Author: MIT News

Re-imagining our theories of language

by Maura R. O'Connor | Department of Brain and Cognitive Sciences |

Over a decade ago, the neuroscientist Ev Fedorenko asked 48 English speakers to complete tasks like reading sentences, recalling information, solving math problems, and listening to music. As they did this, she scanned their brains using functional magnetic resonance imaging to see which circuits were activated. If, as linguists have proposed for decades, language is connected to thought in the human brain, then the language processing regions would be activated even during nonlinguistic tasks.

Fedorenko’s experiment, published in 2011 in the Proceedings of the National Academy of Sciences, showed that when it comes to arithmetic, musical processing, general working memory, and other nonlinguistic tasks, language regions of the human brain showed no response. Contrary to what many linguistists have claimed, complex thought and language are separate things. One does not require the other. “We have this highly specialized place in the brain that doesn’t respond to other activities,” says Fedorenko, who is an associate professor at the Department of Brain and Cognitive Sciences (BCS) and the McGovern Institute for Brain Research. “It’s not true that thought critically needs language.”

The design of the experiment, using neuroscience to understand how language works, how it evolved, and its relation to other cognitive functions, is at the heart of Fedorenko’s research. She is part of a unique intellectual triad at MIT’s Department of BCS, along with her colleagues Roger Levy and Ted Gibson. (Gibson and Fedorenko have been married since 2007). Together they have engaged in a years-long collaboration and built a significant body of research focused on some of the biggest questions in linguistics and human cognition. While working in three independent labs — EvLab, TedLab, and the Computational Psycholinguistics Lab — the researchers are motivated by a shared fascination with the human mind and how language works in the brain. “We have a great deal of interaction and collaboration,” says Levy. “It’s a very broadly collaborative, intellectually rich and diverse landscape.”

Using combinations of computational modeling, psycholinguistic experimentation, behavioral data, brain imaging, and large naturalistic language datasets, the researchers also share an answer to a fundamental question: What is the purpose of language? Of all the possible answers to why we have language, perhaps the simplest and most obvious is communication. “Believe it or not,” says Ted Gibson, “that is not the standard answer.”

Gibson first came to MIT in 1993 and joined the faculty of the Linguistics Department in 1997. Recalling the experience today, he describes it as frustrating. The field of linguistics at that time was dominated by the ideas of Noam Chomsky, one of the founders of MIT’s Graduate Program in Linguistics, who has been called the father of modern linguistics. Chomsky’s “nativist” theories of language posited that the purpose of language is the articulation of thought and that language capacity is built-in in advance of any learning. But Gibson, with his training in math and computer science, felt that researchers didn’t satisfyingly test these ideas. He believed that finding the answer to many outstanding questions about language required quantitative research, a departure from standard linguistic methodology. “There’s no reason to rely only on you and your friends, which is how linguistics has worked,” Gibson says. “The data you can get can be much broader if you crowdsource lots of people using experimental methods.” Chomsky’s ascendancy in linguistics presented Gibson with what he saw as a challenge and an opportunity. “I felt like I had to figure it out in detail and see if there was truth in these claims,” he says.

Three decades after he first joined MIT, Gibson believes that the collaborative research at BCS is persuasive and provocative, pointing to new ways of thinking about human culture and cognition. “Now we’re at a stage where it is not just arguments against. We have a lot of positive stuff saying what language is,” he explains. Levy adds: “I would say all three of us are of the view that communication plays a very import role in language learning and processing, but also in the structure of language itself.”

Levy points out that the three researchers completed PhDs in different subjects: Fedorenko in neuroscience, Gibson in computer science, Levy in linguistics. Yet for years before their paths finally converged at MIT, their shared interests in quantitative linguistic research led them to follow each other’s work closely and be influenced by it. The first collaboration between the three was in 2005 and focused on language processing in Russian relative clauses. Around that time, Gibson recalls, Levy was presenting what he describes as “lovely work” that was instrumental in helping him to understand the links between language structure and communication. “Communicative pressures drive the structures,” says Gibson. “Roger was crucial for that. He was the one helping me think about those things a long time ago.”

Levy’s lab is focused on the intersection of artificial intelligence, linguistics, and psychology, using natural language processing tools. “I try to use the tools that are afforded by mathematical and computer science approaches to language to formalize scientific hypotheses about language and the human mind and test those hypotheses,” he says.

Levy points to ongoing research between him and Gibson focused on language comprehension as an example of the benefits of collaboration. “One of the big questions is: When language understanding fails, why does it fail?” Together, the researchers have applied the concept of a “noisy channel,” first developed by the information theorist Claude Shannon in the 1950s, which says that information or messages are corrupted in transmission. “Language understanding unfolds over time, involving an ongoing integration of the past with the present,” says Levy. “Memory itself is an imperfect channel conveying the past from our brain a moment ago to our brain now in order to support successful language understanding.” Indeed, the richness of our linguistic environment, the experience of hundreds of millions of words by adulthood, may create a kind of statistical knowledge guiding our expectations, beliefs, predictions, and interpretations of linguistic meaning. “Statistical knowledge of language actually interacts with the constraints of our memory,” says Levy. “Our experience shapes our memory for language itself.”

All three researchers say they share the belief that by following the evidence, they will eventually discover an even bigger and more complete story about language. “That’s how science goes,” says Fedorenko. “Ted trained me, along with Nancy Kanwisher, and both Ted and Roger are very data-driven. If the data is not giving you the answer you thought, you don’t just keep pushing your story. You think of new hypotheses. Almost everything I have done has been like that.” At times, Fedorenko’s research into parts of the brain’s language system has surprised her and forced her to abandon her hypotheses. “In a certain project I came in with a prior idea that there would be some separation between parts that cared about combinatorics versus words meanings,” she says, “but every little bit of the language system is sensitive to both. At some point, I was like, this is what the data is telling us, and we have to roll with it.”

The researchers’ work pointing to communication as the constitutive purpose of language opens new possibilities for probing and studying non-human language. The standard claim is that human language has a drastically more extensive lexicon than animals, which have no grammar. “But many times, we don’t even know what other species are communicating,” says Gibson. “We say they can’t communicate, but we don’t know. We don’t speak their language.” Fedorenko hopes that more opportunities to make cross-species linguistic comparisons will open up. “Understanding where things are similar and where things diverge would be super useful,” she says.

Meanwhile, the potential applications of language research are far-reaching. One of Levy’s current research projects focuses on how people read and use machine learning algorithms informed by the psychology of eye movements to develop proficiency tests. By tracking the eye movements of people who speak English as a second language while they read texts in English, Levy can predict how good they are at English, an approach that could one day replace the Test of English as a Foreign Language. “It’s an implicit measure of language rather than a much more game-able test,” he says.

The researchers agree that some of the most exciting opportunities in the neuroscience of language lies with large language models that provide new opportunities for asking new questions and making new discoveries. “In the neuroscience of language, the kind of stories that we’ve been able to tell about how the brain does language were limited to verbal, descriptive hypotheses,” says Fedorenko. Computationally implemented models are now amazingly good at language and show some degree of alignment to the brain, she adds. Now, researchers can ask questions such as: what are the actual computations that cells are doing to get meaning from strings of words? “You can now use these models as tools to get insights into how humans might be processing language,” she says. “And you can take the models apart in ways you can’t take apart the brain.”

Fourteen MIT School of Science professors receive tenure for 2022 and 2023

Anne Trafton |

In 2022, nine MIT faculty were granted tenure in the School of Science:

Gloria Choi examines the interaction of the immune system with the brain and the effects of that interaction on neurodevelopment, behavior, and mood. She also studies how social behaviors are regulated according to sensory stimuli, context, internal state, and physiological status, and how these factors modulate neural circuit function via a combinatorial code of classic neuromodulators and immune-derived cytokines. Choi joined the Department of Brain and Cognitive Sciences after a postdoc at Columbia University. She received her bachelor’s degree from the University of California at Berkeley, and her PhD from Caltech. Choi is also an investigator in The Picower Institute for Learning and Memory.

Nikta Fakhri develops experimental tools and conceptual frameworks to uncover laws governing fluctuations, order, and self-organization in active systems. Such frameworks provide powerful insight into dynamics of nonequilibrium living systems across scales, from the emergence of thermodynamic arrow of time to spatiotemporal organization of signaling protein patterns and discovery of odd elasticity. Fakhri joined the Department of Physics in 2015 following a postdoc at University of Göttingen. She completed her undergraduate degree at Sharif University of Technology and her PhD at Rice University.

Geobiologist Greg Fournier uses a combination of molecular phylogeny insights and geologic records to study major events in planetary history, with the hope of furthering our understanding of the co-evolution of life and environment. Recently, his team developed a new technique to analyze multiple gene evolutionary histories and estimated that photosynthesis evolved between 3.4 and 2.9 billion years ago. Fournier joined the Department of Earth, Atmospheric and Planetary Sciences in 2014 after working as a postdoc at the University of Connecticut and as a NASA Postdoctoral Program Fellow in MIT’s Department of Civil and Environmental Engineering. He earned his BA from Dartmouth College in 2001 and his PhD in genetics and genomics from the University of Connecticut in 2009.

Daniel Harlow researches black holes and cosmology, viewed through the lens of quantum gravity and quantum field theory. His work generates new insights into quantum information, quantum field theory, and gravity. Harlow joined the Department of Physics in 2017 following postdocs at Princeton University and Harvard University. He obtained a BA in physics and mathematics from Columbia University in 2006 and a PhD in physics from Stanford University in 2012. He is also a researcher in the Center for Theoretical Physics.

A biophysicist, Gene-Wei Li studies how bacteria optimize the levels of proteins they produce at both mechanistic and systems levels. His lab focuses on design principles of transcription, translation, and RNA maturation. Li joined the Department of Biology in 2015 after completing a postdoc at the University of California at San Francisco. He earned an BS in physics from National Tsinghua University in 2004 and a PhD in physics from Harvard University in 2010.

Michael McDonald focuses on the evolution of galaxies and clusters of galaxies, and the role that environment plays in dictating this evolution. This research involves the discovery and study of the most distant assemblies of galaxies alongside analyses of the complex interplay between gas, galaxies, and black holes in the closest, most massive systems. McDonald joined the Department of Physics and the Kavli Institute for Astrophysics and Space Research in 2015 after three years as a Hubble Fellow, also at MIT. He obtained his BS and MS degrees in physics at Queen’s University, and his PhD in astronomy at the University of Maryland in College Park.

Gabriela Schlau-Cohen combines tools from chemistry, optics, biology, and microscopy to develop new approaches to probe dynamics. Her group focuses on dynamics in membrane proteins, particularly photosynthetic light-harvesting systems that are of interest for sustainable energy applications. Following a postdoc at Stanford University, Schlau-Cohen joined the Department of Chemistry faculty in 2015. She earned a bachelor’s degree in chemical physics from Brown University in 2003 followed by a PhD in chemistry at the University of California at Berkeley.

Phiala Shanahan’s research interests are focused around theoretical nuclear and particle physics. In particular, she works to understand the structure and interactions of hadrons and nuclei from the fundamental degrees of freedom encoded in the Standard Model of particle physics. After a postdoc at MIT and a joint position as an assistant professor at the College of William and Mary and senior staff scientist at the Thomas Jefferson National Accelerator Facility, Shanahan returned to the Department of Physics as faculty in 2018. She obtained her BS from the University of Adelaide in 2012 and her PhD, also from the University of Adelaide, in 2015.

Omer Yilmaz explores the impact of dietary interventions on stem cells, the immune system, and cancer within the intestine. By better understanding how intestinal stem cells adapt to diverse diets, his group hopes to identify and develop new strategies that prevent and reduce the growth of cancers involving the intestinal tract. Yilmaz joined the Department of Biology in 2014 and is now also a member of Koch Institute for Integrative Cancer Research. After receiving his BS from the University of Michigan in 1999 and his PhD and MD from University of Michigan Medical School in 2008, he was a resident in anatomic pathology at Massachusetts General Hospital and Harvard Medical School until 2013.

In 2023, five MIT faculty were granted tenure in the School of Science:

Physicist Riccardo Comin explores the novel phases of matter that can be found in electronic solids with strong interactions, also known as quantum materials. His group employs a combination of synthesis, scattering, and spectroscopy to obtain a comprehensive picture of these emergent phenomena, including superconductivity, (anti)ferromagnetism, spin-density-waves, charge order, ferroelectricity, and orbital order. Comin joined the Department of Physics in 2016 after postdoctoral work at the University of Toronto. He completed his undergraduate studies at the Universita’ degli Studi di Trieste in Italy, where he also obtained a MS in physics in 2009. Later, he pursued doctoral studies at the University of British Columbia, Canada, earning a PhD in 2013.

Netta Engelhardt researches the dynamics of black holes in quantum gravity and uses holography to study the interplay between gravity and quantum information. Her primary focus is on the black hole information paradox, that black holes seem to be destroying information that, according to quantum physics, cannot be destroyed. Engelhardt was a postdoc at Princeton University and a member of the Princeton Gravity Initiative prior to joining the Department of Physics in 2019. She received her BS in physics and mathematics from Brandeis University and her PhD in physics from the University of California at Santa Barbara. Engelhardt is a researcher in the Center for Theoretical Physics and the Black Hole Initiative at Harvard University.

Mark Harnett studies how the biophysical features of individual neurons endow neural circuits with the ability to process information and perform the complex computations that underlie behavior. As part of this work, his lab was the first to describe the physiological properties of human dendrites. He joined the Department of Brain and Cognitive Sciences and the McGovern Institute for Brain Research in 2015. Prior, he was a postdoc at the Howard Hughes Medical Institute’s Janelia Research Campus. He received his BA in biology from Reed College in Portland, Oregon and his PhD in neuroscience from the University of Texas at Austin.

Or Hen investigates quantum chromodynamic effects in the nuclear medium and the interplay between partonic and nucleonic degrees of freedom in nuclei. Specifically, Hen utilizes high-energy scattering of electron, neutrino, photon, proton and ion off atomic nuclei to study short-range correlations: temporal fluctuations of high-density, high-momentum, nucleon clusters in nuclei with important implications for nuclear, particle, atomic, and astrophysics. Hen was an MIT Pappalardo Fellow in the Department of Physics from 2015 to 2017 before joining the faculty in 2017. He received his undergraduate degree in physics and computer engineering from the Hebrew University and earned his PhD in experimental physics at Tel Aviv University.

Sebastian Lourido is interested in learning about the vulnerabilities of parasites in order to develop treatments for infectious diseases and expand our understanding of eukaryotic diversity. His lab studies many important human pathogens, including Toxoplasma gondii, to model features conserved throughout the phylum. Lourido was a Whitehead Fellow at the Whitehead Institute for Biomedical Research until 2017, when he joined the Department of Biology and became a Whitehead Member. He earned his BS from Tulane University in 2004 and his PhD from Washington University in St. Louis in 2012.

Thirty-four community members receive 2023 MIT Excellence Awards, Collier Medal, and Staff Award for Distinction in Service

Anne Trafton |

Twenty-four individuals and one team were awarded MIT Excellence Awards — the highest awards for staff at the Institute — at a well-attended and energetic ceremony the afternoon of June 8 in Kresge Auditorium. In addition to the Excellence Awards, two community members were honored with the Collier Medal and Staff Award for Distinction in Service.

The Excellence Awards, Collier Medal, and Staff Award for Distinction in Service recognize the extraordinary dedication of staff and community members who represent all areas of the Institute, both on campus and at the Lincoln Laboratory.

The Collier Medal honors the memory of Officer Sean Collier, who gave his life protecting and serving the MIT community, and celebrates an individual or group whose actions demonstrate the importance of community. The Staff Award for Distinction in Service, now in its second year, is presented to a staff member whose service to the Institute results in a positive lasting impact on the community.

The 2023 MIT Excellence Award recipients and their award categories are:

  • Sustaining MIT: Erin Genereux; Rachida Kernis; J. Bradley Morrison, and the Tip Box Recycling Team (John R. Collins, Michael A. DeBerio, Normand J. Desrochers III, Mitchell S. Galanek, David M. Pavone, Ryan Samz, Rosario Silvestri, and Lu Zhong);
  • Innovative Solutions: Abram Barrett, Nicole H. W. Henning
  • Bringing Out the Best: Patty Eames, Suzy Maholchic Nelson
  • Serving Our Community: Mahnaz El-Kouedi, Kara Flyg, Timothy J. Meunier, Marie A. Stuppard, Roslyn R. Wesley
  • Embracing Diversity, Equity, and Inclusion: Farrah A. Belizaire
  • Outstanding Contributor: Diane Ballestas, Robert J. Bicchieri, Lindsey Megan Charles, Benoit Desbiolles, Dennis C. Hamel, Heather Anne Holland, Gregory L. Long, Linda Mar, Mary Ellen Sinkus, Sarah E. Willis, and Phyl A. Winn
  • The 2023 Collier Medal recipient was Martin Eric William Nisser, a graduate student fellow in the Department of Electrical Engineering and Computer Science/Computer Science and Artificial Intelligence Laboratory and the School of Engineering/MIT Schwarzman College of Computing.
  • The 2023 recipient of the Staff Award for Distinction in Service was Kimberly A. Haberlin, chief of staff in the Chancellor’s Office.

Presenters included President Sally Kornbluth; Vice President for Human Resources Ramona Allen; Provost Cynthia Barnhart; School of Engineering Dean Anantha Chandrakasan; MIT Police Chief John DiFava and MIT Police Captain Andrew Turco; Institute Community and Equity Officer John Dozier; Lincoln Laboratory Director Eric Evans; and Chancellor Melissa Nobles. As always, an animated and supportive audience with signs, pompoms, and glow bracelets filled the auditorium with cheers for the honorees.

Visit the MIT Human Resources website for more information about the award categories, selection process, recipients, and to view the archive video of the event.

Unraveling connections between the brain and gut

Anne Trafton |

The brain and the digestive tract are in constant communication, relaying signals that help to control feeding and other behaviors. This extensive communication network also influences our mental state and has been implicated in many neurological disorders.

MIT engineers have designed a new technology for probing those connections. Using fibers embedded with a variety of sensors, as well as light sources for optogenetic stimulation, the researchers have shown that they can control neural circuits connecting the gut and the brain, in mice.

In a new study, the researchers demonstrated that they could induce feelings of fullness or reward-seeking behavior in mice by manipulating cells of the intestine. In future work, they hope to explore some of the correlations that have been observed between digestive health and neurological conditions such as autism and Parkinson’s disease.

“The exciting thing here is that we now have technology that can drive gut function and behaviors such as feeding. More importantly, we have the ability to start accessing the crosstalk between the gut and the brain with the millisecond precision of optogenetics, and we can do it in behaving animals,” says Polina Anikeeva, the Matoula S. Salapatas Professor in Materials Science and Engineering, a professor of brain and cognitive sciences, director of the K. Lisa Yang Brain-Body Center, associate director of MIT’s Research Laboratory of Electronics, and a member of MIT’s McGovern Institute for Brain Research.

Portait of MIT scientist Polina Anikeeva
McGovern Institute Associate Investigator Polina Anikeeva in her lab. Photo: Steph Stevens

Anikeeva is the senior author of the new study, which appears today in Nature Biotechnology. The paper’s lead authors are MIT graduate student Atharva Sahasrabudhe, Duke University postdoc Laura Rupprecht, MIT postdoc Sirma Orguc, and former MIT postdoc Tural Khudiyev.

The brain-body connection

Last year, the McGovern Institute launched the K. Lisa Yang Brain-Body Center to study the interplay between the brain and other organs of the body. Research at the center focuses on illuminating how these interactions help to shape behavior and overall health, with a goal of developing future therapies for a variety of diseases.

“There’s continuous, bidirectional crosstalk between the body and the brain,” Anikeeva says. “For a long time, we thought the brain is a tyrant that sends output into the organs and controls everything. But now we know there’s a lot of feedback back into the brain, and this feedback potentially controls some of the functions that we have previously attributed exclusively to the central neural control.”

As part of the center’s work, Anikeeva set out to probe the signals that pass between the brain and the nervous system of the gut, also called the enteric nervous system. Sensory cells in the gut influence hunger and satiety via both the neuronal communication and hormone release.

Untangling those hormonal and neural effects has been difficult because there hasn’t been a good way to rapidly measure the neuronal signals, which occur within milliseconds.

“We needed a device that didn’t exist. So, we decided to make it,” says Atharva Sahasrabudhe.

“To be able to perform gut optogenetics and then measure the effects on brain function and behavior, which requires millisecond precision, we needed a device that didn’t exist. So, we decided to make it,” says Sahasrabudhe, who led the development of the gut and brain probes.

The electronic interface that the researchers designed consists of flexible fibers that can carry out a variety of functions and can be inserted into the organs of interest. To create the fibers, Sahasrabudhe used a technique called thermal drawing, which allowed him to create polymer filaments, about as thin as a human hair, that can be embedded with electrodes and temperature sensors.

The filaments also carry microscale light-emitting devices that can be used to optogenetically stimulate cells, and microfluidic channels that can be used to deliver drugs.

The mechanical properties of the fibers can be tailored for use in different parts of the body. For the brain, the researchers created stiffer fibers that could be threaded deep into the brain. For digestive organs such as the intestine, they designed more delicate rubbery fibers that do not damage the lining of the organs but are still sturdy enough to withstand the harsh environment of the digestive tract.

“To study the interaction between the brain and the body, it is necessary to develop technologies that can interface with organs of interest as well as the brain at the same time, while recording physiological signals with high signal-to-noise ratio,” Sahasrabudhe says. “We also need to be able to selectively stimulate different cell types in both organs in mice so that we can test their behaviors and perform causal analyses of these circuits.”

The fibers are also designed so that they can be controlled wirelessly, using an external control circuit that can be temporarily affixed to the animal during an experiment. This wireless control circuit was developed by Orguc, a Schmidt Science Fellow, and Harrison Allen ’20, MEng ’22, who were co-advised between the Anikeeva lab and the lab of Anantha Chandrakasan, dean of MIT’s School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science.

Driving behavior

Using this interface, the researchers performed a series of experiments to show that they could influence behavior through manipulation of the gut as well as the brain.

First, they used the fibers to deliver optogenetic stimulation to a part of the brain called the ventral tegmental area (VTA), which releases dopamine. They placed mice in a cage with three chambers, and when the mice entered one particular chamber, the researchers activated the dopamine neurons. The resulting dopamine burst made the mice more likely to return to that chamber in search of the dopamine reward.

Then, the researchers tried to see if they could also induce that reward-seeking behavior by influencing the gut. To do that, they used fibers in the gut to release sucrose, which also activated dopamine release in the brain and prompted the animals to seek out the chamber they were in when sucrose was delivered.

Next, working with colleagues from Duke University, the researchers found they could induce the same reward-seeking behavior by skipping the sucrose and optogenetically stimulating nerve endings in the gut that provide input to the vagus nerve, which controls digestion and other bodily functions.

Three scientists holding a fiber in a lab.
Duke University postdoc Laura Rupprecht, MIT graduate student Atharva Sahasrabudhe, and MIT postdoc Sirma Orguc holding their engineered flexible fiber in Polina Anikeeva’s lab at MIT. Photo: Courtesy of the researchers

“Again, we got this place preference behavior that people have previously seen with stimulation in the brain, but now we are not touching the brain. We are just stimulating the gut, and we are observing control of central function from the periphery,” Anikeeva says.

Sahasrabudhe worked closely with Rupprecht, a postdoc in Professor Diego Bohorquez’ group at Duke, to test the fibers’ ability to control feeding behaviors. They found that the devices could optogenetically stimulate cells that produce cholecystokinin, a hormone that promotes satiety. When this hormone release was activated, the animals’ appetites were suppressed, even though they had been fasting for several hours. The researchers also demonstrated a similar effect when they stimulated cells that produce a peptide called PYY, which normally curbs appetite after very rich foods are consumed.

The researchers now plan to use this interface to study neurological conditions that are believed to have a gut-brain connection. For instance, studies have shown that autistic children are far more likely than their peers to be diagnosed with GI dysfunction, while anxiety and irritable bowel syndrome share genetic risks.

“We can now begin asking, are those coincidences, or is there a connection between the gut and the brain? And maybe there is an opportunity for us to tap into those gut-brain circuits to begin managing some of those conditions by manipulating the peripheral circuits in a way that does not directly ‘touch’ the brain and is less invasive,” Anikeeva says.

The research was funded, in part, by the Hock E. Tan and K. Lisa Yang Center for Autism Research and the K. Lisa Yang Brain-Body Center, the National Institute of Neurological Disorders and Stroke, the National Science Foundation (NSF) Center for Materials Science and Engineering, the NSF Center for Neurotechnology, the National Center for Complementary and Integrative Health, a National Institutes of Health Director’s Pioneer Award, the National Institute of Mental Health, and the National Institute of Diabetes and Digestive and Kidney Diseases.

Computational model mimics humans’ ability to predict emotions

Anne Trafton |

When interacting with another person, you likely spend part of your time trying to anticipate how they will feel about what you’re saying or doing. This task requires a cognitive skill called theory of mind, which helps us to infer other people’s beliefs, desires, intentions, and emotions.

MIT neuroscientists have now designed a computational model that can predict other people’s emotions — including joy, gratitude, confusion, regret, and embarrassment — approximating human observers’ social intelligence. The model was designed to predict the emotions of people involved in a situation based on the prisoner’s dilemma, a classic game theory scenario in which two people must decide whether to cooperate with their partner or betray them.

To build the model, the researchers incorporated several factors that have been hypothesized to influence people’s emotional reactions, including that person’s desires, their expectations in a particular situation, and whether anyone was watching their actions.

“These are very common, basic intuitions, and what we said is, we can take that very basic grammar and make a model that will learn to predict emotions from those features,” says Rebecca Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Sean Dae Houlihan PhD ’22, a postdoc at the Neukom Institute for Computational Science at Dartmouth College, is the lead author of the paper, which appears today in Philosophical Transactions A. Other authors include Max Kleiman-Weiner PhD ’18, a postdoc at MIT and Harvard University; Luke Hewitt PhD ’22, a visiting scholar at Stanford University; and Joshua Tenenbaum, a professor of computational cognitive science at MIT and a member of the Center for Brains, Minds, and Machines and MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

Predicting emotions

While a great deal of research has gone into training computer models to infer someone’s emotional state based on their facial expression, that is not the most important aspect of human emotional intelligence, Saxe says. Much more important is the ability to predict someone’s emotional response to events before they occur.

“The most important thing about what it is to understand other people’s emotions is to anticipate what other people will feel before the thing has happened,” she says. “If all of our emotional intelligence was reactive, that would be a catastrophe.”

To try to model how human observers make these predictions, the researchers used scenarios taken from a British game show called “Golden Balls.” On the show, contestants are paired up with a pot of $100,000 at stake. After negotiating with their partner, each contestant decides, secretly, whether to split the pool or try to steal it. If both decide to split, they each receive $50,000. If one splits and one steals, the stealer gets the entire pot. If both try to steal, no one gets anything.

Depending on the outcome, contestants may experience a range of emotions — joy and relief if both contestants split, surprise and fury if one’s opponent steals the pot, and perhaps guilt mingled with excitement if one successfully steals.

To create a computational model that can predict these emotions, the researchers designed three separate modules. The first module is trained to infer a person’s preferences and beliefs based on their action, through a process called inverse planning.

“This is an idea that says if you see just a little bit of somebody’s behavior, you can probabilistically infer things about what they wanted and expected in that situation,” Saxe says.

Using this approach, the first module can predict contestants’ motivations based on their actions in the game. For example, if someone decides to split in an attempt to share the pot, it can be inferred that they also expected the other person to split. If someone decides to steal, they may have expected the other person to steal, and didn’t want to be cheated. Or, they may have expected the other person to split and decided to try to take advantage of them.

The model can also integrate knowledge about specific players, such as the contestant’s occupation, to help it infer the players’ most likely motivation.

The second module compares the outcome of the game with what each player wanted and expected to happen. Then, a third module predicts what emotions the contestants may be feeling, based on the outcome and what was known about their expectations. This third module was trained to predict emotions based on predictions from human observers about how contestants would feel after a particular outcome. The authors emphasize that this is a model of human social intelligence, designed to mimic how observers causally reason about each other’s emotions, not a model of how people actually feel.

“From the data, the model learns that what it means, for example, to feel a lot of joy in this situation, is to get what you wanted, to do it by being fair, and to do it without taking advantage,” Saxe says.

Core intuitions

Once the three modules were up and running, the researchers used them on a new dataset from the game show to determine how the models’ emotion predictions compared with the predictions made by human observers. This model performed much better at that task than any previous model of emotion prediction.

The model’s success stems from its incorporation of key factors that the human brain also uses when predicting how someone else will react to a given situation, Saxe says. Those include computations of how a person will evaluate and emotionally react to a situation, based on their desires and expectations, which relate to not only material gain but also how they are viewed by others.

“Our model has those core intuitions, that the mental states underlying emotion are about what you wanted, what you expected, what happened, and who saw. And what people want is not just stuff. They don’t just want money; they want to be fair, but also not to be the sucker, not to be cheated,” she says.

“The researchers have helped build a deeper understanding of how emotions contribute to determining our actions; and then, by flipping their model around, they explain how we can use people’s actions to infer their underlying emotions. This line of work helps us see emotions not just as ‘feelings’ but as playing a crucial, and subtle, role in human social behavior,” says Nick Chater, a professor of behavioral science at the University of Warwick, who was not involved in the study.

In future work, the researchers hope to adapt the model so that it can perform more general predictions based on situations other than the game-show scenario used in this study. They are also working on creating models that can predict what happened in the game based solely on the expression on the faces of the contestants after the results were announced.

The research was funded by the McGovern Institute; the Paul E. and Lilah Newton Brain Science Award; the Center for Brains, Minds, and Machines; the MIT-IBM Watson AI Lab; and the Multidisciplinary University Research Initiative.

Tackling the MIT campus’s top energy consumers, building by building

Anne Trafton |

When staff in MIT’s Department of Facilities would visualize energy use and carbon-associated emissions by campus buildings, Building 46 always stood out — attributed to its energy intensity, which accounted for 8 percent of MIT’s total campus energy use. This high energy draw was not surprising, as the building is home of the Brain and Cognitive Sciences Complex and a large amount of lab space, but it also made the building a perfect candidate for an energy performance audit to seek out potential energy saving opportunities.

This audit revealed that several energy efficiency updates to the building mechanical systems infrastructure, including optimization of the room-by-room ventilation rates, could result in an estimated 35 percent reduction of energy use, which would in turn lower MIT’s total greenhouse gas emissions by an estimated 2 percent — driving toward the Institute’s 2026 goal of net-zero and 2050 goal of elimination of direct campus emissions.

Building energy efficiency projects are not new for MIT. Since 2010, MIT has been engaged in a partnership agreement with utility company Eversource establishing the Efficiency Forward program, empowering MIT to invest in more than 300 energy conservation projects to date and lowering energy consumption on campus for a total calculated savings of approximately 70 million kilowatt hours and 4.2 million therms. But at 418,000 gross square feet, Building 46 is the first energy efficiency project of its size on the campus.

“We’ve never tackled a whole building like this — it’s the first capital project that is technically an energy project,” explains Siobhan Carr, energy efficiency program manager, who was part of the team overseeing the energy audit and lab ventilation performance assessment in the building. “That gives you an idea of the magnitude and complexity of this.”

The project started with the full building energy assessment and lab ventilation risk audit. “We had a team go through every corner of the building and look at every possible opportunity to save energy,” explains Jessica Parks, senior project manager for systems performance and turnover in campus construction. “One of the biggest issues we saw was that there’s a lot of dry lab spaces which are basically offices, but they’re all getting the same ventilation as if they were a high-intensity lab.” Higher ventilation and more frequent air exchange rates draw more energy. By optimizing for the required ventilation rates, there was an opportunity to save energy in nearly every space in the building.

In addition to the optimized ventilation, the project team will convert fume hoods from constant volume to variable volume and install equipment to help the building systems run more efficiently. The team also identified opportunities to work with labs to implement programs such as fume hood hibernation and unoccupied setbacks for temperature and ventilation. As different spaces in the building have varying needs, the energy retrofit will touch all 1,254 spaces in the building — one by one — to implement the different energy measures to reach that estimated 35 percent reduction in energy use.

Although time-consuming and complex, this room-by-room approach has a big benefit in that it has allowed research to continue in the space largely uninterrupted. With a few exceptions, the occupants of Building 46, which include the Department of Brain and Cognitive Sciences, The McGovern Institute for Brain Research, and The Picower Institute for Learning and Memory, have remained in place for the duration of the project. Partners in the MIT Environment, Health and Safety Office are instrumental to this balance of renovations and keeping the building operational during the optimization efforts and are one of several teams across MIT contributing to building efficiency efforts.

The completion date of the building efficiency project is set for 2024, but Carr says that some of the impact of this ongoing work may soon be seen. “We should start to see savings as we move through the building, and we expect to fully realize all of our projected savings a year after completion,” she says, noting that the length of time is required for a year-over-year perspective to see the full reduction in energy use.

The impact of the project goes far beyond the footprint of Building 46 as it offers insights and spurred actions for future projects — including buildings 76 and 68, the number two and three top energy users on campus. Both buildings recently underwent their own energy audits and lab ventilation performance assessments. The energy efficiency team is now crafting a plan for full-building approaches, much like Building 46. “To date, 46 has presented many learning opportunities, such as how to touch every space in a building while research continues, as well as how to overcome challenges encountered when working on existing systems,” explains Parks. “The good news is that we have developed solutions for those challenges and the teams have been proactively implementing those lessons in our other projects.”

Communication has proven to be another key for these large projects where occupants see the work happening and often play a role in answering questions about their unique space. “People are really engaged, they ask questions about the work, and we ask them about the space they’re in every day,” says Parks. “The Building 46 occupants have been wonderful partners as we worked in all of their spaces, which is paving the way for a successful project.”

The release of Fast Forward in 2021 has also made communications easier, notes Carr, who says the plan helps to frame these projects as part of the big picture — not just a construction interruption. “Fast Forward has brought a visibility into what we’re doing within [MIT] Facilities on these buildings,” she says. “It brings more eyes and ears, and people understand that these projects are happening throughout campus and not just in their own space — we’re all working to reduce energy and to reduce greenhouse gas across campus.”

The Energy Efficiency team will continue to apply that big-picture approach as ongoing building efficiency projects on campus are assessed to reach toward a 10 to 15 percent reduction in energy use and corresponding emissions over the next several years.

Scientists discover how mutations in a language gene produce speech deficits

Anne Trafton |

Mutations of a gene called Foxp2 have been linked to a type of speech disorder called apraxia that makes it difficult to produce sequences of sound. A new study from MIT and National Yang Ming Chiao Tung University sheds light on how this gene controls the ability to produce speech.

In a study of mice, the researchers found that mutations in Foxp2 disrupt the formation of dendrites and neuronal synapses in the brain’s striatum, which plays important roles in the control of movement. Mice with these mutations also showed impairments in their ability to produce the high-frequency sounds that they use to communicate with other mice.

Those malfunctions arise because Foxp2 mutations prevent the proper assembly of motor proteins, which move molecules within cells, the researchers found.

“These mice have abnormal vocalizations, and in the striatum there are many cellular abnormalities,” says Ann Graybiel, an MIT Institute Professor, a member of MIT’s McGovern Institute for Brain Research, and an author of the paper. “This was an exciting finding. Who would have thought that a speech problem might come from little motors inside cells?”

Fu-Chin Liu PhD ’91, a professor at National Yang Ming Chiao Tung University in Taiwan, is the senior author of the study, which appears today in the journal Brain. Liu and Graybiel also worked together on a 2016 study of the potential link between Foxp2 and autism spectrum disorder. The lead authors of the new Brain paper are Hsiao-Ying Kuo and Shih-Yun Chen of National Yang Ming Chiao Tung University.

Speech control

Children with Foxp2-associated apraxia tend to begin speaking later than other children, and their speech is often difficult to understand. The disorder is believed to arise from impairments in brain regions, such as the striatum, that control the movements of the lips, mouth, and tongue. Foxp2 is also expressed in the brains of songbirds such as zebra finches and is critical to those birds’ ability to learn songs.

Foxp2 encodes a transcription factor, meaning that it can control the expression of many other target genes. Many species express Foxp2, but humans have a special form of Foxp2. In a 2014 study, Graybiel and colleagues found evidence that the human form of Foxp2, when expressed in mice, allowed the mice to accelerate the switch from declarative to procedural types of learning.

In that study, the researchers showed that mice engineered to express the human version of Foxp2, which differs from the mouse version by only two DNA base pairs, were much better at learning mazes and performing other tasks that require turning repeated actions into behavioral routines. Mice with human-like Foxp2 also had longer dendrites — the slender extensions that help neurons form synapses — in the striatum, which is involved in habit formation as well as motor control.

In the new study, the researchers wanted to explore how the Foxp2 mutation that has been linked with apraxia affects speech production, using ultrasonic vocalizations in mice as a proxy for speech. Many rodents and other animals such as bats produce these vocalizations to communicate with each other.

While previous studies, including the work by Liu and Graybiel in 2016, had suggested that Foxp2 affects dendrite growth and synapse formation, the mechanism for how that occurs was not known. In the new study, led by Liu, the researchers investigated one proposed mechanism, which is that Foxp2 affects motor proteins.

One of these molecular motors is the dynein protein complex, a large cluster of proteins that is responsible for shuttling molecules along microtubule scaffolds within cells.

“All kinds of molecules get shunted around to different places in our cells, and that’s certainly true of neurons,” Graybiel says. “There’s an army of tiny molecules that move molecules around in the cytoplasm or put them into the membrane. In a neuron, they may send molecules from the cell body all the way down the axons.”

A delicate balance

The dynein complex is made up of several other proteins. The most important of these is a protein called dynactin1, which interacts with microtubules, enabling the dynein motor to move along microtubules. In the new study, the researchers found that dynactin1 is one of the major targets of the Foxp2 transcription factor.

The researchers focused on the striatum, one of the regions where Foxp2 is most often found, and showed that the mutated version of Foxp2 is unable to suppress dynactin1 production. Without that brake in place, cells generate too much dynactin1. This upsets the delicate balance of dynein-dynactin1, which prevents the dynein motor from moving along microtubules.

Those motors are needed to shuttle molecules that are necessary for dendrite growth and synapse formation on dendrites. With those molecules stranded in the cell body, neurons are unable to form synapses to generate the proper electrophysiological signals they need to make speech production possible.

Mice with the mutated version of Foxp2 had abnormal ultrasonic vocalizations, which typically have a frequency of around 22 to 50 kilohertz. The researchers showed that they could reverse these vocalization impairments and the deficits in the molecular motor activity, dendritic growth, and electrophysiological activity by turning down the gene that encodes dynactin1.

Mutations of Foxp2 can also contribute to autism spectrum disorders and Huntington’s disease, through mechanisms that Liu and Graybiel previously studied in their 2016 paper and that many other research groups are now exploring. Liu’s lab is also investigating the potential role of abnormal Foxp2 expression in the subthalamic nucleus of the brain as a possible factor in Parkinson’s disease.

The research was funded by the Ministry of Science and Technology of Taiwan, the Ministry of Education of Taiwan, the U.S. National Institute of Mental Health, the Saks Kavanaugh Foundation, the Kristin R. Pressman and Jessica J. Pourian ’13 Fund, and Stephen and Anne Kott.

2023 MacVicar Faculty Fellows named

Anne Trafton |

The Office of the Vice Chancellor and the Registrar’s Office have announced this year’s Margaret MacVicar Faculty Fellows: professor of brain and cognitive sciences John Gabrieli, associate professor of literature Marah Gubar, professor of biology Adam C. Martin, and associate professor of architecture Lawrence “Larry” Sass.

For more than 30 years, the MacVicar Faculty Fellows Program has recognized exemplary and sustained contributions to undergraduate education at MIT. The program is named in honor of Margaret MacVicar, the first dean for undergraduate education and founder of the Undergraduate Research Opportunities Program (UROP). New fellows are chosen every year through a competitive nomination process that includes submission of letters of support from colleagues, students, and alumni; review by an advisory committee led by the vice chancellor; and a final selection by the provost. Fellows are appointed to a 10-year term and receive $10,000 per year of discretionary funds.

Gabrieli, Gubar, Martin, and Sass join an elite group of more than 130 scholars from across the Institute who are committed to curricular innovation, excellence in teaching, and supporting students both in and out of the classroom.

John Gabrieli

“When I learned of this wonderful honor, I felt gratitude — for how MIT values teaching and learning, how my faculty colleagues bring such passion to their teaching, and how the students have such great curiosity for learning,” says new MacVicar Fellow John Gabrieli.

Gabrieli PhD ’87 received a bachelor’s degree in English from Yale University and his PhD in behavioral neuroscience from MIT. He is the Grover M. Hermann Professor in the Department of Brain and Cognitive sciences. Gabrieli is also an investigator in the McGovern Institute for Brain Research and the founding director of the MIT Integrated Learning Initiative (MITili). He holds appointments in the Department of Psychiatry at Massachusetts General Hospital and the Harvard Graduate School of Education, and studies the organization of memory, thought, and emotion in the human brain.

He joined Course 9 as a professor in 2005 and since then, he has taught over 3,000 undergraduates through the department’s introductory course, 9.00 (Introduction to Psychological Science). Gabrieli was recognized with departmental awards for excellence in teaching in 2009, 2012, and 2015. Highly sought after by undergraduate researchers, the Gabrieli Laboratory (GabLab) hosts five to 10 UROPs each year.

A unique element of Gabrieli’s classes is his passionate, hands-on teaching style and his use of interactive demonstrations, such as optical illusions and personality tests, to help students grasp some of the most fundamental topics in psychology.

His former teaching assistant Daniel Montgomery ’22 writes, “I was impressed by his enthusiasm and ability to keep students engaged throughout the lectures … John clearly has a desire to help students become excited about the material he’s teaching.”

Senior Elizabeth Carbonell agrees: “The excitement professor Gabrieli brought to lectures by starting with music every time made the classroom an enjoyable atmosphere conducive to learning … he always found a way to make every lecture relatable to the students, teaching psychological concepts that would shine a light on our own human emotions.”

Lecturer and 9.00 course coordinator Laura Frawley says, “John constantly innovates … He uses research-based learning techniques in his class, including blended learning, active learning, and retrieval practice.” His findings on blended learning resulted in two MITx offerings including 9.00x (Learning and Memory), which utilizes a nontraditional approach to assignments and exams to improve how students retrieve and remember information.

In addition, he is known for being a devoted teacher who believes in caring for the student as a whole. Through MITili’s Mental Wellness Initiative, Gabrieli, along with a compassionate team of faculty and staff, are working to better understand how mental health conditions impact learning.

Associate department head and associate professor of brain and cognitive sciences Josh McDermott calls him “an exceptional educator who has left his mark on generations of MIT undergraduate students with his captivating, innovative, and thoughtful approach to teaching.”

Mariana Gomez de Campo ’20 concurs: “There are certain professors that make their mark on students’ lives; professor Gabrieli permanently altered the course of mine.”

Laura Schulz, MacVicar Fellow and associate department head of brain and cognitive sciences, remarks, “His approach is visionary … John’s manner with students is unfailingly gracious … he hastens to remind them that they are as good as it gets, the smartest and brightest of their generation … it is the kind of warm, welcoming, inclusive approach to teaching that subtly but effectively reminds students that they belong here at MIT … It is little wonder that they love him.”

Marah Gubar

Marah Gubar joined MIT as an associate professor of literature in 2014. She received her BA in English literature from the University of Michigan at Ann Arbor and a PhD from Princeton University. Gubar taught in the English department at the University of Pittsburgh and served as director of the Children’s Literature Program. She received MIT’s James A. and Ruth Levitan Teaching Award in 2019 and the Teaching with Digital Technology Award in 2020.

Gubar’s research focuses on children’s literature, history of children’s theater, performance, and 19th- and 20th-century representations of childhood. Her research and pedagogies underscore the importance of integrated learning.

Colleagues at MIT note her efficacy in introducing new concepts and new subjects into the literature curriculum during her tenure as curricular chair. Gubar set the stage for wide-ranging curricular improvements, resulting in a host of literature subjects on interrelated topics within and across disciplines.

Gubar teaches several classes, including 21L.452 (Literature and Philosophy) and 21L.500 (How We Got to Hamilton). Her lectures provide uniquely enriching learning experiences in which her students are encouraged to dive into literary texts; craft thoughtful, persuasive arguments; and engage in lively intellectual debate.

Gubar encourages others to bring fresh ideas and think outside the box. For example, her seminar on “Hamilton” challenges students to recontextualize the hip-hop musical in several intellectual traditions. Professor Eric Klopfer, head of the Comparative Media Studies Program/Writing and interim head of literature, calls Gubar “a thoughtful, caring instructor, and course designer … She thinks critically about whose story is being told and by whom.”

MacVicar Fellow and professor of literature Stephen Tapscott praises her experimentation, abstract thinking, and storytelling: “Professor Gubar’s ability to frame intellectual questions in terms of problems, developments, and performance is an important dimension of the genius of her teaching.”

“Marah is hands-down the most enthusiastic, effective, and engaged professor I had the pleasure of learning from at MIT,” writes one student. “She’s one of the few instructors I’ve had who never feels the need to reassert her place in the didactic hierarchy, but approaches her students as intellectual equals.”

Tapscott continues, “She welcomes participation in ways that enrich the conversation, open new modes of communication, and empower students as autonomous literary critics. In professor Gubar’s classroom we learn by doing … and that progress also includes ‘doing’ textual analysis, cultural history, and abstract literary theory.”

Gubar is also a committed mentor and student testimonials highlight her supportive approach. One of her former students remarked that Gubar “has a strong drive to be inclusive, and truly cares about ‘getting it right’ … her passion for literature and teaching, together with her drive for inclusivity, her ability to take accountability, and her compassion and empathy for her students, make [her] a truly remarkable teacher.”

On receiving this award Marah Gubar writes, “The best word I can think of to describe how I reacted to hearing that I had received this very overwhelming honor is ‘plotzing.’ The Yiddish verb ‘to plotz’ literally means to crack, burst, or collapse, so that captures how undone I was. I started to cry, because it suddenly struck me how much joy my father, Edward Gubar, would have taken in this amazing news. He was a teacher, too, and he died during the first phase of this terrible pandemic that we’re still struggling to get through.”

Adam C. Martin

Adam C. Martin is a professor and undergraduate officer in the Department of Biology. He studies the molecular mechanisms that underlie tissue form and function. His research interests include gastrulation, embryotic development, cytoskeletal dynamics, and the coordination of cellular behavior. Martin received his PhD from the University of California at Berkeley and his BS in biology (genetics) from Cornell University. Martin joined the Course 7 faculty in 2011.

“I am overwhelmed with gratitude knowing that this has come from our students. The fact that they spent time to contribute to a nomination is incredibly meaningful to me,” says Martin. “I want to also thank all of my faculty colleagues with whom I have taught, appreciate, and learned immensely from over the past 12 years. I am a better teacher because of them and inspired by their dedication.”

He is committed to undergraduate education, teaching several key department offerings including 7.06 (Cell Biology), 7.016 (Introductory Biology), 7.002 (Fundamentals of Experimental Molecular Biology), and 7.102 (Introduction to Molecular Biology Techniques).

Martin’s style combines academic and scientific expertise with creative elements like props and demonstrations. His “energy and passion for the material” is obvious, writes Iain Cheeseman, associate department head and the Herman and Margaret Sokol Professor of Biology. “In addition to creating engaging lectures, Adam went beyond the standard classroom requirements to develop videos and animations (in collaboration with the Biology MITx team) to illustrate core cell biological approaches and concepts.”

What sets Martin apart is his connection with students, his positive spirit, and his welcoming demeanor. Apolonia Gardner ’22 reflects on the way he helped her outside of class through his running group, which connects younger students with seniors in his lab. “Professor Martin was literally committed to ‘going the extra mile’ by inviting his students to join him on runs around the Charles River on Friday afternoons,” she says.

Amy Keating, department head and Jay A. Stein professor of biology, and professor of biological engineering, goes on to praise Martin’s ability to attract students to Course 7 and guide them through their educational experience in his role as the director of undergraduate studies. “He hosts social events, presides at our undergraduate research symposium and the department’s undergraduate graduation and awards banquet, and works with the Biology Undergraduate Student Association,” she says.

As undergraduate officer, Martin is involved in both advising and curriculum building. He mentors UROP students, serves as a first-year advisor, and is a current member of MIT’s Committee on the Undergraduate Program (CUP).

Martin also brings a commitment to diversity, equity, and inclusion (DEI) as evidenced by his creation of a DEI journal club in his lab so that students have a dedicated space to discuss issues and challenges. Course 7 DEI officer Hallie Dowling-Huppert writes that Martin “thinks deeply about how DEI efforts are created to ensure that department members receive the maximum benefit. Adam considers all perspectives when making decisions, and is extremely empathetic and caring towards his students.”

“He makes our world so much better,” Keating observes. “Adam is a gem.”

Lawrence “Larry” Sass

Larry Sass SM ’94, PhD ’00 is an associate professor in the Department of Architecture. He earned his PhD and SM in architecture at MIT, and has a BArch from Pratt Institute in New York City. Sass joined the faculty in the Department of Architecture in 2002. His work focuses on the delivery of affordable housing for low-income families. He was included in an exhibit titled “Home Delivery: Fabricating the Modern Dwelling” at the Museum of Modern Art in New York City.

Sass’s teaching blends computation with design. His two signature courses, 4.500 (Design Computation: Art, Objects and Space) and 4.501 (Tiny Fab: Advancements in Rapid Design and Fabrication of Small Homes), reflect his specialization in digitally fabricating buildings and furniture from machines.

Professor and head of architecture Nicholas de Monchaux writes, “his classes provide crucial instruction and practice with 3D modeling and computer-generated rendering and animation …  [He] links digital design to fabrication, in a process that invites students to define desirable design attributes of an object, develop a digital model, prototype it, and construct it at full scale.”

More generally, Sass’ approach is to help students build confidence in their own design process through hands-on projects. MIT Class of 1942 Professor John Ochsendorf, MacVicar Fellow, and founding director of the Morningside Academy for Design with appointments in the departments of architecture and civil and environmental engineering, confirms, “Larry’s teaching is a perfect embodiment of the ‘mens et manus’ spirit … [he] requires his students to go back and forth from mind and hand throughout each design project.”

Students say that his classes are a journey of self-discovery, allowing them to learn more about themselves and their own abilities. Senior Natasha Hirt notes, “What I learned from Larry was not something one can glean from a textbook, but a new way of seeing space … he tectonically shifted my perspective on buildings. He also shifted my perspective on myself. I’m a better designer for his teachings, and perhaps more importantly, I better understand how I design.”

Senior Izzi Waitz echoes this sentiment: “Larry emphasizes the importance of intentionally thinking through your designs and being confident in your choices … he challenges, questions, and prompts you so that you learn to defend and support yourself on your own.”

As a UROP coordinator, Sass assures students that the “sky is the limit” and all ideas are welcome. Postgraduate teaching fellow and research associate Myles Sampson says, “During the last year of my SM program, I assisted Larry in conducting a year-long UROP project … He structured the learning experience in a way that allowed the students to freely flex their design muscles: no idea was too outrageous.”

Sass is equally devoted to his students outside the classroom. In his role as head of house at MacGregor House, he lives in community with more than 300 undergraduates each year, providing academic guidance, creating residential programs and recreational activities, and ensuring that student wellness and mental health is a No. 1 priority.

Professor of architecture and MacVicar Fellow Les Norford says, “In two significant ways, Larry has been ahead of his time: combining digital representation and design with making and being alert to the well-being of his students.”

“In his kindness, he honors the memory of Margaret MacVicar, as well as the spirit of MIT itself,” Hirt concludes. “He is a designer, a craftsman, and an innovator. He is an inspiration and a compass.”

On receiving this award, Sass is full of excitement: “I love teaching and being part of the MIT community. I am grateful for the opportunity to be part of the MacVicar family of fellows.”

New insights into training dynamics of deep classifiers

by MIT Center for Brains, Minds and Machines |

A new study from researchers at MIT and Brown University characterizes several properties that emerge during the training of deep classifiers, a type of artificial neural network commonly used for classification tasks such as image classification, speech recognition, and natural language processing.

The paper, “Dynamics in Deep Classifiers trained with the Square Loss: Normalization, Low Rank, Neural Collapse and Generalization Bounds,” published today in the journal Research, is the first of its kind to theoretically explore the dynamics of training deep classifiers with the square loss and how properties such as rank minimization, neural collapse, and dualities between the activation of neurons and the weights of the layers are intertwined.

In the study, the authors focused on two types of deep classifiers: fully connected deep networks and convolutional neural networks (CNNs).

A previous study examined the structural properties that develop in large neural networks at the final stages of training. That study focused on the last layer of the network and found that deep networks trained to fit a training dataset will eventually reach a state known as “neural collapse.” When neural collapse occurs, the network maps multiple examples of a particular class (such as images of cats) to a single template of that class. Ideally, the templates for each class should be as far apart from each other as possible, allowing the network to accurately classify new examples.

An MIT group based at the MIT Center for Brains, Minds and Machines studied the conditions under which networks can achieve neural collapse. Deep networks that have the three ingredients of stochastic gradient descent (SGD), weight decay regularization (WD), and weight normalization (WN) will display neural collapse if they are trained to fit their training data. The MIT group has taken a theoretical approach — as compared to the empirical approach of the earlier study — proving that neural collapse emerges from the minimization of the square loss using SGD, WD, and WN.

Co-author and MIT McGovern Institute postdoc Akshay Rangamani states, “Our analysis shows that neural collapse emerges from the minimization of the square loss with highly expressive deep neural networks. It also highlights the key roles played by weight decay regularization and stochastic gradient descent in driving solutions towards neural collapse.”

Weight decay is a regularization technique that prevents the network from over-fitting the training data by reducing the magnitude of the weights. Weight normalization scales the weight matrices of a network so that they have a similar scale. Low rank refers to a property of a matrix where it has a small number of non-zero singular values. Generalization bounds offer guarantees about the ability of a network to accurately predict new examples that it has not seen during training.

The authors found that the same theoretical observation that predicts a low-rank bias also predicts the existence of an intrinsic SGD noise in the weight matrices and in the output of the network. This noise is not generated by the randomness of the SGD algorithm but by an interesting dynamic trade-off between rank minimization and fitting of the data, which provides an intrinsic source of noise similar to what happens in dynamic systems in the chaotic regime. Such a random-like search may be beneficial for generalization because it may prevent over-fitting.

“Interestingly, this result validates the classical theory of generalization showing that traditional bounds are meaningful. It also provides a theoretical explanation for the superior performance in many tasks of sparse networks, such as CNNs, with respect to dense networks,” comments co-author and MIT McGovern Institute postdoc Tomer Galanti. In fact, the authors prove new norm-based generalization bounds for CNNs with localized kernels, that is a network with sparse connectivity in their weight matrices.

In this case, generalization can be orders of magnitude better than densely connected networks. This result validates the classical theory of generalization, showing that its bounds are meaningful, and goes against a number of recent papers expressing doubts about past approaches to generalization. It also provides a theoretical explanation for the superior performance of sparse networks, such as CNNs, with respect to dense networks. Thus far, the fact that CNNs and not dense networks represent the success story of deep networks has been almost completely ignored by machine learning theory. Instead, the theory presented here suggests that this is an important insight in why deep networks work as well as they do.

“This study provides one of the first theoretical analyses covering optimization, generalization, and approximation in deep networks and offers new insights into the properties that emerge during training,” says co-author Tomaso Poggio, the Eugene McDermott Professor at the Department of Brain and Cognitive Sciences at MIT and co-director of the Center for Brains, Minds and Machines. “Our results have the potential to advance our understanding of why deep learning works as well as it does.”

School of Science presents 2023 Infinite Expansion Awards

Anne Trafton |

The MIT School of Science has announced seven postdocs and research scientists as recipients of the 2023 Infinite Expansion Award. Nominated by their peers and mentors, the awardees are recognized not only for their exceptional science, but for mentoring and advising junior colleagues, supporting educational programs, working with the MIT Postdoctoral Association, or contributing some other way to the Institute.

The 2023 Infinite Expansion award winners in the School of Science are:

  • Kyle Jenks, a postdoc in the Picower Institute for Learning and Memory, nominated by professor and Picower Institute investigator Mriganka Sur;
  • Matheus Victor, a postdoc in the Picower Institute, nominated by professor and Picower Institute director Li-Huei Tsai.

A monetary award is granted to recipients, and a celebratory reception will be held for the winners this spring with family, friends, nominators, and recipients of the Infinite Expansion Award.