TY  - JOUR
A1  - Seelig, Stefan A.
A1  - Rabe, Maximilian Michael
A1  - Malem-Shinitski, Noa
A1  - Risse, Sarah
A1  - Reich, Sebastian
A1  - Engbert, Ralf
T1  - Bayesian parameter estimation for the SWIFT model of eye-movement control during reading
JF  - Journal of mathematical psychology
N2  - Process-oriented theories of cognition must be evaluated against time-ordered observations. Here we present a representative example for data assimilation of the SWIFT model, a dynamical model of the control of fixation positions and fixation durations during natural reading of single sentences. First, we develop and test an approximate likelihood function of the model, which is a combination of a spatial, pseudo-marginal likelihood and a temporal likelihood obtained by probability density approximation Second, we implement a Bayesian approach to parameter inference using an adaptive Markov chain Monte Carlo procedure. Our results indicate that model parameters can be estimated reliably for individual subjects. We conclude that approximative Bayesian inference represents a considerable step forward for computational models of eye-movement control, where modeling of individual data on the basis of process-based dynamic models has not been possible so far.
KW  - dynamical models
KW  - reading
KW  - eye movements
KW  - saccades
KW  - likelihood function
KW  - Bayesian inference
KW  - MCMC
KW  - interindividual differences
Y1  - 2020
U6  - https://doi.org/10.1016/j.jmp.2019.102313
SN  - 0022-2496
SN  - 1096-0880
VL  - 95
PB  - Elsevier
CY  - San Diego
ER  - 
TY  - JOUR
A1  - Gafos, Adamantios I.
A1  - Lieshout, Pascal H. H. M. van
T1  - Models and theories of speech production
BT  - editorial
JF  - Frontiers in psychology
KW  - speech production
KW  - motor control
KW  - dynamical models
KW  - phonology
KW  - speech
KW  - disorders
KW  - timing
Y1  - 2020
U6  - https://doi.org/10.3389/fpsyg.2020.01238
SN  - 1664-1078
VL  - 11
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  - 
TY  - JOUR
A1  - Rabe, Maximilian Michael
A1  - Chandra, Johan
A1  - Krügel, André
A1  - Seelig, Stefan A.
A1  - Vasishth, Shravan
A1  - Engbert, Ralf
T1  - A bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts
JF  - Psychological Review
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.
KW  - reading eye movements
KW  - dynamical models
KW  - Bayesian inference
KW  - oculomotor
KW  - control
KW  - individual differences
Y1  - 2021
U6  - https://doi.org/10.1037/rev0000268
SN  - 0033-295X
SN  - 1939-1471
VL  - 128
IS  - 5
SP  - 803
EP  - 823
PB  - American Psychological Association
CY  - Washington
ER  -