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

Vince Porretta, Aki-Juhani Kyröläinen, Jacolien van Rij, Juhani Järvikivi

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Citations (Scopus)

Abstract

Abstract. 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 advan‐ tages in addressing the issues inherent to other methods, allowing researchers to more fully understand and interpret VWP data.
Original languageEnglish
Title of host publicationIntelligent Decision Technologies 2017
EditorsIreneusz Czarnowski, Robert J. Howlett, Lakhmi C. Jain
Place of PublicationCham, Switzerland
PublisherSpringer International Publishing AG
Pages268-277
ISBN (Electronic)978-3-319-59424-8
ISBN (Print)978-3-319-59423-1
DOIs
Publication statusPublished - 2017

Publication series

NameSmart Innovation, Systems and Technologies
PublisherSpringer International Publishing AG
Volume73
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Cite this