Saliency models aim to predict where we look at within a visual scene. They are based on low-level visual features including color, intensity, and orientation. They process these visual features to generate a 2D saliency map, indicating the most salient part of the image. In contrast with saliency models, saccadic models intend to predict the sequence of eye fixations, i.e. the way observers deploy their gaze while viewing a stimulus on screen. Rather than computing a unique saliency map, saccadic models compute plausible visual scanpaths, i.e. the actual sequence of fixations and saccades unfolding across time. By the term plausible, we mean that the predicted scanpaths should be as similar as possible to human scanpaths. They should exhibit similar characteristics, such as the same distributions of saccade amplitudes and saccade orientations. Saccadic models offer many advantages over saliency models. The most important one is the ability to tailor the saccadic model to a particular context, such as a particular type of scene, a particular population or to a particular task at hand. This modelling framework not only relies on the image salience (i.e. properties of the stimulus) but most importantly on our ability to model viewing biases and systematic gaze patterns of observers. In this presentation, we introduce an age-dependent saccadic model able to reproduce the gaze behavior of a certain target age group.