Language

MIT researchers have found that lawyers prefer, and better understand, simplified texts, rather than legalese, reports Jesse Greenspan for Scientific American. “The researchers presented 105 U.S. attorneys with contract excerpts written in both “legalese” and plain English and tested their comprehension and recall for each,” explains Greenspan. “While the attorneys outperformed laypeople overall, they still found the legalese contracts harder to grasp than those written in plain English.”

Researchers at MIT have found that lawyers “have an easier time remembering legal documents written in simple English over those filled with so-called legalese,” reports Ed Cara for Gizmodo. “On average, for instance, lawyers scored 45% on a test that asked them to recall documents written in legalese, compared to the average 38% scored by nonlawyers,” explains Cara. “But the lawyers’ score also increased to over 50% when they were given the simplified version.”  

Prof. Edward Flemming speaks with Boston.com reporter Ross Cristantiello about the origins of the Boston accent. Flemming says the “'softening' and eventual dropping of “R” sounds appears to have spread from the south of England through ports up and down the eastern coast of America, influencing the accents found in cities like Charleston and New York City.”

Researchers from MIT and elsewhere have studied the mind of polyglots and uncovered how language-specific regions of the brain respond to different and familiar languages, reports Natalia Mesa for Science. The researchers found that “the activity in the brain’s language network fluctuated based on how well participants understood a language. The more familiar the language, the larger the response,” writes Mesa. “There was one exception to the rule: when participants heard their native tongue, their language networks were actually quieter than when they heard other familiar languages.”

Researchers from the MIT-IBM Watson AI Lab and the Harvard Natural Language Processing Group developed the Giant Language model Test Room (GLTR), an algorithm that attempts to detect if text was written by a bot, reports Megan Morrone for Fast Company. “Using the ‘it takes one to know one’ method, if the GLTR algorithm can predict the next word in a sentence, then it will assume that sentence has been written by a bot,” explains Morrone.

Prof. Evelina Fedorenko speaks with Atlantic reporter Matteo Wong about her research exploring how “the brain behaves when an individual speaks different languages.” Fedorenko explains that “it seems like languages provide us with mappings between forms and meanings.”

Scientists at MIT have found that specific neurons in the human brain light up whenever we see images of food, reports Dr. Mallika Marshall for CBS Boston. “The researchers now want to explore how people’s responses to certain foods might differ depending on their personal preferences, likes and dislikes and past experiences,” Marshall.

Writing for The New York Times, Prof. Michel DeGraff details how the education system in Haiti discriminates against Kreyòl, forcing children to speak and learn in French, “a legacy of the French colonial design for Haiti’s impoverishment, which continues, centuries later, to drain us as a nation.” DeGraff adds: “Unshackling Haitian minds and society from centuries of linguistic discrimination is the first step to help Haiti overcome the disastrous consequences of its colonial and neocolonial history.”

Researchers at MIT have discovered that pictures of food appear to stimulate strong reactions among specific sets of neurons in the human brain, a trait that could have evolved due to the importance of food for humans, reports Sascha Pare for The Guardian. “The researchers posit these neurons have gone undetected because they are spread across the other specialized cluster for faces, places, bodies and words, rather than concentrated in one region,” writes Pare.

Prof. Evelina Fedorenko is studying a woman whose brain does not have a left temporal lobe in an effort to understand how the brain regions thought to play a role in language learning and comprehension develop. “Because EG’s left temporal lobe is missing, Fedorenko’s team had a chance to answer an interesting question: are the temporal regions a prerequisite for setting up the frontal language areas?” writes Grace Browne for Wired. 

Sculptor Matthew Angelo Harrison and artist Raymond Boisjoly will both have art installations on display at the MIT List Visual Arts Center this upcoming spring, reports Pamela Reynolds for WBUR. Reynolds notes that Boisjoly’s “latest work continues the artist’s practice of working with text, photography and images in consideration of how language, culture and ideas can be framed and transmitted.” Harrison, “has frozen union organizing artifacts into chunks of resin,” writes Reynolds. 

Inc. columnist Justin Bariso spotlights the late Prof. Patrick Winston’s IAP course “How to Speak,” which was aimed at helping people improve their communications skills while also underscoring the important role engagement plays in becoming a better listener. Some people ask why [no laptops, no cellphones] is a rule of engagement," said Winston. "The answer is, we humans only have one language processor. And if your language processor is engaged ... you're distracted. And, worse yet, you distract all of the people around you. Studies have shown that."

Axios reporter Alison Snyder writes that a new study by MIT researchers demonstrates how AI algorithms could provide insight into the human brain’s processing abilities. The researchers found “Predicting the next word someone might say — like AI algorithms now do when you search the internet or text a friend — may be a key part of the human brain's ability to process language,” writes Snyder.

Using an integrative modeling technique, MIT researchers compared dozens of machine learning algorithms to brain scans as part of an effort to better understand how the brain processes language. The researchers found that “neural networks and computational science might, in fact, be critical tools in providing insight into the great mystery of how the brain processes information of all kinds,” writes Anna Blaustein for Scientific American.

MIT researchers have developed an AI-enabled machine known as DeepRole that can beat human players in an online multiplayer game where each player’s true motives and roles are kept secret from one another. This “is the first gaming bot that can win online multiplayer games in which the participants' team allegiances are initially unclear,” reports Xinhua.