This INSPIRE award is partially funded by the Linguistics Program and the Perception, Action & Cognition Program in the Division of Behavioral and Cognitive Sciences in the Directorate for Social, Behavioral & Economic Sciences; by the Robust Intelligence Program in the Division of Information & Intelligent Systems in the Directorate for Computer & Information Science & Engineering; and by the Algorithmic Foundations Program in the Division of Computer and Network Systems in the Directorate for Computer & Information Science & Engineering.

Discrete, combinatorial systems of structured symbols permeate human cognition in domains such as language, motor control, complex action planning, learning, and higher-level vision. Nonetheless, the computational apparatus that the brain exploits is based on continuous, activation-based propagation of information through complex networks of neurons. A fundamental problem of the cognitive sciences is how to integrate gradient, continuous neural computation with the discrete combinatorial dimension of cognition. The solution to this puzzle will provide a deeper understanding of the mind and may also serve as the basis of a new generation of computing systems capable of authentically brain-like behavior.

Under the direction of Dr. Smolensky, the research team will develop an approach to this puzzle by exploring and testing the predictions of their theory of Gradient Symbolic Computation (GSC) in the domain of language. Their efforts will include the development of the formal, mathematical foundations of GSC. In parallel, the PIs will develop a framework for modeling Gradient Symbolic Processing. To that end, the PIs will use computational modeling and experimental psycholinguistic studies of phenomena that typify the morpho-phonological, syntactic, and semantic characteristics of language and language processing.

The broader impacts of the work include the potential to transform general computing for future approaches to computer design, to provide innovations in computer language processing, and to empower major advances in our understanding of human language, its impairment in disease, and its learning and remediation. The project also strongly engages STEM education. Undergraduate, graduate, and post-doctoral researchers will all play key roles in highly interdisciplinary STEM research integrating experimental, theoretical, and computational methods. The new type of computation created will provide an integrative framework for developing courses bridging computation theory, psychology, and linguistics. Pedagogical materials developed in these courses will be made publicly available to facilitate undergraduate and graduate program development at other institutions.

National Science Foundation (NSF)
Division of Behavioral and Cognitive Sciences (BCS)
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Joan Maling
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Johns Hopkins University
United States
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