@misc{BackhausEngbertRothkegeletal.2020, author = {Backhaus, Daniel and Engbert, Ralf and Rothkegel, Lars Oliver Martin and Trukenbrod, Hans Arne}, title = {Task-dependence in scene perception: Head unrestrained viewing using mobile eye-tracking}, series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, journal = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, number = {5}, issn = {1866-8364}, doi = {10.25932/publishup-51912}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-519124}, pages = {23}, year = {2020}, abstract = {Real-world scene perception is typically studied in the laboratory using static picture viewing with restrained head position. Consequently, the transfer of results obtained in this paradigm to real-word scenarios has been questioned. The advancement of mobile eye-trackers and the progress in image processing, however, permit a more natural experimental setup that, at the same time, maintains the high experimental control from the standard laboratory setting. We investigated eye movements while participants were standing in front of a projector screen and explored images under four specific task instructions. Eye movements were recorded with a mobile eye-tracking device and raw gaze data were transformed from head-centered into image-centered coordinates. We observed differences between tasks in temporal and spatial eye-movement parameters and found that the bias to fixate images near the center differed between tasks. Our results demonstrate that current mobile eye-tracking technology and a highly controlled design support the study of fine-scaled task dependencies in an experimental setting that permits more natural viewing behavior than the static picture viewing paradigm.}, language = {en} } @article{SchwetlickRothkegelTrukenbrodetal.2020, author = {Schwetlick, Lisa and Rothkegel, Lars Oliver Martin and Trukenbrod, Hans Arne and Engbert, Ralf}, title = {Modeling the effects of perisaccadic attention on gaze statistics during scene viewing}, series = {Communications biology}, volume = {3}, journal = {Communications biology}, number = {1}, publisher = {Springer Nature}, address = {London}, issn = {2399-3642}, doi = {10.1038/s42003-020-01429-8}, pages = {11}, year = {2020}, abstract = {Lisa Schwetlick et al. present a computational model linking visual scan path generation in scene viewing to physiological and experimental work on perisaccadic covert attention, the act of attending to an object visually without obviously moving the eyes toward it. They find that integrating covert attention into predictive models of visual scan paths greatly improves the model's agreement with experimental data.
How we perceive a visual scene depends critically on the selection of gaze positions. For this selection process, visual attention is known to play a key role in two ways. First, image-features attract visual attention, a fact that is captured well by time-independent fixation models. Second, millisecond-level attentional dynamics around the time of saccade drives our gaze from one position to the next. These two related research areas on attention are typically perceived as separate, both theoretically and experimentally. Here we link the two research areas by demonstrating that perisaccadic attentional dynamics improve predictions on scan path statistics. In a mathematical model, we integrated perisaccadic covert attention with dynamic scan path generation. Our model reproduces saccade amplitude distributions, angular statistics, intersaccadic turning angles, and their impact on fixation durations as well as inter-individual differences using Bayesian inference. Therefore, our result lend support to the relevance of perisaccadic attention to gaze statistics.}, language = {en} } @article{BackhausEngbertRothkegeletal.2020, author = {Backhaus, Daniel and Engbert, Ralf and Rothkegel, Lars Oliver Martin and Trukenbrod, Hans Arne}, title = {Task-dependence in scene perception: Head unrestrained viewing using mobile eye-tracking}, series = {Journal of vision}, volume = {20}, journal = {Journal of vision}, number = {5}, publisher = {Association for Research in Vision and Opthalmology}, address = {Rockville}, issn = {1534-7362}, doi = {10.1167/jov.20.5.3}, pages = {1 -- 21}, year = {2020}, abstract = {Real-world scene perception is typically studied in the laboratory using static picture viewing with restrained head position. Consequently, the transfer of results obtained in this paradigm to real-word scenarios has been questioned. The advancement of mobile eye-trackers and the progress in image processing, however, permit a more natural experimental setup that, at the same time, maintains the high experimental control from the standard laboratory setting. We investigated eye movements while participants were standing in front of a projector screen and explored images under four specific task instructions. Eye movements were recorded with a mobile eye-tracking device and raw gaze data were transformed from head-centered into image-centered coordinates. We observed differences between tasks in temporal and spatial eye-movement parameters and found that the bias to fixate images near the center differed between tasks. Our results demonstrate that current mobile eye-tracking technology and a highly controlled design support the study of fine-scaled task dependencies in an experimental setting that permits more natural viewing behavior than the static picture viewing paradigm.}, language = {en} } @article{SchuettRothkegelTrukenbrodetal.2017, author = {Sch{\"u}tt, Heiko Herbert and Rothkegel, Lars Oliver Martin and Trukenbrod, Hans Arne and Reich, Sebastian and Wichmann, Felix A. and Engbert, Ralf}, title = {Likelihood-based parameter estimation and comparison of dynamical cognitive models}, series = {Psychological Review}, volume = {124}, journal = {Psychological Review}, number = {4}, publisher = {American Psychological Association}, address = {Washington}, issn = {0033-295X}, doi = {10.1037/rev0000068}, pages = {505 -- 524}, year = {2017}, abstract = {Dynamical models of cognition play an increasingly important role in driving theoretical and experimental research in psychology. Therefore, parameter estimation, model analysis and comparison of dynamical models are of essential importance. In this article, we propose a maximum likelihood approach for model analysis in a fully dynamical framework that includes time-ordered experimental data. Our methods can be applied to dynamical models for the prediction of discrete behavior (e.g., movement onsets); in particular, we use a dynamical model of saccade generation in scene viewing as a case study for our approach. For this model, the likelihood function can be computed directly by numerical simulation, which enables more efficient parameter estimation including Bayesian inference to obtain reliable estimates and corresponding credible intervals. Using hierarchical models inference is even possible for individual observers. Furthermore, our likelihood approach can be used to compare different models. In our example, the dynamical framework is shown to outperform nondynamical statistical models. Additionally, the likelihood based evaluation differentiates model variants, which produced indistinguishable predictions on hitherto used statistics. Our results indicate that the likelihood approach is a promising framework for dynamical cognitive models.}, language = {en} } @article{RothkegelSchuettTrukenbrodetal.2019, author = {Rothkegel, Lars Oliver Martin and Sch{\"u}tt, Heiko Herbert and Trukenbrod, Hans Arne and Wichmann, Felix A. and Engbert, Ralf}, title = {Searchers adjust their eye-movement dynamics to target characteristics in natural scenes}, series = {Scientific reports}, volume = {9}, journal = {Scientific reports}, publisher = {Nature Publ. Group}, address = {London}, issn = {2045-2322}, doi = {10.1038/s41598-018-37548-w}, pages = {12}, year = {2019}, abstract = {When searching a target in a natural scene, it has been shown that both the target's visual properties and similarity to the background influence whether and how fast humans are able to find it. So far, it was unclear whether searchers adjust the dynamics of their eye movements (e.g., fixation durations, saccade amplitudes) to the target they search for. In our experiment, participants searched natural scenes for six artificial targets with different spatial frequency content throughout eight consecutive sessions. High-spatial frequency targets led to smaller saccade amplitudes and shorter fixation durations than low-spatial frequency targets if target identity was known. If a saccade was programmed in the same direction as the previous saccade, fixation durations and successive saccade amplitudes were not influenced by target type. Visual saliency and empirical fixation density at the endpoints of saccades which maintain direction were comparatively low, indicating that these saccades were less selective. Our results suggest that searchers adjust their eye movement dynamics to the search target efficiently, since previous research has shown that low-spatial frequencies are visible farther into the periphery than high-spatial frequencies. We interpret the saccade direction specificity of our effects as an underlying separation into a default scanning mechanism and a selective, target-dependent mechanism.}, language = {en} } @article{SchuettRothkegelTrukenbrodetal.2019, author = {Sch{\"u}tt, Heiko Herbert and Rothkegel, Lars Oliver Martin and Trukenbrod, Hans Arne and Engbert, Ralf and Wichmann, Felix A.}, title = {Disentangling bottom-up versus top-down and low-level versus high-level influences on eye movements over time}, series = {Journal of vision}, volume = {19}, journal = {Journal of vision}, number = {3}, publisher = {Association for Research in Vision and Opthalmology}, address = {Rockville}, issn = {1534-7362}, doi = {10.1167/19.3.1}, pages = {23}, year = {2019}, abstract = {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.}, language = {en} } @misc{SchwetlickTrukenbrodEngbert2019, author = {Schwetlick, Lisa and Trukenbrod, Hans Arne and Engbert, Ralf}, title = {The Influence of Visual Long Term Memory on Eye Movements During Scene Viewing}, series = {Perception}, volume = {48}, journal = {Perception}, number = {S1}, publisher = {Sage Publ.}, address = {London}, issn = {0301-0066}, pages = {138 -- 138}, year = {2019}, language = {en} } @misc{SchuettRothkegelTrukenbrodetal.2019, author = {Sch{\"u}tt, Heiko Herbert and Rothkegel, Lars Oliver Martin and Trukenbrod, Hans Arne and Engbert, Ralf and Wichmann, Felix A.}, title = {Predicting fixation densities over time from early visual processing}, series = {Perception}, volume = {48}, journal = {Perception}, publisher = {Sage Publ.}, address = {London}, issn = {0301-0066}, pages = {64 -- 65}, year = {2019}, abstract = {Bottom-up saliency is often cited as a factor driving the choice of fixation locations of human observers, based on the (partial) success of saliency models to predict fixation densities in free viewing. However, these observations are only weak evidence for a causal role of bottom-up saliency in natural viewing behaviour. To test bottom-up saliency more directly, we analyse the performance of a number of saliency models---including our own saliency model based on our recently published model of early visual processing (Sch{\"u}tt \& Wichmann, 2017, JoV)---as well as the theoretical limits for predictions over time. On free viewing data our model performs better than classical bottom-up saliency models, but worse than the current deep learning based saliency models incorporating higher-level information like knowledge about objects. However, on search data all saliency models perform worse than the optimal image independent prediction. We observe that the fixation density in free viewing is not stationary over time, but changes over the course of a trial. It starts with a pronounced central fixation bias on the first chosen fixation, which is nonetheless influenced by image content. Starting with the 2nd to 3rd fixation, the fixation density is already well predicted by later densities, but more concentrated. From there the fixation distribution broadens until it reaches a stationary distribution around the 10th fixation. Taken together these observations argue against bottom-up saliency as a mechanistic explanation for eye movement control after the initial orienting reaction in the first one to two saccades, although we confirm the predictive value of early visual representations for fixation locations. The fixation distribution is, first, not well described by any stationary density, second, is predicted better when including object information and, third, is badly predicted by any saliency model in a search task.}, language = {en} } @article{TrukenbrodBarthelmeWichmannetal.2019, author = {Trukenbrod, Hans Arne and Barthelme, Simon and Wichmann, Felix A. and Engbert, Ralf}, title = {Spatial statistics for gaze patterns in scene viewing}, series = {Journal of vision}, volume = {19}, journal = {Journal of vision}, number = {5}, publisher = {Association for Research in Vision and Opthalmology}, address = {Rockville}, issn = {1534-7362}, doi = {10.1167/19.6.5}, pages = {1 -- 19}, year = {2019}, abstract = {Scene viewing is used to study attentional selection in complex but still controlled environments. One of the main observations on eye movements during scene viewing is the inhomogeneous distribution of fixation locations: While some parts of an image are fixated by almost all observers and are inspected repeatedly by the same observer, other image parts remain unfixated by observers even after long exploration intervals. Here, we apply spatial point process methods to investigate the relationship between pairs of fixations. More precisely, we use the pair correlation function, a powerful statistical tool, to evaluate dependencies between fixation locations along individual scanpaths. We demonstrate that aggregation of fixation locations within 4 degrees is stronger than expected from chance. Furthermore, the pair correlation function reveals stronger aggregation of fixations when the same image is presented a second time. We use simulations of a dynamical model to show that a narrower spatial attentional span may explain differences in pair correlations between the first and the second inspection of the same image.}, language = {en} } @article{RothkegelTrukenbrodSchuettetal.2017, author = {Rothkegel, Lars Oliver Martin and Trukenbrod, Hans Arne and Sch{\"u}tt, Heiko Herbert and Wichmann, Felix A. and Engbert, Ralf}, title = {Temporal evolution of the central fixation bias in scene viewing}, series = {Journal of vision}, volume = {17}, journal = {Journal of vision}, publisher = {Association for Research in Vision and Opthalmology}, address = {Rockville}, issn = {1534-7362}, doi = {10.1167/17.13.3}, pages = {1626 -- 1638}, year = {2017}, abstract = {When watching the image of a natural scene on a computer screen, observers initially move their eyes toward the center of the image—a reliable experimental finding termed central fixation bias. This systematic tendency in eye guidance likely masks attentional selection driven by image properties and top-down cognitive processes. Here, we show that the central fixation bias can be reduced by delaying the initial saccade relative to image onset. In four scene-viewing experiments we manipulated observers' initial gaze position and delayed their first saccade by a specific time interval relative to the onset of an image. We analyzed the distance to image center over time and show that the central fixation bias of initial fixations was significantly reduced after delayed saccade onsets. We additionally show that selection of the initial saccade target strongly depended on the first saccade latency. A previously published model of saccade generation was extended with a central activation map on the initial fixation whose influence declined with increasing saccade latency. This extension was sufficient to replicate the central fixation bias from our experiments. Our results suggest that the central fixation bias is generated by default activation as a response to the sudden image onset and that this default activation pattern decreases over time. Thus, it may often be preferable to use a modified version of the scene viewing paradigm that decouples image onset from the start signal for scene exploration to explicitly reduce the central fixation bias.}, language = {en} }