Language can be used to move thoughts between minds, even those separated by considerable distance or time. The speaker takes a thought, packages it up into a series of sounds (or gestures), from which the listener must recover the original thought. This alone would be an impressive feat difficult for science to explain. We must also explain how children learn these procedures. Our research considers all aspects of this problem.
Methodologically, the lab uses a number of well-established methods, such as judgment studies, eye-tracking, and ERPs. However, we are particularly interested in exploring and exploiting new and emerging methods. One such method is testing tens or even hundreds of thousands of children and adults over the Internet, and in doing so being able to study questions that were previously out of reach (for instance, see this paper). This work involves both viral quizzes that recruit large numbers of subjects and also citizen science projects, in which volunteer, amateur researchers help analyze linguistic phenomena.
We currently have three major lines of research:
Critical periods in language: Questions about the existence and nature of critical periods for language have been a driving force in cognitive science ... and also very hard to answer. We have been applying a combination of Big Data and machine learning approaches in order to make headway. For instance, by applying a novel statistical model to a dataset of nearly 700,000 grammar tests, we estimated that the ability to learn grammar begins to decline at 17-18 years old -- far later than previously supposed. In another line of work, we have been applying deep learning to understand how the sentence structures of non-native speakers differ from those of native speakers.
The relationship between syntax and semantics: Most theories of language acquisition (syntactic bootstrapping, semantic bootstrapping, constructivism, etc.) rely heavily on an assumed relationship between syntax and semantics: that the meaning of a sentence governs -- or at least heavily influence -- its syntax. However, different theories make very different claims about what this relationship looks like. We are trying to clarify the relationship between syntax and semantics in order to distinguish between these theories. Much of this work involves studying vocabulary development for telltale signatures of different theories, as in this study of pscyh verbs. In a related line of work, we have been crowdsourcing the linguistic analysis of verb syntax and semantics.
Neural computation of language: One of our long-standing goals is to use evidence about how language is organized in the brain to constrain theories of language and language acquisition. This requires fairly fine-grained information about how information is stored and processed in the brain. In collaboration with Stefano Anzellotti, we are currently applying both Bayesian modeling and deep learning to this problem. This is a very new line of work, so there are no results yet to report. Stay tuned!