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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 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.

Using gaze-contingent display changes in the boundary paradigm during sentence reading, it has recently been shown that parafoveal word-processing difficulties affect fixations on words to the right of the boundary. Current interpretations of this post-boundary preview difficulty effect range from delayed parafoveal-on-foveal effects in parallel word-processing models to forced fixations in serial word-processing models. However, these findings are based on an experimental design that, while allowing to isolate preview difficulty effects, might have established a bias with respect to asymmetries in parafoveal preview benefit for high-frequent and low-frequent target words. Here, we present a revision of this paradigm varying the preview’s lexical frequency and keeping the target word constant. We found substantial effects of the preview difficulty in fixation durations after the boundary confirming that preview processing affects the oculomotor decisions not only via trans-saccadic integration of preview and target word information. An additional time-course analysis showed that the preview difficulty effect was significant across the full fixation duration distribution on the target word without any evidence on the pretarget word before the boundary. We discuss implications of the accumulating evidence of post-boundary preview difficulty effects for models of eye movement control during reading.

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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.