A dynamical scan-path model for task-dependence during scene viewing.

Schwetlick, Lisa; Backhaus, Daniel; Engbert, Ralf

doi:10.1037/rev0000379

Cited by 9 publications

(14 citation statements)

References 117 publications

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“…By implementing psychophysically uncovered mechanisms of attentional and oculomotor control, ScanDy allows to generate sequences of eye movements for any visual scene. Recent years have shown a growing interest in the simulation of time-ordered fixation sequences for static scenes (Tatler, Brockmole, and Carpenter, 2017; Malem-Shinitski et al, 2020; Schwetlick, Rothkegel, et al, 2020; Schwetlick, Backhaus, and Engbert, 2022; Kucharsky et al, 2021; Kümmerer, Bethge, and Wallis, 2022), as well as the frame-wise prediction of where humans tend to look on average when observing a dynamic scene (Molin, Etienne-Cummings, and Niebur, 2015; Min and Corso, 2019; Droste, Jiao, and Noble, 2020; Wang, Liu, et al, 2021). We are currently not aware of another computational model that is able to simulate time-resolved gaze positions for the full duration of dynamic scenes, analogous to human eye tracking data.…”

Section: Discussionmentioning

confidence: 99%

“…Scanpath prediction in static scenes was pioneered by the seminal work of Itti, Koch, and Niebur (1998), who implemented the previously postulated concept of a saliency map (Koch and Ullman, 1985) algorithmically and suggested a strategy to sequentially select locations in the saliency map based on a “winner-take-all” and a subsequent “inhibition of return” mechanism. A more detailed model of attentional dynamics and saccadic selection in static scene viewing was proposed with the SceneWalk model family (Engbert et al, 2015; Schütt et al, 2017; Schwetlick, Rothkegel, et al, 2020; Schwetlick, Backhaus, and Engbert, 2022). These models predict the likelihood of moving the gaze to a certain position based on a foveated access to saliency information and a leaky memory process.…”

Section: Introductionmentioning

confidence: 99%

“…They introduced a spatiotemporal likelihood function to statistically model fixation positions and fixation durations, which are assumed to follow a WALD (inverse Gaussian) distribution, simultaneously and were successful in reproducing many aspects of scanpath statistics. The most recent version of the SceneWalk model (Schwetlick, Backhaus, and Engbert, 2022) also incorporates fixation durations when generating scanpaths. The distribution of fixation durations is assumed to follow a Gamma distribution, which is parameterized such that the mean fixation duration at a location parametrically depends on the local image saliency at this position.…”

Section: Introductionmentioning

confidence: 99%

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Objects guide human gaze behavior in dynamic real-world scenes

Roth

Rolfs

Hellwich

et al. 2023

Preprint

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The complexity of natural scenes makes it challenging to experimentally study the mechanisms behind human gaze behavior when viewing dynamic environments. Historically, eye movements were believed to be driven primarily by bottom-up saliency, but increasing evidence suggests that objects also play a significant role in guiding attention. We present a new computational framework to investigate the importance of objects for attentional guidance. This framework is designed to simulate realistic scanpaths for dynamic real-world scenes, including saccade timing and smooth pursuit behavior. Individual model components are based on psychophysically uncovered mechanisms of visual attention and saccadic decision-making. All mechanisms are implemented in a modular fashion with a small number of well-interpretable parameters. To systematically analyze the importance of objects in guiding gaze behavior, we implemented four different models within this framework: two purely location-based models, where one is based on low-level saliency and one on high-level saliency, and two object-based models, with one incorporating low-level saliency for each object and the other one not using any saliency information. We optimized each model's parameters to reproduce the saccade amplitude and fixation duration distributions of human scanpaths using evolutionary algorithms. We compared model performance with respect to spatial and temporal fixation behavior, including the proportion of fixations exploring the background, as well as detecting, inspecting, and revisiting objects. A model with object-based attention and inhibition, which uses saliency information to prioritize between objects for saccadic selection, leads to scanpath statistics with the highest similarity to the human data. This demonstrates that scanpath models benefit from object-based attention and selection, suggesting that object-level attentional units play an important role in guiding attentional processing.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Objects guide human gaze behavior in dynamic real-world scenes

Roth

Rolfs

Hellwich

et al. 2023

Preprint

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“…Given these properties, we tested the computational faithfulness of SEAM using the Markov Chain Monte Carlo (MCMC) sampling algorithm DREAM zs Laloy and Vrugt (2012) based on profile log-likelihoods and model parameter recovery, similar to the approach taken in Rabe et al (2021). The DREAM zs (Laloy & Vrugt, 2012;ter Braak & Vrugt, 2008;Vrugt et al, 2009) sampler has previously been successfully used with complex dynamical models of eye-movement control, including SWIFT for reading (Rabe et al, 2021) and SceneWalk for scene viewing (Schwetlick et al, 2022;Schwetlick et al, 2020).…”

Section: Simulation Studymentioning

confidence: 99%

“…Successful examples of applying data assimilation for visual tasks are, for example, Sce-neWalk (Schwetlick et al, 2022;Schwetlick et al, 2020) for scene viewing and SWIFT (Rabe et al, 2021;Seelig et al, 2020) for reading. There, each event of the sequence, x i , is a fixation.…”

Section: Sequential Likelihoodmentioning

confidence: 99%

SEAM: An Integrated Activation-Coupled Model of Sentence Processing and Eye Movements in Reading

Rabe¹,

Paape²,

Mertzen³

et al. 2023

Preprint

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Models of eye-movement control during reading, developed largely within psychology, usually focus on visual, attentional, and motor processes but neglect postlexical language processing; by contrast, models of sentence comprehension processes, developed largely within psycholinguistics, generally focus only on postlexical language processes. We present a model that combines these two research threads, by integrating eye-movement control and sentence processing. Developing such an integrated model is extremely challenging and computationally demanding, but such an integration is an important step toward complete mathematical models of natural language comprehension in reading. We combine the SWIFT model of eye-movement control (Engbert et al.,

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How body postures affect gaze control in scene viewing under specific task conditions

Backhaus,

Engbert

2024

Exp Brain Res

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Gaze movements during visual exploration of natural scenes are typically investigated with the static picture viewing paradigm in the laboratory. While this paradigm is attractive for its highly controlled conditions, limitations in the generalizability of the resulting findings to more natural viewing behavior have been raised frequently. Here, we address the combined influences of body posture and viewing task on gaze behavior with the static picture viewing paradigm under free viewing as a baseline condition. We recorded gaze data using mobile eye tracking during postural manipulations in scene viewing. Specifically, in Experiment 1, we compared gaze behavior during head-supported sitting and quiet standing under two task conditions. We found that task affects temporal and spatial gaze parameters, while posture produces no effects on temporal and small effects on spatial parameters. In Experiment 2, we further investigated body posture by introducing four conditions (sitting with chin rest, head-free sitting, quiet standing, standing on an unstable platform). Again, we found no effects on temporal and small effects on spatial gaze parameters. In our experiments, gaze behavior is largely unaffected by body posture, while task conditions readily produce effects. We conclude that results from static picture viewing may allow predictions of gaze statistics under more natural viewing conditions, however, viewing tasks should be chosen carefully because of their potential effects on gaze characteristics.

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A dynamical scan-path model for task-dependence during scene viewing.

Cited by 9 publications

References 117 publications

Objects guide human gaze behavior in dynamic real-world scenes

Objects guide human gaze behavior in dynamic real-world scenes

SEAM: An Integrated Activation-Coupled Model of Sentence Processing and Eye Movements in Reading

How body postures affect gaze control in scene viewing under specific task conditions

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