Stephan Meylan
I am a computational cognitive scientist who studies how children learn language, both to support the development of machine intelligence and to understand humans' remarkable cognitive capacities. As a research scientist at MIT's Department of Brain and Cognitive Sciences, my work seeks to reverse engineer the cognitive representations and learning mechanisms underlying first language development. My work uses ideas and methods from cognitive science, natural language processing, psycholinguistics, robotics, and child development to reimagine children's early communicative development as increasingly sophisticated multi-agent action coordination. This line of research builds on doctoral work looking at the role of probabilistic inference in language learning and language processing (as characterized with Bayesian and neural network models), as well as the traces left in language structures by the need for efficient inference at multiple timescales. More broadly, I am interested in the evolutionary and ecological significance of language, especially how it enables cultural transmission of knowledge and cooperative problem solving. My research has been funded by the (U.S.) National Science Foundation, the Air Force Office of Scientific Research, DARPA, the National Institutes of Health, and the Simons Foundation.