TY - JOUR

T1 - A Bayesian Approach to Dynamical Modeling of Eye-Movement Control in Reading of Normal, Mirrored, and Scrambled Texts

AU - Rabe, Maximilian M.

AU - Chandra, Johan

AU - Krügel, André

AU - Seelig, Stefan A.

AU - Vasishth, Shravan

AU - Engbert, Ralf

N1 - Funding Information: This work was funded by Deutsche Forschungsgemeinschaft via Collaborative Research Center (SFB) 1287, project B03 (project no. 317633480). R. E. and S. A. S. received additional support by SFB 1294, project B03 (project no. 318763901). We acknowledge support by Norddeutscher Verbund für Hoch- und Höchstleistungsrechnen (HLRN, project no. bbx00001) for providing high-performance computing resources that contributed to the research results reported in this work. We thank Martijn Meeter for valuable comments on the manuscript. Publisher Copyright: © 2021 American Psychological Association

PY - 2021

Y1 - 2021

N2 - In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777–813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between-subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions

AB - In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777–813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between-subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions

KW - Bayesian inference

KW - Dynamical models

KW - Individual differences

KW - Oculomotor control

KW - Reading eye movements

UR - http://www.scopus.com/inward/record.url?scp=85107847446&partnerID=8YFLogxK

U2 - 10.1037/rev0000268

DO - 10.1037/rev0000268

M3 - Journal article

C2 - 33983783

AN - SCOPUS:85107847446

VL - 128

SP - 803

EP - 823

JO - Psychological Review

JF - Psychological Review

SN - 0033-295X

IS - 5

ER -