A Bayesian Approach to Dynamical Modeling of Eye-Movement Control in Reading of Normal, Mirrored, and Scrambled Texts
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A Bayesian Approach to Dynamical Modeling of Eye-Movement Control in Reading of Normal, Mirrored, and Scrambled Texts. / Rabe, Maximilian M.; Chandra, Johan; Krügel, André; Seelig, Stefan A.; Vasishth, Shravan; Engbert, Ralf.
In: Psychological Review, Vol. 128, No. 5, 2021, p. 803-823.Research output: Contribution to journal › Journal article › Research › peer-review
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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 -
ID: 389895108