This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
The NSF funded research project conducted by Anne Sereno and Saumil Patel at the University of Houston will use human behavioral experiments and a mathematical model to examine the brain mechanisms underlying reflexive spatial attention in vision. The information processing capability of the human central nervous system (CNS) is limited by factors such as the number of neurons available in the system as well as the way they are connected to each other. These limitations are overcome by mechanisms that either move and aim the sensory organs towards the source of relevant information in the environment (such as head and eye movements) or "aim" the CNS itself towards the relevant information arriving from these sensory organs ("covert" attention). It is well known that visual spatial attention can occur reflexively and automatically without conscious awareness. However, the details of the neural circuitry involved in reflexive spatial attention are still largely unknown. Further, although much of what is known about the neural mechanisms of visual spatial attention has been learned from studying monkeys, there are few, if any, theoretical models that link neurophysiological data from monkeys to human behavioral data. Sereno and Patel's research will begin to fill this gap by extending a physiologically plausible model of reflexive attention to analyze previously collected neural data from monkeys. Recent work suggests that the parietal areas of the brain, previously thought to be largely devoted to spatial processing, and thus important for spatial attention, are also sensitive to stimulus shape. Thus, an additional goal of this research is to determine the role that object shape plays in reflexive spatial attention.
Understanding the neural circuitry of reflexive spatial attention is an important step in linking sensory signals to perception and behavior. This research will provide a theoretical framework for linking physiological data from monkeys to human behavioral data. In particular, the work proposed here will yield insight into how the brain automatically filters information and guides behavior. Knowledge gained from this project may improve the design and performance of machine-vision systems (such as those used in robotics), particularly their ability to automatically focus attention and thus successfully function in crowded environments. This project will also provide unique training for psychology graduate students, with an emphasis on the application of mathematical tools to understanding the workings of the human nervous system.
