The core model of sentence processing used in the book is introduced and its empirical coverage relative to the existing reading time data is considered. Here, we also discuss the Approximate Bayesian Computation method for parameter estimation for model evaluation.

This chapter discusses two extensions of the model presented in the previous chapter: the effect of prominence (through discourse prominence, etc.) and the effect of so-called multi-associative cues. The empirical coverage of the extended model is evaluated against benchmark data.

This chapter discusses three central phenomena of interest in sentence processing: reanalysis, underspecification, and capacity-based differences in sentence comprehension. The model's quantitative predictions are evaluated against two benchmark data-sets that investigate reanalysis and underspecification.

This chapter presents another extension of the core model: an eye-movement control system is integrated with the parsing architecture, and this extended model is investigated using benchmark eyetracking data (the Potsdam Sentence Corpus).

This chapter investigates whether sentence comprehension difficulty in aphasia can be explained in terms of retrieval processes. By modelling individuals with aphasia (IWAs) separately, we show that different IWAs show impairments along different dimensions: slowed processing, intermittent deficiency, and resource reduction. The parameters in the cue-based retrieval model have a theoretical interpretation that allows these three theories to be implemented within the architecture. In a further investigation, we compare the relative predictive accuracy of the cue-based model with that of the direct-access model. The benchmark data here are from Caplan et al. (2015); k-fold cross-validation is used as in the preceding chapter. The cue-based retrieval model is shown to have a better predictive performance.

This chapter presents a model comparison between two competing models of retrieval processes: the cue-based retrieval model presented in this book and the direct-access model. The two models are implemented in a Bayesian framework, and then model comparison is carried out using k-fold cross-validation. The benchmark data used for evaluation are from a previously published large-sample, self-paced reading study (181 participants). The results show that the direct-access model has a better performance on this benchmark data than the cue-based retrieval model.