Machine Networks of Attention from Human Networks of Attention

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The addition of 'attention' to machine learning has recently improved many algorithms with the potential to transform a wide range of machine learning approaches (transformers, perceivers). Attention, simply, allows an algorithm to allocate more weight to input that is relevant for certain tasks, and less weight to the irrelevant. We propose that attention mechanisms in machine learning will become increasingly important as the volume of input data increases, and even efficient algorithms will have to make informed choices about which input should receive priority or actively inhibited. We will apply current theories of human attention to improve machine learning algorithms that adjust to the goals of the agent. Biological attention has been studied for more than 100 years and comprises multiple overlapping networks that help an organism allocate neural processing to sensory input that is important to a given task. For example, the orienting network uses eye movements and shifts of spatial attention to inspect important areas of our environment. The executive control network adjusts sensory priority for our evolving goals. We will use high quality eye tracking data in various tasks as a proxy for human attention and use these data to inform novel attentional mechanisms for machine learning. The key objectives of the research will be to improve existing attention mechanisms in machine learning algorithms such that they are better able to prioritize input data for a given task. The resulting improved efficiency will reduce resources needed for existing tasks and and improve the scope of these algorithms for more computationally expensive tasks. By explicitly testing mechanisms that we know from human visual processing, we will gain a better understanding of how machine and human attention might work together in joint tasks.