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  - 
TY  - JOUR
A1  - Schwarz, Wolfgang
A1  - Miller, Jeff
T1  - GSDT: An Integrative Model of Visual Search
JF  - Journal of experimental psychology : Human perception and performance
N2  - We present a new quantitative process model (GSDT) of visual search that seeks to integrate various processing mechanisms suggested by previous studies within a single, coherent conceptual frame. It incorporates and combines 4 distinct model components: guidance (G), a serial (S) item inspection process, diffusion (D) modeling of individual item inspections, and a strategic termination (T) rule. For this model, we derive explicit closed-form results for response probability and mean search time (reaction time [RT]) as a function of display size and target presence/absence. The fit of the model is compared in detail to data from 4 visual search experiments in which the effects of target/distractor discriminability and of target prevalence on performance (present/absent display size functions for mean RT and error rate) are studied. We describe how GSDT accounts for various detailed features of our results such as the probabilities of hits and correct rejections and their mean RTs; we also apply the model to explain further aspects of the data, such as RT variance and mean miss RT.
KW  - visual search
KW  - signal prevalence
KW  - strategic termination
KW  - diffusion model
KW  - display size effect
Y1  - 2016
U6  - https://doi.org/10.1037/xhp0000247
SN  - 0096-1523
SN  - 1939-1277
VL  - 42
SP  - 1654
EP  - 1671
PB  - American Psychological Association
CY  - Washington
ER  -