CompCog: Bridging the gap between behavioral and neural correlates of attention using a computational model of neural mechanisms

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A challenge for our visual system is being able to focus on information important for our current task, while also being responsive to unexpected events. For example, when driving, if one is looking carefully for a street sign, one should also be able to stop to avoid a car pulling out from a side street. To address this challenge, our brain uses an attentional system that monitors the environment for important information and decides what should be focused at each moment in time. The proposed research will investigate this attentional system, using experiments that examine behaviors and brain activity, as well as with a computer model that integrates other findings from many different labs. This model will help researchers to better understand how brainwaves are related to both the activity of brain cells and behavior when visual attention is engaged. The model will be made publicly available in a format that anyone can run on their own computer, so that other researchers can use it to help advance our understanding in many important areas, such as how cars and other automated systems will be able to navigate and interact safely with the world. Finally, this work supports educational efforts aimed at engaging undergraduate students with a highly technical approach to scientific investigation and funds outreach efforts in high schools to give students the opportunity to develop a greater interest in neuroscience and robotics.This work builds on decades of research into the nature of visual attention, which has generated a large volume of data about how the brain processes information. Based on this work, the researchers have created a computational model of attention that attempts to simulate how the brain chooses which pieces of information to attend. To validate this model, they will conduct a series of eight new experiments that test specific predictions of the model. These tests will provide information about how the different layers of neurons in the model should communicate to most closely approximate the human brain's attentional system. The final model will be compiled into a version that can be downloaded by other researchers or educators. This model will provide a polished graphical user interface, allowing novice users to explore how the simulated attention system works, and how brainwaves are generated. A further objective will be to develop a new kind of experiment that tests the delay between vision and attention. The data from this paradigm will give scientists crucial details about how the human visual system temporarily holds information while determining what to do with it.