Computational Techniques for Exploring Language in the Brain

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People want intuitive interactions with their computers, tablets and smartphones; increasingly, they want to ask their devices to perform tasks, not tap and click commands. Apple's Siri and Microsoft's Cortana are two recent attempts to realize this goal. We can ask Siri to set an alarm, to place a call to our mother, and many other everyday tasks. However, when it comes to more complex requests requiring a deeper understanding of natural language (e.g. “Call me an ambulance”), computers fall short, leaving the consumer disappointed. In the long term, we are in need of computer programs that can understand language as humans understand it. This is the overarching goal of my research program: to push the boundaries of computational natural language understanding. Specifically, I will use brain images to study language “in vivo”. To improve language understanding in computers, I will collect brain images while people read various segments of text. The purpose of these experiments will be to study how people combine words to produce complex meaning that is more than the “sum of the parts”. For example, when people read the words “happy accident”, the positive and negative word do not average into a neutral phrase; instead, the phrase itself is also positive. How does the brain process words, with distinct meanings, to create a unified meaning? I will study this process, called semantic composition, at the phrase, sentence and paragraph level. When people read words in isolation (as has been extensively studied with brain imaging), the onset of the language-related cognitive processes is predictable and stable. However, as words are combined to make more complex language, the cognitive load increases, causing the onset of language-related processes to be less "time-locked" to stimuli (word) onset. We need new machine learning methods that can handle such variation in brain images. Brain image datasets are small but of high dimension, which makes it difficult to build robust models. However, there will be many times more data available if we can combine data across people. I will create new machine learning algorithms that use brain images from different people to build up a "dictionary" of known patterns. I will adapt the dictionaries to specific people, using correlations in brain activity as a guide. Detecting and describing the more complex aspects of language understanding is crucial for building a coherent picture of how people represent and manipulate meaning. My research will inform new algorithms for computational language understanding, as well as provide solutions to some of the problems inherent in multi-subject brain imaging datasets. The impact of my research will reach beyond language in the healthy brain, and improve the diagnosis and treatment of a multitude of brain disorders, including language disorders, sleep disorders and even forms of dementia.