A4 Refereed article in a conference publication
Visual World Paradigm Data: From Preprocessing to Nonlinear Time-Course Analysis
Authors: Porretta V, Kyröläinen AJ, van Rij J, Järvikivi J
Conference name: International Conference on Intelligent Decision Technologies
Publisher: SPRINGER-VERLAG NEW YORK, MS INGRID CUNNINGHAM, 175 FIFTH AVE, NEW YORK, NY 10010 USA
Publication year: 2018
Book title : INTELLIGENT DECISION TECHNOLOGIES 2017, KES-IDT 2017, PT II
Journal name in source: INTELLIGENT DECISION TECHNOLOGIES 2017, KES-IDT 2017, PT II
Journal acronym: SMART INNOV SYST TEC
Series title: Smart Innovation, Systems and Technologies
Volume: 73
First page : 268
Last page: 277
Number of pages: 10
ISBN: 978-3-319-59423-1
eISBN: 978-3-319-59424-8
ISSN: 2190-3018
DOI: https://doi.org/10.1007/978-3-319-59424-8_25(external)
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.
