TY - JOUR A1 - Barthelme, Simon A1 - Trukenbrod, Hans Arne A1 - Engbert, Ralf A1 - Wichmann, Felix A. T1 - Modeling fixation locations using spatial point processes JF - Journal of vision N2 - Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate and why they fixated where they fixated. To a first approximation, a set of fixations can be viewed as a set of points in space; this implies that fixations are spatial data and that the analysis of fixation locations can be beneficially thought of as a spatial statistics problem. We argue that thinking of fixation locations as arising from point processes is a very fruitful framework for eye-movement data, helping turn qualitative questions into quantitative ones. We provide a tutorial introduction to some of the main ideas of the field of spatial statistics, focusing especially on spatial Poisson processes. We show how point processes help relate image properties to fixation locations. In particular we show how point processes naturally express the idea that image features' predictability for fixations may vary from one image to another. We review other methods of analysis used in the literature, show how they relate to point process theory, and argue that thinking in terms of point processes substantially extends the range of analyses that can be performed and clarify their interpretation. KW - eye movements KW - fixation locations KW - saliency KW - modeling KW - point process KW - spatial statistics Y1 - 2013 U6 - https://doi.org/10.1167/13.12.1 SN - 1534-7362 VL - 13 IS - 12 PB - Association for Research in Vision and Opthalmology CY - Rockville ER - TY - JOUR A1 - Schütt, Heiko Herbert A1 - Rothkegel, Lars Oliver Martin A1 - Trukenbrod, Hans Arne A1 - Engbert, Ralf A1 - Wichmann, Felix A. T1 - Disentangling bottom-up versus top-down and low-level versus high-level influences on eye movements over time JF - Journal of vision N2 - 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 analyzing their influence over time. For this purpose, we develop a saliency model that 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 deep neural network-based saliency model. To account for top-down influences, we evaluate the models on two large data sets with different tasks: first, a memorization 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 characterized by a gradual broadening of the fixation density, and a steady state that is reached after roughly 10 fixations. Saccade-target selection during the initial exploration and in the steady state is related to similar areas of interest, which are better predicted when including high-level features. In the search data set, 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. KW - saliency KW - fixations KW - natural scenes KW - visual search KW - eye movements Y1 - 2019 U6 - https://doi.org/10.1167/19.3.1 SN - 1534-7362 VL - 19 IS - 3 PB - Association for Research in Vision and Opthalmology CY - Rockville ER -