The neurophysiology of eye movements has been studied extensively, and several computational models have been proposed for decision-making processes that underlie the generation of saccades towards a visual stimulus in a situation of uncertainty. However, existing models do not account for the dynamics of learning across a sequence of stimuli, and they do not apply to situations in which subjects are exposed to events with conditional probabilities. In this methodological paper, we extend previous `linear rise to threshold' models to explain the latency between the onset of a peripheral visual target and the beginning of a saccade towards it. By reformulating previous models in terms of a generative, hierarchical model, two separate sub-models can be combined to account for the interplay between learning of target locations across trials and the decision-making process within trials. We derive a maximum-likelihood scheme for parameter estimation as well as model comparison on the basis of log likelihood ratios. The utility of the model is demonstrated by applying it to empirical saccade data acquired from three healthy subjects. Model comparison is used (i) to show that eye movements do not only reflect marginal but also conditional probabilities of target locations, and (ii) to reveal subject-specic learning profiles over trials. These individual learning profiles are sufficiently distinct that test samples can be successfully mapped onto the correct subject by a naive Bayes classifier. Altogether, our approach successfully extends existing `linear rise to threshold' models of saccadic decision making, overcomes some of their limitations, and enables statistical inference both about learning of target locations across trials and decision-making processes within trials.