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

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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?