on" experience for students to actually do some modeling, and a survey of recent models and issues in the literature.
The course will be a mixture of lectures, paper discussions, and student-led presentations. Lectures will cover the major topics in the art of computational modeling (e.g., model fitting, parameter estimation, learning algorithms, etc.), discussions will cover some recent models and movements in cognitive science, and the student presentations will focus on projects of individual interest.
Pre-requisites
Students should have already taken Q530 and Q560, or have equivalent experience and knowledge. Most programming languages will be acceptable (if you have a favorite). If you have yet to learn a structured language, I recommend Python or Matlab, which you can learn quickly if you have previous computer programming experience (of course, C and Fortran are better supported for high-performance computing). You should also have some experience with collecting and analyzing data from human subjects (rats will do).
Goals
Along with a discussion of the recent literature and an understanding of core concepts of cognitive modeling, students will get experience:
Reading Materials
There is no textbook for this course. Prof. Busemeyer has graciously allowed us to use some chapters from his cognitive modeling textbook that is in progress (feedback is welcome). These chapters and other selected articles will be listed online.
You may be randomly selected to provide an overview for one or more papers for our discussions. Your job is simply to give us an overview of the paper and raise two or more questions or issues to start off our discussion. Required readings are not necessarily each week. You can find the schedule of topics and papers here.
Grading
There will be no exams or tests during this course. Evaluation will be based on in-class presentations, discussions, and a final project. The final project will count for 50% of the grade; the two presentations will each count for 20% of the grade, and participation will constitute the final 10%.
Individual Project
Project Information: http://www.indiana.edu/~clcl/Q550_WWW/Projects.htm
Each student will select a domain that is of personal interest and will propose a simple experiment and a simple model. The model should not be complex—it must be simple enough that the student can clearly explain it to the class and be confident that it is accurately programmed. 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.
There are (at least) three approaches to the individual project: 1) identify an existing model and the phenomena it explains, collect data, and simplify the model to the basic mechanisms necessary to address the core phenomenon; 2) identify two competing models of a phenomenon, code the models, and create an experiment which will allow you to constrain between the models either qualitatively or quantitatively; or 3) identify a robust phenomenon for which there is currently no explanatory model, and create/fit one.
Students will program a model and collect/analyze experimental data (using the other students and instructor as subjects). Each student will present two 15-minute talks: the first one describing the empirical phenomenon and theoretical background, and the second one proposing the simple model and experiment. At the end of the course, each student will submit source code, and two papers (one small, one larger).