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 - Engbert, Ralf
A1 - Rabe, Maximilian Michael
A1 - Schwetlick, Lisa
A1 - Seelig, Stefan A.
A1 - Reich, Sebastian
A1 - Vasishth, Shravan
T1 - Data assimilation in dynamical cognitive science
JF - Trends in cognitive sciences
N2 - Dynamical models make specific assumptions about cognitive processes that generate human behavior. In data assimilation, these models are tested against timeordered data. Recent progress on Bayesian data assimilation demonstrates that this approach combines the strengths of statistical modeling of individual differences with the those of dynamical cognitive models.
Y1 - 2022
U6 - https://doi.org/10.1016/j.tics.2021.11.006
SN - 1364-6613
SN - 1879-307X
VL - 26
IS - 2
SP - 99
EP - 102
PB - Elsevier
CY - Amsterdam
ER -
TY - JOUR
A1 - Malem-Shinitski, Noa
A1 - Opper, Manfred
A1 - Reich, Sebastian
A1 - Schwetlick, Lisa
A1 - Seelig, Stefan A.
A1 - Engbert, Ralf
T1 - A mathematical model of local and global attention in natural scene viewing
JF - PLoS Computational Biology : a new community journal
N2 - Author summary
Switching between local and global attention is a general strategy in human information processing. We investigate whether this strategy is a viable approach to model sequences of fixations generated by a human observer in a free viewing task with natural scenes. Variants of the basic model are used to predict the experimental data based on Bayesian inference. Results indicate a high predictive power for both aggregated data and individual differences across observers. The combination of a novel model with state-of-the-art Bayesian methods lends support to our two-state model using local and global internal attention states for controlling eye movements.
Understanding the decision process underlying gaze control is an important question in cognitive neuroscience with applications in diverse fields ranging from psychology to computer vision. The decision for choosing an upcoming saccade target can be framed as a selection process between two states: Should the observer further inspect the information near the current gaze position (local attention) or continue with exploration of other patches of the given scene (global attention)? Here we propose and investigate a mathematical model motivated by switching between these two attentional states during scene viewing. The model is derived from a minimal set of assumptions that generates realistic eye movement behavior. We implemented a Bayesian approach for model parameter inference based on the model's likelihood function. In order to simplify the inference, we applied data augmentation methods that allowed the use of conjugate priors and the construction of an efficient Gibbs sampler. This approach turned out to be numerically efficient and permitted fitting interindividual differences in saccade statistics. Thus, the main contribution of our modeling approach is two-fold; first, we propose a new model for saccade generation in scene viewing. Second, we demonstrate the use of novel methods from Bayesian inference in the field of scan path modeling.
Y1 - 2020
U6 - https://doi.org/10.1371/journal.pcbi.1007880
SN - 1553-734X
SN - 1553-7358
VL - 16
IS - 12
PB - PLoS
CY - San Fransisco
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 -