The Dynamics of Top-Down Attention: Predictive Coding Loops for Auditory Selective Attention

自上而下注意力的动态：听觉选择性注意力的预测编码循环

• 批准号: RGPIN-2018-05976
• 负责人: Tata, Matthew
• 金额: $ 2.11万
• 依托单位: University of Lethbridge
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=666566
• 关键词: Dynamics; Top; Down; Attention; Predictive

In 1980, the eminent vision scientist Richard Gregory articulated a remarkable idea about human perception. He said that perceiving is not the direct result of our sensory systems sending information into our brains. Instead, perception is a mental model of the world, and our brains merely check with our senses to make sure the model isn't producing errors.***Underlying this idea is the concept of Bayesian inference: that the best way to perceive something is to accumulate and integrate statistical evidence about it over time. Although Gregory's proposal seems counterintuitive and contrary to our experience, it closely matches emerging evidence about how both biological and artificial systems can perceive the world. Recent dramatic advances in machine learning use this idea in neural networks that can perceive speech and understand video, while neuroscientists are finding evidence that the human brain uses the same kind of fundamental computation in a process called predictive coding.***Predictive coding is probably especially important for auditory perception because the sounds reaching our ears are usually mixtures of signals from many different sources. The sound we want to hear needs to be “unmixed” and predictive coding offers a theory about how the brain might do this. The research proposed here takes three approaches to study our ability to selectively listen to one sound even when there is lots of distraction: 1) a computational neuroscience program to develop and test predictive coding neural networks for hearing; 2) a cognitive neuroscience program using neuroimaging to understand how the brain implements selective attention by predictive coding; and 3) a cognitive robotics program that translates these ideas into software to make robots that can respond to speech commands in noisy real-world environments.***This work will have both immediate and long-term benefits for Canada. By studying and better understanding the fundamental computations of the human brain, we can invent better technologies for a wide range of applications such as robotics and medical devices. We can also begin to understand one of the most profound mysteries of science: how does the biology of the brain generate the percepts of the mind?

1980年，杰出的视觉科学家理查德·格雷戈里（Richard Gregory）阐述了一个关于人类感知的非凡观点。他说，感知并不是我们的感觉系统向大脑发送信息的直接结果。相反，感知是对世界的一种心理模型，我们的大脑只是用我们的感官来检查，以确保这个模型不会产生错误。这个想法的基础是贝叶斯推理的概念：感知事物的最佳方式是随着时间的推移积累和整合有关它的统计证据。虽然格雷戈里的建议似乎有悖直觉，与我们的经验相反，但它与生物和人工系统如何感知世界的新证据密切相关。最近，机器学习领域取得了巨大进展，将这一想法应用于能够感知语音和理解视频的神经网络，而神经科学家正在寻找证据，证明人类大脑在一种称为预测编码的过程中使用了同样的基本计算。预测编码可能对听觉感知特别重要，因为到达我们耳朵的声音通常是来自许多不同来源的信号的混合。我们想要听到的声音需要“纯净”，而预测编码提供了一种关于大脑如何做到这一点的理论。这里提出的研究采用了三种方法来研究我们在有很多分心的情况下选择性倾听一种声音的能力：1)一个计算神经科学项目，开发和测试听觉的预测编码神经网络；2)认知神经科学项目，利用神经成像来了解大脑如何通过预测编码实现选择性注意；3)一个认知机器人程序，将这些想法转化为软件，使机器人能够在嘈杂的现实环境中对语音命令做出反应。这项工作将给加拿大带来眼前和长远的好处。通过研究和更好地理解人类大脑的基本计算，我们可以为广泛的应用发明更好的技术，如机器人和医疗设备。我们也可以开始理解科学中最深奥的奥秘之一：大脑的生物学是如何产生心灵的感知的？