This course will provide a hands-on introduction to Generalized Additive Modeling (GAMs). We will introduce methods to identify the best model given the data, and demonstrate how to visualize non-linear effects and non-linear interactions. In addition, we will address several potential problems, including, but not limited to, dealing with the common problem (for time series data) of encountering autocorrelation in the residuals of a model.

This course introduces theoretical and methodological issues in the study of bilingualism. The first half of the course focuses on bilingual acquisition in early childhood. We examine how children develop two languages in families where they are exposed to dual input from birth. The issues covered include language differentiation, cross-linguistic influence and code-mixing in bilingual development. Data from the language development of children acquiring Chinese and English, as well as other language pairs will be used for illustration.

Language isn't created by a computer, or a brain in a jar. It's created by people using their bodies to interact with each other and the world around them. Since the human body is both the birthplace of language and the instrument through with language is produced and perceived, the structure and content of language are profoundly shaped by specifics of our bodies.

This course will provide an overview of recent developments in cognitive neuroscience research on language, primarily focusing on language comprehension. We will discuss research that uses cognitive neuroscience methods to ask questions about the form of linguistic representations, as well as research that investigates the actual neural implementation of these representations; specific topics will likely span sound, meaning, and syntax. Strengths and limitations of various neuroimaging methods (EEG, MEG, fMRI) for addressing particular questions will be debated.