CompCog: Noisy-channel processing in human language understanding

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Every day we understand hundreds of sentences that we have never encountered and we produce hundreds more. This success is remarkable given the noisy environments in which language takes place, the errors speakers make, and limitations of our memory and attention. The present project develops and tests a theory of robust language understanding. The investigators combine tools of information theory, natural language processing, linguistics, and experimental psychology to provide a mathematically formalized model of human language comprehension as probabilistic inference over a “noisy channel”. The project contributes to our basic scientific understanding of human language and the human mind, while strengthening bridges between psycholinguistics and contemporary artificial intelligence research. The work has wide-ranging long-term potential to enhance our understanding of healthy cognitive performance and development in the area of language and to identify and guide treatments for developmental and acquired language disorders. In this program of research, the investigators develop a computationally and algorithmically precise theory of how human understanding of sentences unfolds moment-by-moment. This incremental noisy-channel theory is implemented using state-of-the-art symbolic and neural network-based approaches to modeling language from artificial intelligence and natural language processing. A key component includes an account of how the distributional statistics of language shape noisy memory representations used during real-time language processing. Distinctive empirical predictions regarding robustness to errors in the linguistic input and regarding when and how the proposed mechanisms influence comprehension, allow this approach to be evaluated relative to alternative psycholinguistic theories. The predictions are tested using controlled behavioral experiments on how native speakers process and interpret linguistic input.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.