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

CompCog：使用神经机制的计算模型弥合注意力的行为和神经相关性之间的差距

基本信息

批准号：
1734220
负责人：
Bradley Wyble
金额：
$ 39.07万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734220&HistoricalAwards=false
关键词：
CompCog Bridging gap between behavioral

项目摘要

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.

对视觉系统的挑战是能够专注于对我们当前任务重要的信息，同时也对意外事件做出响应。例如，开车时，如果人们仔细寻找路标，也应该能够停下来，以避免从小街驶出的汽车。为了应对这一挑战，我们的大脑使用了一个注意力系统，该系统可以监视环境以获取重要信息，并决定应集中在每时每刻应集中的内容。拟议的研究将使用检查行为和大脑活动的实验以及计算机模型来整合来自许多不同实验室的其他发现的计算机模型。该模型将帮助研究人员更好地了解脑电波如何与脑细胞的活性和视觉关注时的行为相关。该模型将以任何人都可以在自己的计算机上运行的格式公开提供，以便其他研究人员可以使用它来帮助我们在许多重要领域的理解，例如汽车和其他自动化系统如何能够与世界安全地进行导航和互动。最后，这项工作支持旨在吸引本科生采用高科技研究的教育工作，并为高中提供资金宣传工作，使学生有机会对神经科学和机器人技术产生更大的兴趣。这项工作基于数十年来研究视觉注意力的性质，这为大脑对大脑的信息进行了大量数据，有关大脑如何处理信息。基于这项工作，研究人员创建了一种注意力的计算模型，该模型试图模拟大脑选择要参加的信息。为了验证该模型，他们将进行一系列八个新实验，以测试模型的特定预测。这些测试将提供有关模型中不同层的神经元如何通信以最紧密近似人脑注意力系统的信息。最终模型将汇编为可以由其他研究人员或教育工作者下载的版本。该模型将提供一个抛光的图形用户界面，使新手用户可以探索模拟的注意系统的工作原理以及如何生成脑波。一个进一步的目标是开发一种新的实验，以测试视力和注意力之间的延迟。来自该范式的数据将为科学家提供有关人类视觉系统如何暂时保存信息的重要细节，同时确定该如何处理该信息。