A tutorial on Bayesian inference for dynamical modeling of eye-movement control during reading
Publikation: Bidrag til tidsskrift › Review › Forskning › fagfællebedømt
Dynamical models are crucial for developing process-oriented, quantitative theories in cognition and behavior. Due to the impressive progress in cognitive theory, domain-specific dynamical models are complex, which typically creates challenges in statistical inference. Mathematical models of eye-movement control might be looked upon as a representative case study. In this tutorial, we introduce and analyze the SWIFT model (Engbert et al., 2002; Engbert et al., 2005), a dynamical modeling framework for eye-movement control in reading that was developed to explain all types of saccades observed in experiments from an activation-based approach. We provide an introduction to dynamical modeling, which explains the basic concepts of SWIFT and its statistical inference. We discuss the likelihood function of a simplified version of the SWIFT model as a key foundation for Bayesian parameter estimation (Rabe et al., 2021; Seelig et al., 2019). In posterior predictive checks, we demonstrate that the simplified model can reproduce interindividual differences via parameter variation. All computations in this tutorial are implemented in the R-Language for Statistical Computing and are made publicly available. We expect that the tutorial might be helpful for advancing dynamical models in other areas of cognitive science.
Originalsprog | Engelsk |
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Artikelnummer | 102843 |
Tidsskrift | Journal of Mathematical Psychology |
Vol/bind | 119 |
ISSN | 0022-2496 |
DOI | |
Status | Udgivet - apr. 2024 |
Bibliografisk note
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© 2024 Elsevier Inc.
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