A team of statisticians from the University of Texas at Austin has received the top award in the field, a Mitchell Prize.
The team included Giorgio Paulon and Dr. Abhra Sarkar, who did a statistical research study modeling what happens in the brains of non-native English speakers while working to learn another language’s tonal differentiations as compared to the native language, a UT statement said.
For Sarkar, this is the second time he has been award the Michell Prize since 2018 as an assistance professor for UT Austin’s Department of Statistics and Data Sciences when he was honored for developing a new statistical model that analyzed animal vocalization through Bayesian analysis, a method that answers research questions using probability, the Daily Texan reported.
The duo studied 20 English speakers as they learned how to differentiate the tonal variations that give syllables different meanings in Mandarin. The study found that there are significant tonal difference in the language of Mandarin that does not exist in other languages, which makes Mandarin the hardest language to learn. In discovering this, Sarkar, Paulon and their colleagues set out to discover how the brain in turn rewires itself when learning new languages, the statement said.
“This is an ambitious goal, but this could help eventually develop precision learning strategies for different people depending on how their individual brains work,” Sarkar said in the statement.
According to the release, the project of tonal study of language learning began in 2018 with the help of Joined by Fernando Llanos and Bharath Chandrasekaran, Sarkar and Paulon, and all four were later selected for their 2020 paper, published in the Journal of the American Statistical Association in September.
Named after a senior research staff member at Oak Ridge National Laboratory, the Mitchell Prize recognizes “outstanding paper that describes how a Bayesian analysis has solved an important applied problem.”
