Author: Julie Pryor

How visual learning happens in the brain

by Jennifer Michalowski |

The wiring and rewiring of the brain never ends. Neural pathways are constantly being reshaped as we interact with the world and learn new things. At MIT’s McGovern Institute and York University in Toronto, scientists are combining detailed analysis of brain activity with computational modeling to better understand that change.

McGovern Institute postdoctoral fellow Lynn Sörensen, Investigator James DiCarlo, and York University assistant professor Kohitij Kar worked together to compare what happened when monkeys and an artificial neural network with brain-like architecture were trained to visually identify the same objects. As the model’s performance improved, it reorganized itself in ways that closely paralleled changes the team detected in the brains of monkeys.

Their work, reported today in the journal Nature Communications, shows how changes in visual processing support animals’ ability to learn to discriminate new kinds of objects. By modeling these changes, the researchers hope to better predict how training reshapes perception, which could one day inform educational strategies for a wide range of learners.

Subtle changes

Learning about a new object calls on many parts of the brain. Visual-processing areas work together to make sense of information taken in through the eyes, then communicate with other brain areas to give the visual information meaning and guide behavior. Multiple parts of this system likely change during learning, and the research team wanted a clearer understanding of how that change is distributed.

Neuroscientists have debated how much change occurs in the brain’s visual-processing areas when an animal learns to recognize new objects. Some suspected that visual-processing pathways remain largely unchanged during learning to avoid broadly disrupting visual perception, but others have reported changes in activity within dedicated visual-processing areas with this kind of learning in humans and other primates.  

To take a closer look, the team focused on neural activity in a key component of the brain’s visual object-processing network, the inferior temporal (IT) cortex. By the time visual information reaches the IT cortex, key object features are clearly represented—so much so that it’s possible to “decode” what object a monkey is seeing and even predict what errors it’s likely to make in identifying it, simply by analyzing patterns of neural activity there.

The team recorded neural activity in the IT cortex from two groups of monkeys as the animals looked at and identified images of objects. Some of the monkeys were untrained, so the images they saw had little meaning to them. Others had already learned to identify similar objects, so they could usually discriminate between elephants, chairs, and other select objects, even when those objects were presented at different sizes, from different angles, or against different backgrounds than the ones they had seen before.

The broad pattern of activity in the IT cortex was largely similar in trained and untrained monkeys, suggesting that learning had not dramatically rewritten this high-level visual representation. Still, the group found subtle but reliable differences in the way neurons in the IT cortex responded to images in monkeys that had learned to recognize the kinds of objects they were shown, compared to the untrained monkeys.

Modeling learning

The group turned to computational models to investigate how those modest changes might contribute to learning. Sörensen trained a suite of artificial neural networks whose internal components had been mapped to monkey IT cortex to identify the same categories of objects the monkeys had seen. The models were designed to learn using gradient descent, meaning they continually improved their accuracy by adjusting their parameters in response to errors.

Only some of the primate-like models showed learning behavior that matched that of the monkeys. In those that did, the IT-like stage changed in ways that resembled the learning-related changes the researchers had observed in the IT cortex of trained monkeys.

While gradient descent is commonly used to train artificial intelligence, it is generally considered biologically implausible as a direct model of how the brain learns. The researchers say the strong match in learning effects between the monkeys and their model demonstrates that these kinds of artificial neural networks can offer insights into biological learning at a useful level of abstraction, even if the brain does not learn in the same way.

“This shows that you can actually build in silico versions of future experiments,” Sörensen says. “I think that gives us this playground of asking ‘what if’ questions—and potentially predicting new things that go beyond the experimenter’s intuition.”

Most of the changes that allowed for learning in the model occurred outside of the IT cortex. “This tells us that there is a lot between the area we recorded from and the final behavioral readout that needs to change during this process,” Kar says. He adds that the team’s model will be useful as researchers look more deeply into how downstream brain areas contribute to learning.

The researchers stress that their study allowed more granular measurements of brain activity than would be possible in humans, and because monkeys’ brains are organized similarly to our own, their experiments have direct relevance to human learning. They say understanding the impact of plasticity in monkeys’ IT cortex could help researchers design new learning strategies for humans.

“Our prior conceptual working model of you—or a monkey—learning new objects was that your brain makes changes to synaptic connections that are largely downstream of your visual system, so you don’t destroy your visual system,” says DiCarlo, who is also the Peter de Florez Professor of Brain and Cognitive Sciences and Director of the MIT Siegel Family Quest for Intelligence. “You wouldn’t want your whole visual system to become an elephant detector [just because you’ve learned to identify an elephant]. But this study went beyond that to say actually, when you learn ‘elephant,’ your IT does change a little bit to make it a little more relevant to elephants.”

That likely has consequences for recognizing other visual features, too. Subtle changes in the IT cortex that support elephant recognition might also make you better at identifying things other than elephants, DiCarlo says. Likewise, the same changes might make it a little harder to identify something else.

These kinds of consequences may be difficult to predict intuitively, but become obvious with computational modeling. For instance, the team’s models revealed that after learning to recognize new objects, the IT cortex contained more information about objects’ locations. By providing insights like these, models could aid the design of more effective training strategies for visual tasks, including for people with altered sensory processing, who may learn from visual information in atypical ways.

 

 

Separating logic and language

by Jennifer Michalowski |

Portrait of a smiling scientist in a blue v-neck blouse.
Hope Kean, a postdoctoral researcher and former ICoN graduate fellow in Evelina Fedorenko’s lab. Photo by Caitlin Cunningham Photography.

Some people find it useful to talk through their problems—but language isn’t necessary for logical reasoning, cognitive neuroscientists at MIT’s McGovern Institute say. In research published today in the journal PNAS, researchers led by McGovern Institute investigator Evelina Fedorenko have shown that people can perform well on tasks that require logical reasoning even if their language abilities are severely impaired. What’s more, brain imaging shows that language-processing parts of the brain are not called on for logical reasoning. Fedorenko is also an associate professor of brain and cognitive sciences at MIT.

Philosophers, linguists, and cognitive scientists have debated the relationship between language and thought for thousands of years, with many arguing that we use language to think.  There are good reasons to suspect a close relationship between logic and language, acknowledges Hope Kean, a postdoctoral researcher and former ICoN graduate fellow in Fedorenko’s lab. “Abstract thinking has properties that look a lot like language,” she says, pointing to structural similarities: “You can decompose a thought into subcomponents, like little atoms of logical propositions, and you can combine them in a hierarchical manner to make more complex structured rules, very akin to language.”

But she and Fedorenko suspected that while we largely depend on language to communicate about logical reasoning—from presenting a problem to explaining how we have arrived at conclusions—the brain might use a separate system for the reasoning itself. “There are aspects of thinking that seem to go beyond some of the limitations of language,” Kean explains. Logical reasoning demands precision that language often lacks. And language is linear, progressing one word at a time, whereas evaluating available information to reach logical conclusions can require thinking in less linear ways.

Logical reasoning

These observations left Kean curious about how the brain handles logical reasoning. It’s a particularly difficult question to answer scientifically, because it’s hard to take language out of the equation when working with human study participants. But Fedorenko’s team did just that by collaborating with Rosemary Varley, a neuroscientist at University College London who studies acquired language disorders, and her team.

Together, the scientists worked with two patients who had experienced stroke that damaged language-processing parts of their brains, leaving them with severe impairments in both understanding and producing language. They designed language-free logic games in which participants were asked to infer relationships between sets of numbers. Given two lists, they had to figure out the hidden rule that turned one list into the other, such as reversing the digits or removing numbers above a certain value. Once they thought they’d discovered the rule, they had to apply it to new examples. In a second game, participants were presented a set of geometric patterns and asked to identify another pattern to complete the matrix.

As participants solved increasingly difficult puzzles, it became clear that people don’t need language for this kind of reasoning. Patients with language impairments solved the problems as well as a control group and were even able to communicate the rules they inferred using gestures or with a sketch. “It really upends a theory that says that symbolic rule induction is not possible without linguistic capacities,” says Kean.

Four brain scans with the bottom two scans showing activation areas in red.
Anatomical scans of two participants with severe aphasia (top row) and a projection of the probabilistic atlas for the language network into the MRI image of one of the participants. Image: Hope Kean

Alongside this part of the study, Kean and colleagues also used functional brain imaging to study what happens in the brains of healthy adults when they are engaged in logical reasoning. Participants in this part of the study visited MIT for a series of MRI scans, which captured images of their brain activity during an array of tasks. In addition to completing different kinds of logic games inside the scanner, participants were asked to engage in tasks designed to map the language-processing parts of their brain. Another set of tasks was used to map each person’s Multiple Demand network—a distributed brain system that supports complex problem solving.

These neurotypical participants completed logic games similar to those used with the language-impaired patients. They were also presented with problems that required syllogistic reasoning, using “if-then” statements such as “If the ball is red, then it is big. The ball is red. Is the ball big?” The team varied the difficulty of the logic puzzles so they could see which brain areas became more active when the need for logical reasoning intensified. Likewise, they looked for changes in brain activity when participants had to infer a hidden rule versus simply applying a rule they’d been given.

Here too, a separation between language and logic was clear: The MRI scans showed the brain’s language system is not engaged for either inductive reasoning (when participants identified hidden rules) or deductive reasoning (when they assessed the validity of syllogistic conclusions). Surprisingly, the Multiple Demand network, which many scientists had suspected was important for logical reasoning, was engaged during inductive reasoning but didn’t seem to get involved in deductive reasoning—a finding Kean is building on in her ongoing work.

For Fedorenko and Kean, the findings are strong support for a separation of logic and language in the brain. They add to previous findings from Fedorenko’s lab showing that other types of thinking, such as object categorization and social reasoning, also do not rely on language.

Acquired language impairments and AI

The researchers say these findings have important implications for how we think about acquired language impairments, or aphasia. Specialists who work with people with aphasia have long recognized that loss of language does not mean loss of intelligence. People with aphasia can continue to enjoy playing chess, solving sudoku puzzles, or being in charge of the family’s finances. But it is common for others to confuse their communicative difficulties with thinking difficulties.

“This research adds to a growing body of work establishing that even severely aphasic individuals can preserve their ability for abstract logical thought—a defining feature of our species,” Fedorenko says. “We should continue to educate the public that linguistic difficulties—in aphasia, but also in those with developmental language conditions, such as stuttering, or those who do not speak English natively—are not indicative of how smart or capable someone is.”

There could be implications for artificial intelligence, too. Large language models like ChatGPT and Claude are trained entirely on text and use text as their output—yet they convincingly simulate some kinds of human reasoning. Exploring the differences between these models and the human brain, where language and abstract logical thought are distinct, might offer useful insights to inform future models, Kean says.

When it comes to understanding how the human brain reasons, Kean calls this a new frontier in the geography of thought—and she says it’s one she is eager to explore.

 

The brain’s language network is more extensive than previously thought

Anne Trafton |

For decades, neuroscientists have known that specific regions in the brain’s left hemisphere are responsible for processing language. However, a new study from MIT shows that language processing also occurs in many other parts of the brain.

Using functional magnetic resonance imaging (fMRI) data from more than 700 people, the researchers identified 17 additional regions of the brain that appear to play a role in language. These regions are scattered across the brain, including parts of the cerebellum, hippocampus, and cerebral cortex, and they make up about 5 percent of the total volume of the adult brain — about the size of a large strawberry.

“Even though there are all these distant components, it’s pretty restricted in terms of volume. You don’t need that much of the brain to do language,” says Evelina Fedorenko, an MIT associate professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Exactly how these regions contribute to language processing is still to be discovered, although the researchers have made some progress toward determining the functions of the cerebellar regions that they identified.

A group of smiling researchers stand in front of a wall of windows.
The authors of the Journal of Neuroscience manuscript pictured together, from left to right: Benjamin Lipkin, Colton Casto, Evelilna Fedorenko, Agata Wolna, Aaron Wright, and Sam Hutchinson. Photo: Alexandra Sokhina

MIT postdoc Agata Wolna is the lead author of the paper, which appears today in the Journal of Neuroscience. Other authors include Aaron Wright, a K. Lisa Yang Post-Baccalaureate Research Scholar at MIT; Colton Casto, a graduate student at Harvard University; Samuel Hutchinson, a graduate student at MIT; and Benjamin Lipkin PhD ’26.

Tracking language

The brain’s language processing centers include Broca’s area, first discovered in the 1800s, plus additional regions in the left frontal and temporal lobes of the brain. Scientists have found that some of the corresponding areas of the right hemisphere also contribute to processing language, especially the social-emotional components of language.

There have also been hints that other parts of the brain might be involved in language processing. Early in her career, Fedorenko’s language studies often showed active brain regions outside of the canonical language centers, but she says she was discouraged from including them in her papers.

“When we initially started looking at language, in the first couple of papers, I tried to be comprehensive and include anything that seemed consistent across participants, and there was a huge amount of resistance,” she says.

“People would say things like, ‘Well, we know those are not language areas, so please focus on the language areas.’”

In the new study, she and Wolna wanted to revisit those brain scans and see if they could systematically identify language regions outside of the standard language-processing areas.

To do that, they analyzed data from 772 people who had been scanned in Fedorenko’s lab since 2013. Each of these participants underwent a task known as a language localizer, which is used to determine the location of language processing areas for each subject.

During the test, participants read or listen to sentences as well as sequences of nonwords. For each person, the researchers measure the difference in strength of response when reading real sentences or nonsense sequences. The brain areas that work harder during the sentence condition are considered to be doing something relevant to language, especially if they respond while both reading and listening to sentences.

“It’s a very simple paradigm that lets you identify this core language system in individual brains,” Wolna says.

When searching for language areas, the researchers usually use a relatively strict statistical threshold. In this study, they relaxed the threshold and also used some targeted searches in subcortical areas, in hopes of finding all areas that may contribute to language processing. “We always see this frontal temporal network, but there’s quite a lot of evidence that there are other regions that are also critical for language processing,” Wolna says. “By using a laxer threshold and zooming in on areas with weak MRI signal, we tried to maximize the chances of finding small and weakly responsive regions outside of this left frontal temporal system.”

A widespread network

For about 490 of the participants, the researchers also had data on how their brain responded during a spatial working memory task — remembering the locations of flashing squares on a grid. This task engages a brain network called the multiple demand system, which does not overlap with the core language areas.

This task allowed the researchers to ask whether any of the newly identified language-sensitive regions specifically respond to language and not more general cognitive processes.

Of the 17 new language sites that were revealed by this study, five are located in the cerebellum, which is mainly involved in coordinating the body’s movement. In a study published earlier this year, researchers led by Casto found that three of those cerebellar regions also became engaged during some nonlinguistic cognitive tasks, which was also seen in the new study.

“Those areas that respond to both language and some other tasks could be really interesting and important because they may be doing something like integrating information from different cortical systems,” Fedorenko says.

They also found language-selective regions in the medial frontal cortex, the bottom surface of the left temporal lobe, the hippocampus, and the amygdala. The researchers now plan to further study how these brain regions might contribute to language processing.

A graphic illustrating the established language regions of the brain (red) alongside the new, extended language regions discovered by Fedorenko’s team (blue). Image: Agata Wolna

“We can now test some ideas from past work, and also more rigorously characterize these regions across different kinds of language manipulations, and different kinds of non-linguistic tasks, to try to understand what it is that they’re doing,” Fedorenko says.

The research was funded by the Simons Center for the Social Brain at MIT, the McGovern Institute, MIT’s Department of Brain and Cognitive Sciences, and the MIT Siegel Family Quest for Intelligence.

 

 

Two McGovern faculty appointed to named professorships at MIT

by Julie Pryor |

MIT has appointed McGovern faculty members Sven Dorkenwald and Josh McDermott to named professorships that will provide additional support for their “outstanding research and educational careers.” Named professorships at MIT are prestigious endowed faculty chairs that provide crucial financial support for both junior faculty and senior scholars, enabling them to pursue bold research and global challenges.

Dorkenwald, who recently joined MIT as an assistant professor of brain and cognitive sciences and an investigator at the McGovern Institute, has been selected to hold the  Silverman (1968) Family Career Development Professorship for a three-year term beginning July 1, 2026. A trailblazer in the field of computational neuroscience, Dorkenwald reconstructs maps of neuronal circuits to investigate how they support complex computations. He is recognized for his leadership in connectomics—an emerging discipline focused on reconstructing and analyzing neural circuitry at unprecedented scale and detail. Jeffrey Silverman ’68 is a life member emeritus of the MIT Corporation. His generous gift to the institute empowers early career professors to pursue high-risk research. 

McDermott, a professor of brain and cognitive sciences and an associate investigator at the McGovern Institute, has been selected to hold the Uncas (1923) and Helen Whitaker Professorship for a five-year renewable term beginning July 1, 2026. McDermott’s research operates at the intersection of psychology, neuroscience, and engineering to study how people hear and interpret sound. Groundbreaking discoveries from the McDermott lab are informing new treatments for hearing loss, and paving the way for machine systems that emulate the human ability to recognize and interpret sound. The Uncas (1923) and Helen Whitaker Professorship chair was established in 1980 through a gift from the late Helen Whitaker, the first woman elected to life membership of the MIT Corporation. It is designed to support distinguished faculty whose work spans multiple disciplines to solve complex, real-world problems. 

 

The brain’s internal ruler

by Jennifer Michalowski |

McGovern Investigator Fan Wang. Photo: Caitliin Cunningham

If you are crossing an unfamiliar room in the dark, you may grope around a bit to get a sense of your space.

But for many animals, feeling out a space comes more naturally. A mouse, for instance, can efficiently navigate in the dark just by grazing its whiskers against walls and other obstacles.

Fan Wang, a professor of brain and cognitive sciences and an investigator at the McGovern Institute, has discovered how neurons in a mouse’s brainstem use signals from the animal’s touch-sensitive whiskers to estimate an object’s distance from the face.

Her team’s findings, published online June 25, 2026, in the journal Neuron, unlock key circuitry the brain uses to represent the space immediately surrounding the body.

Mapping space

The circuit the team discovered is part of the brain’s system for creating an egocentric map of space—that is, understanding where things are relative to one’s own body. Neuroscientists know that the brain calls on specialized circuits to understand space in this way, which are different from its system for mapping space using external landmarks.

In their study, Wang and her team explored how the brain maps the space closest to the body, which is known as the peripersonal space. This is the space in which we move, and it is vital that we understand where things are in relationship to our bodies so we can reach, step, avoid hazards, and otherwise interact effectively with our environment.

Wang says mice were an appealing model for investigating how the brain understands objects’ distance within the peripersonal space, because a rodent’s whiskers seem so much like a built-in set of rulers. These whiskers, which vary in length, are swept back and forth as the animals explore their environment. As whiskers bend and vibrate, the mechanical sensations are relayed to the brain by sensory neurons at their base. Those neurons fire more when a whisker bends close to the face than they do in response to contact near the whisker’s tip, communicating information about the proximity of the touch.

Close-up image of a mouse peeking through a hole with whiskers grazing the edge of the hole. Image: Istockphoto

Wang’s team wanted to know if the brain uses these signals to build an internal ruler-like representation of distance more precise than “near” or “far.” To find out, graduate student Wenxi Xiao and research scientist Kyle Severson monitored neural activity in a small sensory-processing region in the brainstem where tactile signals from the whiskers first arrive in the brain. They studied what happened there as mice walked on a treadmill while brushing their whiskers against a wall that passed by at different distances.

Many neurons in the region were sensitive to the whisker bending triggered by the wall. Some behaved similarly to the sensory neurons they were getting their information from, firing more when the wall was closer to the face and thus serving as a proximity-based distance code. But other cells were tuned in to discrete distances, firing only when the distance of the wall the whiskers had touched was within a specific range.

For some neurons, activity peaked when the wall was 23 mm away from the face, near the tips of the longest whiskers. Others responded most when the wall was at intermediate distances.

“Each of these neurons represents a specific distance, and together they span the full range reached by the longest whisker, like tick marks on the ruler,” Wang explains. “We call that the map code.”

The team wanted to know how the brain converts proximity signals from different whiskers into accurate map code of object’s distances from the head. “You cannot just listen to individual whisker neurons, because a contact at the tip of a short whisker would be in the middle of a long whisker. You need a brain circuit to build a unified distance map,” Wang says.

Through computational modeling and by exploring what happened when they manipulated neural signaling in specific ways, Wang’s team showed how distances can be calculated by comparing inputs from different sensory neurons. Their findings suggest that each brainstem neuron that makes up the map code receives both direct excitatory inputs from proximity-sensitive whisker neurons and inhibitory inputs from neurons driven by proximity-dependent whisker touch signals.

“Essentially the inhibitory pathway allows the brainstem to compare two inputs by subtraction,” Wang explains. “If one input signals ‘this is how far it is’ and the other signals ‘this is how far I estimate it to be,’ subtracting one from the other yields an intermediate value. We think it’s a simple and elegant way to transform tactile input into a representation of discrete distance.”

Wang notes that despite their importance, the brain’s body-centered representations of space have so far received little attention from neuroscientists, who know much more about how we understand locations in space relative to landmarks (an allocentric map). She is eager to investigate how the egocentric map code her team discovered is integrated with other brain systems to guide movement, social interactions, and other behavior, and hopes the findings will further exploration from other groups.

The study was funded by grants from the National Institutes of Health.

Would you return a favor? Scientists say it depends on the relationship

by Anne Trafton |

When a friend buys you a cup of coffee, it’s likely that next time, you’ll return the gesture. This type of reciprocal generosity has been well-documented in behavioral economic studies.

However, anthropologists and other social scientists have known for decades that in the context of relationships where one person has more power, status, or influence, reciprocal generosity is usually not the norm.

Researchers at MIT have now experimentally demonstrated, for the first time, that small changes to the relationship context can dramatically change people’s actions and expectations of reciprocal generosity.

During interactions between people of different social status, people tend to expect that generosity will flow one way, and it can be either up or down. It may be that a professor always buys coffee for her students, or that a student always offers to help carry groceries for his resident advisor. Once the precedent is established, it is expected to continue.

One interpretation of the findings is that keeping track of whose turn it is to do a favor is the exception in social interactions, not the rule. That is, it is extra work that we do when we want to maintain equal relationships.

“In many intimate relationships, hierarchical relationships, or other kinds of role-based relationships, you don’t put in the work of trying to keep track of turns,” says Rebecca Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences, a member of the McGovern Institute for Brain Research, and associate dean of science at MIT. “Under this interpretation, we just follow precedent because following a precedent is easier. We all know what to expect, and we don’t have to keep track of what happened last time.”

Saxe is the senior author of the study, which appears in the journal Open Mind. MIT graduate student Alicia Chen is the paper’s lead author.

Changing expectations

Most experimental studies of generosity have been done in the context of behavioral economics and game theory. In such experiments, people are usually paired with a stranger and asked to play games that require coordination. Such studies have found that people tend to use turn-taking and reciprocity as their default strategies. These scenarios, however, are stripped from any social context that might exist between people in the real world.

Saxe and Chen wanted to see if they could measure the effects of social context by incorporating relationships into the type of experiments used to evaluate people’s expectations regarding generosity.

“Where generosity becomes hard and complicated is when it starts to occur in the context of existing relationships, because it changes the terms of the relationships,” Saxe says. “What’s expected of you is very different within a relationship than outside of one.”

To study these effects, the researchers designed experiments in which participants read stories about different types of interactions. In some of the scenarios, the subjects of the stories were described as having either symmetric or asymmetric relationships. In others, they were given specific social relationships such as aunt-niece or manager-employee.

Each story described interactions that might be seen in typical daily life, such as buying coffee for a co-worker or preparing a meal for one’s family. Participants were then asked to predict what would happen the next time the interaction occurred.

In all of these scenarios, the researchers found that people expected that generous acts would be reciprocated when they occurred between individuals in symmetric relationships such as friends, cousins, or co-workers of equal rank. However, their expectations changed for asymmetric relationships, where each person has a different social status. In those cases, people expected that any precedent that was set would continue in the future.

One possible explanation for this is that reciprocity is not the norm but an exception that only occurs in the interactions between equals or strangers, the researchers say. Many of our interactions are with people with whom we have asymmetric relationship, and to maintain those relationships, it’s simply easier to follow precedent.

“If there’s no need to keep track of our equal status, then in some ways it’s the default to fall back on following precedents,” Saxe says.

Maintaining relationships

The study showed that in asymmetric relationships, generosity could flow in either direction. Once that direction was established, it was expected to continue. For example, after an older brother bought concert tickets for a much younger brother, the study participants expected that the older brother would also buy the tickets for the next concert.

“We found that when people know the relationship is asymmetric, they don’t expect reciprocity; they expect the same action to keep on going,” Chen says. “If the lower-rank person acts generously, people expect that to continue, and if the higher-rank person acts generously, people expect that to continue.”

Following precedents is not only easier, but keeping up these actions may help solidify and define existing relationships. For example, anthropologists have long known that gift-giving helps to construct and maintain social relationships.

“Following a precedent can be a way of actively maintaining relationships and hierarchies, when the asymmetry of the exchange truly reflects the asymmetry of the relationship,” Saxe says.

The researchers are now working on creating computational models that could be used to analyze different factors that people take into account when they’re considering whether someone might reciprocate a generous act. In addition to the factors examined in this study, others could include how much each person will benefit, what type of relationship they’re in, and culturally specific expectations of how people should act in different situations.

“One really powerful thing about these models is that we can build in existing theories, add things to the models, and then compare how much these extra factors, like considerations related to social relationships, matter in terms of explaining what people are doing,” Chen says. “This allows us to quantitatively compare the different theories to each other.”

The research was funded by the Simons Foundation Autism Research Initiative and the Patrick J. McGovern Foundation.

Language development in the brain

by Jennifer Michalowski |

The brain’s capacity to use and understand language expands rapidly in the first years of life, as babies start to make sense of the words they hear and eventually begin to piece together sentences of their own. The language-processing parts of the brain that make this possible continue to evolve in older children, as they expand their vocabularies and learn to use language more flexibly.

Scientists at MIT’s McGovern Institute have captured snapshots of the developing language-processing network in brain scans of hundreds of children and adolescents. Their data, reported on May 16 in the journal Nature Communications, show that the network continues to mature, becoming better integrated and increasingly responsive until around age 16. But they also found that a key feature of the adult language network is established early on: its localization in the left side of the brain.

Language lateralization

It is well known that using language is mostly the job of the left hemisphere. As adults, we call on the language-processing regions there when we read, write, speak, or listen to others talk. But there was some question as to whether this left lateralization is established early in life, or might instead emerge as the language network matures, with both sides of the brain contributing to language in childhood.

To find out, researchers needed to see young brains in action—and several McGovern Institute labs had collected exactly the right kind of data. Groups led by Associate Professor of Brain and Cognitive Sciences Evelina Fedorenko, John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology, and Rebecca Saxe, the John W. Jarve (1978) Professor of Brain and Cognitive Sciences  teamed up to share brain scans from children, adolescents, and adults and compare how their brains responded to language.

In studies aimed at better understanding a variety of cognitive functions and developmental disorders, the three teams had all collected functional MRI data while subjects participated in “language localizer” tasks—an approach the Fedorenko lab developed to map the language-processing network in a person’s brain. By monitoring brain activity with functional MRI as people engage in both language tasks and non-linguistic tasks, researchers can identify parts of the brain that are exclusively dedicated to language processing, whose precise anatomic location varies across individuals.

To activate the language network, the researchers had children listen to stories inside the MRI scanner. Depending on their age, some heard excerpts of Alice in Wonderland, some listened to podcasts and TED talks, and others heard shorter, simpler stories. To watch their brains during a non-linguistic task, the researchers had the children listen to nonsense words.

Across the data from the three labs, which included children between the ages of four and 16, as well as adults for comparison, the team saw clear developmental changes in the brain’s response to language. “The integration of the system—how well different subregions of the system correlated with each other and worked together during language processing—was stronger in older children as compared to younger children,” says Ola Ozernov-Palchik, a research scientist in Gabrieli’s lab and a research assistant professor at Boston University. The system was also more strongly activated by language in older children, which may reflect their growing comprehension of what they hear.

But strikingly, almost all language processing happened on the left side of the brain, even in the youngest subjects. “From age four on, it seems just as lateralized as in an adult,” Gabrieli says.

Language and developmental disorders

The researchers say this finding has implications for understanding developmental conditions that impact language, including autism and dyslexia. The right side of the brain frequently gets more involved in language processing in people with these conditions than it does in typically developing children. “Almost every single developmental disorder that’s associated with language has a theory that’s related to language lateralization,” Ozernov-Palchik says.

The reason for more bilateral language processing in some disorders is debated. One idea has been that some people might use both sides of their brain for language processing because their brains are less mature. If the right side of the brain processes language early in life, scientists had reasoned, it might simply continue to do so for longer in people with autism or dyslexia than it does in neurotypical individuals. But if most people use the left side of their brains for language even when they are young, the difference can’t be attributed to a developmental delay. Other developmental differences might cause bilateral language processing instead.

The researchers don’t have the full picture yet; they still need to know what parts of the brain process language in children younger than four. Likewise, they would like to know what the brain areas that become the language network are doing in the first months of life, when infants aren’t using language yet. They are eager to find out, both to understand fundamentals of brain development and to shed light on developmental disorders. “I think understanding that normal trajectory is really critical for interpreting what a deviation from that trajectory is,” says Amanda O’Brien, a former graduate student in Gabrieli’s lab who is now a postdoctoral fellow at Harvard.

One reason people thought lateralization might develop gradually is because damage to the left hemisphere of the brain impacts language abilities differently, depending on when it occurs. “If you have damage to the left hemisphere as an adult, you’re very likely to end up with some form of aphasia, at least temporarily,” Fedorenko explains. “But a lot of the time, with early damage to the left hemisphere, you grow up and you’re totally fine. The language can just develop in the right hemisphere.”

Some scientists suspected that the right side of the brain was able to take over language processing in children who suffered early-life brain damage because it was already participating in this function at the time. But the team’s findings suggest the developing brain may be nimbler than that. “Our data tell you that this early plasticity apparently happens in spite of the fact that by age four, we see these very strongly lateralized responses already,” Fedorenko says.

Brighter MRI signals

by Jennifer Michalowski |

When doctors and scientists want to see inside a body, magnetic resonance imaging (MRI) is a powerful tool. MRI can noninvasively capture detailed images of the body’s muscles, organs, and bones. It can monitor blood flow to generate a map of brain activity. And with new sensors developed by bioengineers at MIT, MRI can track the kinds of molecules that make our brains and bodies work.

In the May 13, 2026, issue of the journal Nature Biomedical Engineering, a team led by Alan Jasanoff, the Eugene McDermott Professor in the Brain Sciences and Human Behavior at MIT reports on their new sensors, which can brighten or dim MRI signals in response to specific molecular targets. The probes are designed to amplify the effect that each target molecule has on MRI signal, dramatically improving sensitivity over previous small-molecule sensors. Jasanoff, who is also an associate investigator at the McGovern Institute for Brain Research, says the approach his team used should enable the development of MRI sensors that detect neurotransmitters and other important molecules in the brain.

“We want to be able to measure distinct chemical signals like neurotransmitters, neuropeptides, and metabolites as they fluctuate across the whole brain,” Jasanoff says.

“These chemicals are important ingredients in neural computations, and we want to use the types of probes that we developed to detect these signals dynamically.”

Engineered nanoparticles

Jasanoff explains that researchers have struggled to use MRI to sensitively detect small molecules in the brain because the amount of any given neurochemical is low. Sensors can be designed to change the brightness of an MRI signal in the presence of specific molecules—but it takes a lot of contrast agent to achieve this. If every molecule of contrast agent needs its own target molecule to activate it, low concentrations of the target molecule limit the sensors’ visibility in an MRI scan. “The signal change that you see in the imaging will be very modest,” Jasanoff says. “It won’t let us detect physiological events.”

The Jasanoff team’s new sensors, whose development was led by postdoctoral researcher Sayani Das and graduate student Jacob Cyert Simon, overcome this problem. To generate a greater signal change in response to target molecules, the researchers designed probes in which a single target molecule impacts not one contrast agent, but many.

To achieve this, Das and Simon packaged an MRI contrast agent inside tiny sacs called liposomal nanoparticles. Each nanoparticle is packed with many molecules of gadolinium, a magnetic material that brightens the MRI signal that arises from hydrogen atoms in water. Inside their protective sacs, gadolinium has no effect on MRI signal, unless water molecules can easily get in and out.

Das and Simon built water channels into the walls of their gadolinium-filled nanoparticles, engineering them so that their opening depends on the presence or absence of a target molecule. When the channels open, more water enters and the gadolinium brightens the local MRI signal, lighting up that spot in a scan.

LisNR architecture consisting of an MRI contrast agent (gadoteridol) enclosed in a liposomal membrane (grey) perforated by water permeable pores (orange). Image courtesy of the researchers.

The researchers call their target-responsive sensors liposomal nanoparticle reporters, or LisNRs (pronounced “listeners”). They designed LisNRs that let water in only in the presence of their target molecule. The water channels in these nanoparticles stay blocked until they encounter their target, which can knock aside a channel-blocking bit of protein. Once the channel blocker is displaced, water enters and MRI signal brightens. They also made LisNRs that dim the MRI signal in the presence of the molecule they are designed to detect. These have a channel that stays open until the target molecule comes along and blocks it, keeping water out. Jasanoff lab members Vinay Sharma, Samira Abozeid, and Gregory Thiabaud played key roles in understanding and optimizing these interactions, and collaborators in the laboratory of Masayuki Inoue at the University of Tokyo helped the group engineer channels with higher potency.

In experiments led by postdoctoral researcher Miranda Dawson, Jasanoff’s team used their LisNRs to detect a molecule called biotin in the brains and bodies of living rats, illustrating the probe’s amplifying effects. “We showed that we could detect micromolar-scale levels of biotin with about tenfold greater sensitivity than we would have if we’d used a more conventional, one-to-one type sensing approach,” Jasanoff says. He adds that the team’s modeling suggests that with further development, they may be able to achieve even greater sensitivity gains.

The group showed that the new sensors can be delivered systemically, reaching various organs and spreading throughout the brain. This makes them promising tools for brain-wide imaging, as well as imaging targets in the peripheral nervous system or other tissues.

A next step will be engineering LisNRs that respond to the specific neurochemicals that Jasanoff and his team hope to study. “There are something like 100 neurochemicals in the brain that we’d love to detect in principle,” he says. They’ll start with dopamine and glutamate—two important and relatively abundant molecules that mediate communications between neurons.

This research, including support for postdoctoral fellows and graduate students involved in the work, was funded in part by Lore Harp McGovern, Yang Tan Collective at MIT, K. Lisa Yang Brain-Body Center at MIT, Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT, and K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT.

MIT Scientists Sven Dorkenwald and Whitney Henry named 2026 Searle Scholars

by Julie Pryor |

MIT scientists Sven Dorkenwald and Whitney Henry have been named 2026 Searle Scholars, an award given annually to 15 exceptional early-career researchers in the fields of biomedical sciences and chemistry. Chosen by a scientific advisory board, Searle Scholars are considered among the most creative young researchers pursuing high-risk/high-reward research. The Searle Scholars Program is funded through the Searle Funds at The Chicago Community Trust and administered by Kinship Foundation.

Dorkenwald is an assistant professor of brain and cognitive sciences and an investigator at the McGovern Institute for Brain Research. Henry is the Robert A. Swanson (1969) Career Development Professor of Life Sciences and an intramural faculty member at the Koch Institute for Integrative Cancer Research. They will each receive $450,000 in flexible funding to support their work over the next three years.

Sven Dorkenwald

Sven Dorkenwald is a computational neuroscientist investigating the organizational principles of neuronal circuits. The synaptic connectivity of neurons, their connectome, is fundamental to how networks of neurons function. Dorkenwald develops computational and collaborative tools to map, analyze, and interpret synapse-resolution connectomes. His work has led to large connectomic reconstructions of the fruit fly brain and parts of mammalian brains. He uses these connectomes to investigate the architecture of neuronal circuits and how their structure supports complex computations.

“As I establish my new lab, the Searle Scholars Award will help us launch ambitious projects and set our long-term scientific direction,” said Dorkenwald. “I am deeply grateful for the support from the Kinship Foundation and look forward to interacting with this amazing cohort of Searle Scholars.”

Dorkenwald joined the faculty of MIT in 2026 as an assistant professor in the Department of Brain and Cognitive Sciences and an investigator at the McGovern Institute. He earned a BS in physics and an MS in computer engineering from the University of Heidelberg, followed by a PhD in computer science and neuroscience at Princeton University in 2023 under the mentorship of Sebastian Seung and Mala Murthy. Dorkenwald completed his postdoctoral training as a Shanahan Research Fellow at the Allen Institute and the University of Washington, while serving as a Visiting Faculty Researcher at Google Research.

Whitney Henry

Whitney Henry investigates the potential of ferroptosis, an iron-dependent form of cell death, for developing novel therapies that target subpopulations of cancer cells that are highly metastatic, therapy-resistant, and therefore critical instigators of tumor relapse. Her research is focused on uncovering the molecular factors influencing ferroptosis susceptibility, investigating its effects on the tumor microenvironment, and developing innovative methods to manipulate ferroptosis resistance in living organisms, drawing from functional genomics, metabolomics, bioengineering, and a range of in vitro and in vivo models.

“I am incredibly grateful to the Kinship Foundation for supporting our research and giving us the freedom to ask bold, curiosity-driven scientific questions,” said Henry. “This support allows us to pursue ambitious ideas, take creative risks, and embark on new research directions.”

Henry joined the MIT faculty in 2024 as an assistant professor in the Department of Biology and a member of the Koch Institute, and is currently an HHMI Freeman Hrabowski Scholar. She received her bachelor’s degree in biology with a minor in chemistry from Grambling State University and her PhD from Harvard University. Following her doctoral studies, she worked in the lab of Robert Weinberg at the Whitehead Institute and was supported by fellowships from the Jane Coffin Childs Memorial Fund for Medical Research and the Ludwig Center at MIT.

Michale Fee and Fan Wang Elected to the National Academy of Sciences

by Julie Pryor |

Michale Fee, the Glen V. and Phyllis F. Dorflinger Professor of Neuroscience and head of the Department of Brain and Cognitive Sciences, and Fan Wang, a professor of brain and cognitive sciences, have been elected to join the National Academy of Sciences (NAS). Fee and Wang, who are also investigators at the McGovern Institute for Brain Research, were elected by current NAS members in recognition of their “distinguished and continuing achievements in original research.”

The NAS is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — with the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations. This year, the NAS elected 120 members and 25 international members, including six MIT faculty, bringing the total number of active members to 2,705.

“Election to the National Academy of Sciences by one’s peers is a great honor for a scientist in the United States,” says McGovern Institute Director Robert Desimone. “Michale and Fan represent the very best of our research community and we are tremendously proud of their accomplishments and this well-deserved recognition.”

Michale Fee’s research explores how the brain learns and generates complex sequential behaviors.  Using the zebra finch as a model system, Fee investigates the neural mechanisms underlying birdsong—a behavior that young birds learn from their fathers through trial and error, much as human infants learn to speak through babbling. His work has revealed that a brain region called the higher vocal center (HVC) functions like an orchestra conductor, precisely controlling the tempo and timing of song production. Other work from his lab has shown how this same circuit helps to store a memory of the father’s song, how baby birds babble in order to practice their song, and how this vocal practice is translated to song learning by listening to themselves sing.

These findings extend far beyond birdsong—the neural circuits controlling birdsong learning are closely related to human brain circuits disrupted in Parkinson’s and Huntington’s disease. Insights from Fee’s research could reveal new clues to the causes and potential treatments of these complex brain disorders.

Fee’s appointment in 2021 as head of the Department of Brain and Cognitive Sciences continues the department’s tradition of being led by scientists whose exemplary work makes MIT a world leader in brain science.

Fan Wang investigates the neural circuits that govern the dynamic interactions between brain and body, exploring how the brain generates sensory perceptions and controls movement. Wang, who is also the co-director of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics, uses cutting-edge techniques including optogenetics, in vivo electrophysiology, and in vivo imaging, to make discoveries with profound clinical implications.

By developing innovative tools to study how brain circuits work, Wang discovered distinct populations of neurons activated by anesthesia that can suppress pain without blocking sensation, and can calm anxiety by regulating automatic body functions like heart rate. She also identified the brain circuits controlling rhythmic movements essential for exploration and communication. Together, these findings reveal how emotion, physiology, movement, and consciousness are deeply interconnected.

Wang combines rigorous basic neuroscience with a commitment to translating her discoveries into therapies that relieve human suffering. Her election to the NAS recognizes her contributions to understanding the brain-body connection and therapeutic potential of her groundbreaking research.

The formal induction ceremony for new NAS members, during which they sign the ledger whose first signatory is Abraham Lincoln, will be held at the Academy’s annual meeting in Washington D.C. next spring.