Visual World Paradigm Data: From Preprocessing to Nonlinear Time-Course Analysis

: Porretta V, Kyröläinen AJ, van Rij J, Järvikivi J

: International Conference on Intelligent Decision Technologies

Publisher: SPRINGER-VERLAG NEW YORK, MS INGRID CUNNINGHAM, 175 FIFTH AVE, NEW YORK, NY 10010 USA

: 2018

: INTELLIGENT DECISION TECHNOLOGIES 2017, KES-IDT 2017, PT II

: SMART INNOV SYST TEC

: Smart Innovation, Systems and Technologies

: 73

: 268

: 277

: 10

: 978-3-319-59423-1

: 978-3-319-59424-8

: 2190-3018

DOI: https://doi.org/10.1007/978-3-319-59424-8_25

The Visual World Paradigm (VWP) is used to study online spoken language processing and produces time-series data. The data present challenges for analysis and they require significant preprocessing and are by nature nonlinear. Here, we discuss VWPre, a new tool for data preprocessing, and generalized additive mixed modeling (GAMM), a relatively new approach for nonlinear time-series analysis (using mgcv and itsadug), which are all available in R. An example application of GAMM using preprocessed data is provided to illustrate its advantages in addressing the issues inherent to other methods, allowing researchers to more fully understand and interpret VWP data.