A Robotics-Inspired Scanpath Model Reveals the Importance of Uncertainty and Semantic Object Cues for Gaze Guidance in Dynamic Scenes
<|reference_start|>A Robotics-Inspired Scanpath Model Reveals the Importance of Uncertainty and Semantic Object Cues for Gaze Guidance in Dynamic Scenes: How we perceive objects around us depends on what we actively attend to, yet our eye movements depend on the perceived objects. Still, object segmentation and gaze behavior are typically treated as two independent processes. Drawing on an information processing pattern from robotics, we present a mechanistic model that simulates these processes for dynamic real-world scenes. Our image-computable model uses the current scene segmentation for object-based saccadic decision-making while using the foveated object to refine its scene segmentation recursively. To model this refinement, we use a Bayesian filter, which also provides an uncertainty estimate for the segmentation that we use to guide active scene exploration. We demonstrate that this model closely resembles observers' free viewing behavior, measured by scanpath statistics, including foveation duration and saccade amplitude distributions used for parameter fitting and higher-level statistics not used for fitting. These include how object detections, inspections, and returns are balanced and a delay of returning saccades without an explicit implementation of such temporal inhibition of return. Extensive simulations and ablation studies show that uncertainty promotes balanced exploration and that semantic object cues are crucial to form the perceptual units used in object-based attention. Moreover, we show how our model's modular design allows for extensions, such as incorporating saccadic momentum or pre-saccadic attention, to further align its output with human scanpaths.<|reference_end|>
@article{mengers2024a,
    title={A Robotics-Inspired Scanpath Model Reveals the Importance of Uncertainty
  and Semantic Object Cues for Gaze Guidance in Dynamic Scenes},
    author={Vito Mengers, Nicolas Roth, Oliver Brock, Klaus Obermayer, Martin
  Rolfs},
    journal={arXiv preprint arXiv:2408.01322},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01322},
    primaryClass={cs.CV cs.AI q-bio.NC}
}