Title: Generalized Additive Mixed Models Author: Harald Baayen Affiliation: University of Tuebingen Abstract: Generalized additive mixed models (GAMMs) are an extension of the generalized linear (mixed) model that provides the analyst with a wide range of tools to model nonlinear functional dependencies in two or more dimensions (wiggly curves, wiggly regression surfaces and hypersurfaces). In my presentation, I will first illustrate how GAMMs can be used handle between-trial autocorrelations in behavioral experiments. I will then present an example of how EEG data can be analysed with GAMMs. As a third example, I will discuss the application of GAMMs to eye-tracking data. GAMMs, which are implemented in the mgcv package for R by Simon Wood, provide a substantial and non-trivial addition to the toolkit of experimental psychology and experimental linguistics.