Welcome to Gary Lupyan’s Lab at University of Wisconsin-Madison.
My main interests revolve around the interaction between language and other cognitive processes: How does language change the way we categorize and perceive the world? What non-communicative aspects of human behavior are impaired in cases of acquired language deficits such as aphasia? What types of thinking depend most on language? Asked another way: what aspects of human cognition were made possible or improved by the evolution of language?
An additional interest is trying to understand why language is the way it is by analyzing sociolinguistic factors. For example, how do languages vary as a function of whether they are used by a diverse population that includes adult learners of the language, or in a tightly-knit small group in which the language is acquired exclusively as a native language by infants. A discussion of this work appears in a recent issue of the New Scientist. I’m a member of the Crow Institute for the Study of Evolution.
I also have broad interests in neural coding, particularly the ways in which reentrant (or recurrent) neural processing gives rise to mental representations that are stable enough to persist in time yet flexible enough to be dynamically modulated by current context and task demands.
If you are interested in our research and the psychology program at UW-Madison, please email Gary Lupyan – lupyan at wisc dot edu
Some of my and my colleagues’ work on the subject of language and thought has been profiled in the New York Times: “When Language can Hold the Answer (NYT link) | (local link). and The New Scientist (local link)
Along with Rick Dale, I published an article in PLoS ONE, showing that the socio-demographic environment within which a language is learned and used may constrain the structure of the language (specifically, morphological encoding). It has been covered in some blogs and such, and in the Economist., and a new article in the New Scientist (local link). See also our new paper (Dale & Lupyan, in press) which uses an agent-based modeling approach along with some data from Google N-grams and Mechanical Turk to explore some of the predictions of the Linguistic Niche Hypothesis.
For inquiries, reprints, and whatever else, email us