Neural Mechanisms of Fixation Choice while Searching Natural Scenes

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The overall goal of these experiments is to understand how the brain controls where we look. To accomplish this, it is important to study brain activity and behavior under conditions that closely approximate those in the real world. All of the experiments we propose to do will use awake behaving rhesus monkeys as subjects. In prior work, we have studied activity in the cortical frontal eye field while monkeys looked at images of natural scenes. The frontal eye field (FEF) is closely involved in the control of purposive voluntary eye movements. While the monkey searched for a target hidden in the images of natural scenes, the activity of FEF neurons consisted of combinations of activity related to planning upcoming eye movements, as well as activity that was sensitive to salient visual features of the image. In parallel with the development of our understanding of how the brain controls eye movements, there have been substantial advances in our understanding of the features of natural images that guide both human and monkey eye movements. These behavioral studies are at the advanced level of being able to accurately predict patterns of eye movements. Our goal in this proposal is to take advantage of these advancements in predicting patterns of eye movements in natural environments to help us understand the brain events that are responsible for this behavior. We will focus upon neuron activity in the FEF due to its essential role in the control of voluntary eye movements. The proposal has 3 Aims each focused upon a different factor that is known to guide eye movements under natural conditions. Salience describes how different a small part of a visual scene is from the remainder of the scene based upon stimulus features such as color, contrast, shape, and orientation. Our first aim will define the effects that salience has upon FEF activity. In our second aim, we'll quantify the effects of relevance. Relevance refers to the importance of visual features for the task at hand; for example, if we're looking for a red target, the red items in the image will be more likely to attract our attention and ultimately be the target for an eye movement. Knowing the broad composition of a scene, a quality that is called scene gist, can tell us the places where an object is more likely to be found. For example, if we are looking for a bicycle, we are more likely to search the sidewalks and roadways of a street scene and ignore other places where bicycles are unlikely to be found. Our final aim will look for the effects of scene gist upon monkey behavior and the FEF activity driving that behavior. In addition to the brain recording experiments outlined above, a large part of our effort will be devoted to mathematical analysis and modeling of the behavioral and neuronal data we obtain. Our ultimate goal is to provide a model that predicts the contributions of salience, relevance, and gist to the activity of FEF neurons. The successful model will be a mathematical representation that predicts search-related activity in the FEF for both artificial and real world conditions.