In this course we introduce a different modeling approach in psychology. Such an approach is mainly concerned with uncovering the structure within the black box; it aims to provide detailed and substantive accounts of specific mental processes. We will show students how to write down likelihoods of various models and how to use computational techniques to maximize likelihood. We will mention issues of model selection based on nested likelihood and others. In the second part of the course, we will illustrate the use of models with examples from psychophysics and other fields. Topics include signal detection theory, threshold models, multinomial processing tree models, reinforcement learning models, etc.

We will use R, a free software environment for statistical computing and graphics that can be downloaded from the web page http://www.r-project.org/, for some of the homework problems.