Deciding Where to Look Next: Frontal Eye Field's Role during Natural Viewing

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 DESCRIPTION (provided by applicant): The long-term goal of this research is to understand how the brain decides where we look in the real world. Many factors influence our eye movements (saccades). For instance, we are more likely to look at salient objects (i.e. those that are conspicuous), such as a bright red balloon in a blue sky. We are also more likely to look at goal-dependent objects (i.e. those that share features with our goals), such as a yellow object when searching for a banana. For several decades, researchers in computer vision have been developing models based on these factors to predict the locations to which we move our eyes. Researchers in neurobiology have also been studying saccade selection, and have suggested the frontal eye field (FEF) plays a large role, as the FEF encodes both visual features and eye movements. But because the FEF encodes both visual features and saccades, it is very difficult to parse FEF activity during natural viewing. For this reason, past experiments have primarily investigated the FEF using simple, constrained tasks with artificial stimuli. In this project, I wil use images of natural scenes, which better approximate the complexity of the real world. I will record with extracellular electrodes from the FEF of awake, behaving rhesus monkeys, while they view natural scenes. In order to determine the FEF's role in the decision of where to saccade next in natural scenes, I will investigate how the FEF encodes visual features that predict saccades. In my two aims, I will test how the FEF encodes salience (Aim 1) and goal-dependence (Aim 2). I will build a model that explains neural activity using visual features (salience and goal-dependence) along with eye movements, which are a confounding source of neural activity. This model will take advantage of computer vision and machine learning algorithms in order to look at the effects of large numbers of correlates and visual features in these natural scenes. The neural data analysis methods developed for these aims will allow researchers that study many brain areas to more easily use natural scenes. Additionally, understanding how the brain chooses where to saccade in natural scenes have important consequences for neurologic and psychiatric health and disease. Several diseases including schizophrenia, autism, and Parkinson's impair the choice of saccades. A better understanding of the link between visual features, eye movements, and FEF activity promises to increase understanding of these diseases and allow the development of novel diagnostic tools.