Looking and seeing: visual information processing, visual attention and eye movements.

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Natural scenes contain a large amount of visual information. To deal with this, we use at least two strategies involving an active interplay between visual processing and eye-movements. First, a central portion of the retina (the "fovea") along with the large portion of the visual brain devoted to it, processes visual information from only a small part of the visual scene in great detail. The peripheral retina processes the rest of the scene in less detail. Approximately every few hundred milliseconds, a fast eye-movement (known as a saccade and lasting less than about fifty milliseconds) repositions the fovea to receive light from a new region of interest in the scene; different regions are thus processed sequentially in high detail. Second, a process known as visual attention enhances the processing of both foveal and peripheral retinal information from currently important stimuli in the scene: for example, bright shining objects in a night sky or red circles when searching for a clown's nose. This enhancement privileges our awareness of attended stimuli and makes them more likely to be processed foveally by being chosen as saccade targets. We know a fair amount about how attention and eye-movements interact in simple scenes with a single eye-movement. For example, a component of spatial attention shifts towards the saccade target prior to the saccade in many conditions. However, we know very little about how this knowledge gained from simple scenes carries over to humans functioning in naturalistic environments. Further, even for simple scenes, we are just starting to learn about how visual attention acts on near-foveal stimuli, even though our visual experience is dominated by foveal stimuli. In another example, since the visual brain represents the visual scene mostly in a eye-centered reference frame, the brain shifts its attentional representation rapidly and synchronized with the eye-movement in order to minimize disruptions. My recent work with my colleagues has just begun to shed light on this process. The long-term goal of this research program is to build a complete understanding of active vision: how do we look around when we are seeing. In the short-term, this proposal will aim to bridge the gap between real-life visual and eye-movement function and the basic mechanisms identified in much simpler tasks. We will use a combination of perceptual and eye-tracking experiments in humans, detailed computational modeling of their attention and eye-movement behavior, and the analysis of pre-existing neuronal datasets from monkeys performing naturalistic visual search tasks. This research is critical to our understanding of human vision in real-life environments, with numerous applications in almost any example of real-life vision that we can think of, from driving and reading to web-design, radiologists examining X-rays and even machine-learning algorithms trying to capture/replicate human visual function.