  Bottom-up and top-down, as well as low-level and high-level factors influence
where we fixate when viewing natural scenes. However, the importance of each of
these factors and how they interact remains a matter of debate. Here, we
disentangle these factors by analysing their influence over time. For this
purpose we develop a saliency model which is based on the internal
representation of a recent early spatial vision model to measure the low-level
bottom-up factor. To measure the influence of high-level bottom-up features, we
use a recent DNN-based saliency model. To account for top-down influences, we
evaluate the models on two large datasets with different tasks: first, a
memorisation task and, second, a search task. Our results lend support to a
separation of visual scene exploration into three phases: The first saccade, an
initial guided exploration characterised by a gradual broadening of the
fixation density, and an steady state which is reached after roughly 10
fixations. Saccade target selection during the initial exploration and in the
steady state are related to similar areas of interest, which are better
predicted when including high-level features. In the search dataset, fixation
locations are determined predominantly by top-down processes. In contrast, the
first fixation follows a different fixation density and contains a strong
central fixation bias. Nonetheless, first fixations are guided strongly by
image properties and as early as 200 ms after image onset, fixations are better
predicted by high-level information. We conclude that any low-level bottom-up
factors are mainly limited to the generation of the first saccade. All saccades
are better explained when high-level features are considered, and later this
high-level bottom-up control can be overruled by top-down influences.
