TY - JOUR

T1 - A tutorial on Bayesian inference for dynamical modeling of eye-movement control during reading

AU - Engbert, Ralf

AU - Rabe, Maximilian M.

N1 - Funding Information: We thank Joseph Houpt and two anonymous reviewers for comments that contributed to a substantial improvement of the manuscript. We also thank Sebastian Reich and Shravan Vasishth for valuable discussions. The authors acknowledge support from Deutsche Forschungsgemeinschaft via grants CRC 1294 Data Assimilation (Project No. 318763901) and CRC 1287 Limits of Variability in Language (Project No. 317633480). Sections 1 to 4 of this work were presented first at the Sixth Summer School on Statistical Methods for Linguistics and Psychology (SMLP), September 12–16, 2022, Potsdam, Germany. Funding Information: We thank Joseph Houpt and two anonymous reviewers for comments that contributed to a substantial improvement of the manuscript. We also thank Sebastian Reich and Shravan Vasishth for valuable discussions. The authors acknowledge support from Deutsche Forschungsgemeinschaft via grants CRC 1294 Data Assimilation (Project No. 318763901) and CRC 1287 Limits of Variability in Language (Project No. 317633480). Sections 1 to 4 of this work were presented first at the Sixth Summer School on Statistical Methods for Linguistics and Psychology (SMLP), September 12–16, 2022, Potsdam, Germany. Publisher Copyright: © 2024 Elsevier Inc.

PY - 2024/4

Y1 - 2024/4

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - Dynamical model

KW - Eye movements

KW - MCMC

KW - Reading

UR - http://www.scopus.com/inward/record.url?scp=85187361898&partnerID=8YFLogxK

U2 - 10.1016/j.jmp.2024.102843

DO - 10.1016/j.jmp.2024.102843

M3 - Review

AN - SCOPUS:85187361898

VL - 119

JO - Journal of Mathematical Psychology

JF - Journal of Mathematical Psychology

SN - 0022-2496

M1 - 102843

ER -