Aki-Juhani Kyröläinen and Vincent Porretta (McMaster University)

This workshop provides a conceptual and hands-on introduction to Generalized Additive Mixed Modeling (GAMM) as implemented in the R package mgcv. GAMM is a nonlinear regression method, which can be used to analyze nonlinear data (e.g., gaze data from reading, VWP, and Pupillometry) and is particularly suited to time course data. Over two days, the following topics will be addressed: 1) conceptual introduction to GAMMs with random effects; 2) basic implementation of GAMMs in R and visualization of effects; 3) Model fitting, model comparison, and interpretation of the model; and 4) Gaussian vs. Binomial models and discussion of autocorrelated residuals in time-series data. The workshop combines short lectures with practical hands-on sessions, so participants are advised to bring a laptop with R installed (or share a laptop with someone else during the practical sessions). While the required R code for the course will be provided, experience with R and linear modeling is definitely an advantage (though not strictly required).