Courses

Computational Cognitive Modeling

In Spring 2025, Greg taught a graduate seminar in computational cognitive modeling.  Part of the collateral damage of this course is the following set of notes, including extensive examples in R as well as exercises for gaining a foothold in the admittedly rocky terrain of cognitive modeling.

https://gregcox7.github.io/cogmod/

Statistical Methods for Psychology

This is a one-semester introduction to statistics for undergraduate students in psychology.  The course is about how data are structured, different numerical and visual ways we can describe data, and how we can use data to learn and make decisions about the world at large.  Greg has been working to incorporate more computation and simulation into the course, with the aim of making more clear and direct the connections between hypotheses, data, and data summaries.  Greg uses a customized version of the OpenIntro textbook Introduction to Modern Statistics by Mine Çetinkaya-Rundel and Johanna Hardin.

Greg's customized text is available here: https://bookdown.org/gregcox7/ims_psych/

In addition, Greg has put together a set of lab activities as part of the course, which are available here: https://gregcox7.github.io/StatLabs/

The Psychology Department also hosts a collection of excellent apps and resources for learning and doing statistics: https://www.albany.edu/psychology/statistics/

Information Processing and Perception

This is a graduate course broadly divided into two parts.  The first part deals with the concept of information in cognitive psychology.  "Information" is processed whenever an organism's behavior is systematically influenced by its environment.  On route from environment to action, the organism transforms its environment into a mental representation which can be studied using multidimensional scaling.  The second part deals with the concept of processing which focuses on the dynamics by which information from the environment is transformed into a mental representation which is then transformed again into an action.  Accumulator models and empirical approaches like Systems Factorial Technology (SFT) allow us to infer aspects of processing from response times.

Check out the collection of slides from the most recent iteration of the course here.