2017
DOI: 10.1007/978-3-319-59424-8_25
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Visual World Paradigm Data: From Preprocessing to Nonlinear Time-Course Analysis

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Cited by 42 publications
(54 citation statements)
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“…Finally, GAMMs can handle unbalanced data (i.e., missing data points), which is common in eye tracking as participants are free to fixate outside of the predetermined interest areas during a trial. GAMMs have successfully been used in past research to analyze visual world eye movements (Porretta, Kyrölaïnen, van Rij, & Järvikivi, 2017;Porretta, Tucker, & Järvikivi, 2016; van Rij, Hollebrandse, & Hendriks, 2016). Also note that GAMMs require visual inspection of model estimates to interpret (non-)significance of factors, especially since Bnonlinear trends are difficult to capture with a single parameter ( Porretta et al, 2017, p. 270).…”
Section: Analysis and Variablesmentioning
confidence: 99%
“…Finally, GAMMs can handle unbalanced data (i.e., missing data points), which is common in eye tracking as participants are free to fixate outside of the predetermined interest areas during a trial. GAMMs have successfully been used in past research to analyze visual world eye movements (Porretta, Kyrölaïnen, van Rij, & Järvikivi, 2017;Porretta, Tucker, & Järvikivi, 2016; van Rij, Hollebrandse, & Hendriks, 2016). Also note that GAMMs require visual inspection of model estimates to interpret (non-)significance of factors, especially since Bnonlinear trends are difficult to capture with a single parameter ( Porretta et al, 2017, p. 270).…”
Section: Analysis and Variablesmentioning
confidence: 99%
“…% fixations = duration of fixations to target duration of fixations to competitor + duration of fixation to target The data were statistically analyzed using generalized additive mixed models (GAMMs; Wood, 2017), which can account for nonlinear trends through time, as found in eye tracking data. GAMMs can also include (linear or nonlinear) random effects, and account for autocorrelation in the time-dependent data (i.e., one data point in time is necessarily correlated to the preceding data point, which can yield to an overconfidence of model estimates; Baayen, van Rij, de Cat & Wood, 2018;Porretta, Kyröläinen, van Rij & Järvikivi, 2018). Furthermore, GAMMs do not assume normal distribution of the data, which makes them appropriate for eye fixation data.…”
Section: Analysis Proceduresmentioning
confidence: 99%
“…Despite its advantages for fitting time-series data, GAMM has not been used to analyze eyetracking data until recently (Porretta et al, 2018b). To our knowledge, to date, there have been no direct comparisons of GCA and GAMM analysis of Visual World data.…”
Section: Generalized Additive Mixed Model (Gamm) Analysis On Target Fmentioning
confidence: 99%
“…An exploratory analysis with generalized additive mixed modeling (GAMM) on target Benefits of the GAMM approach include: (1) the ability to account for autocorrelation often present in time-series data, (2) the ability to fit complex nonlinear curves more easily and flexibly with smooth terms, where a smooth term consists of a smoothing spline (i.e., piecewise polynomial function) and a penalization method for "wiggliness" to optimize function fit, and (3) the ability to model multidimensional continuous interactions in a straightforward way (Baayen, van Rij, de Cat, & Wood, 2016;Baayen, Vasishth, Kliegl, & Bates, 2017;Porretta, Kyröläinen, van Rij, & Järvikivi, 2018b;van Rij, 2015;Wieling, 2018;Winter & Wieling, 2016). Despite its advantages for fitting time-series data, GAMM has not been used to analyze eyetracking data until recently (Porretta et al, 2018b).…”
Section: Generalized Additive Mixed Model (Gamm) Analysis On Target Fmentioning
confidence: 99%
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