Abstract

The sequential deployment of gaze to regions of interest is an integral part of human visual function. Owing to its central importance, decades of research have focused on predicting gaze locations, but there has been relatively little formal attempt to predict the temporal aspects of gaze deployment in natural multi-tasking situations. We approach this problem by decomposing complex visual behaviour into individual task modules that require independent sources of visual information for control, in order to model human gaze deployment on different task-relevant objects. We introduce a softmax barrier model for gaze selection that uses two key elements: a priority parameter that represents task importance per module, and noise estimates that allow modules to represent uncertainty about the state of task-relevant visual information. Comparisons with human gaze data gathered in a virtual driving environment show that the model closely approximates human performance.

Highlights

  • Introduction and backgroundHuman vision interrogates complex, noisy, dynamic environments to accomplish tasks in the world

  • Apparently so effortlessly, yet so reliably? What kind of a control structure is robust in the face of the varying nature of the visual world, and allows us to reliably arrive at our goals? Despite over a century of research on eye movements, we have very little knowledge of the way that the task demands of the visual world are handled by the brain

  • This paper demonstrates a similar model of human visual processing and control where task-oriented modules representing reward and uncertainty are used to direct driving in a dynamic, noisy environment

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Summary

Introduction and background

Noisy, dynamic environments to accomplish tasks in the world. While driving a car, a person navigates to a desired destination (e.g. grocery store) while paying attention to different types of objects in the environment (pedestrians, vehicles, etc.) and obeying traffic laws (speed limit, stop signs, etc.). Humans manage these competing demands for visual information via the deployment of a foveated visual system, which must be actively moved to different targets to obtain highresolution image information. Humans and other animals exhibit a variety of basic orientation and avoidance responses to visually salient stimuli, e.g. a looming stimulus can invoke avoidance Such basic responses were passed on over generations of animals that survived owing to the advantage provided. License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited

Modelling visual attention
Driving simulation
The soft barrier model
Model implementation and results
Conclusion
Full Text
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