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Space-time modeling of intensive binary time series eye-tracking data using a generalized additive logistic regression model.
Cho, Sun-Joo; Brown-Schmidt, Sarah; De Boeck, Paul; Naveiras, Matthew.
Afiliación
  • Cho SJ; Department of Psychology and Human Development.
  • Brown-Schmidt S; Department of Psychology and Human Development.
  • De Boeck P; Department of Psychology.
  • Naveiras M; Department of Psychology and Human Development.
Psychol Methods ; 27(3): 307-346, 2022 Jun.
Article en En | MEDLINE | ID: mdl-35446050
Eye-tracking has emerged as a popular method for empirical studies of cognitive processes across multiple substantive research areas. Eye-tracking systems are capable of automatically generating fixation-location data over time at high temporal resolution. Often, the researcher obtains a binary measure of whether or not, at each point in time, the participant is fixating on a critical interest area or object in the real world or in a computerized display. Eye-tracking data are characterized by spatial-temporal correlations and random variability, driven by multiple fine-grained observations taken over small time intervals (e.g., every 10 ms). Ignoring these data complexities leads to biased inferences for the covariates of interest such as experimental condition effects. This article presents a novel application of a generalized additive logistic regression model for intensive binary time series eye-tracking data from a between- and within-subjects experimental design. The model is formulated as a generalized additive mixed model (GAMM) and implemented in the mgcv R package. The generalized additive logistic regression model was illustrated using an empirical data set aimed at understanding the accommodation of regional accents in spoken language processing. Accuracy of parameter estimates and the importance of modeling the spatial-temporal correlations in detecting the experimental condition effects were shown in conditions similar to our empirical data set via a simulation study. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tecnología de Seguimiento Ocular Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Psychol Methods Asunto de la revista: PSICOLOGIA Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tecnología de Seguimiento Ocular Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Psychol Methods Asunto de la revista: PSICOLOGIA Año: 2022 Tipo del documento: Article Pais de publicación: Estados Unidos