Top-down and bottom-up , 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 simple saliency model , which uses the internal representation of a recent early spatial vision model as input and thus measures the influence of the low-level bottom-up factor. To measure the influence of high-level bottom-up features, we use DeepGaze II, a recent DNN-based saliency model. To vary top-down influences, we evaluate the models on a large Corpus dataset with a memorization task andon a large visual search dataset. We can separate the exploration of a natural scene into three phases: The first saccade, which follows a different narrower distribution, an initial guided exploration characterized by a gradual broadening of the fixation density and an equilibrium state which is reached after roughly 10 fixations. The initial exploration and the equilibrium state target similar areas which are determined largely top-down in the search dataset and are better predicted on the Corpus dataset when including high-level features. In  contrast, the first fixation targets a different fixation density and contains a strong central fixation bias , but also the strongest guidance by the image . Nonetheless, the first fixations are better predicted by including high-level information as early as 200ms after image onset  . We conclude that any low-level bottom-up factors are restricted to the first saccade, possibly caused by the image onset. Later eye movements are better explained by including high-level features , but this high-level bottom-up control can be overruled by top-down influences.
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.
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