Author: Julie Pryor

Can we think without language?

by Anna Ivanova |

As part of our Ask the Brain series, Anna Ivanova, a graduate student who studies how the brain processes language in the labs of Nancy Kanwisher and Evelina Fedorenko, answers the question, “Can we think without language?”

Anna Ivanova headshot
Graduate student Anna Ivanova studies language processing in the brain.

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Imagine a woman – let’s call her Sue. One day Sue gets a stroke that destroys large areas of brain tissue within her left hemisphere. As a result, she develops a condition known as global aphasia, meaning she can no longer produce or understand phrases and sentences. The question is: to what extent are Sue’s thinking abilities preserved?

Many writers and philosophers have drawn a strong connection between language and thought. Oscar Wilde called language “the parent, and not the child, of thought.” Ludwig Wittgenstein claimed that “the limits of my language mean the limits of my world.” And Bertrand Russell stated that the role of language is “to make possible thoughts which could not exist without it.” Given this view, Sue should have irreparable damage to her cognitive abilities when she loses access to language. Do neuroscientists agree? Not quite.

Neuroimaging evidence has revealed a specialized set of regions within the human brain that respond strongly and selectively to language.

This language system seems to be distinct from regions that are linked to our ability to plan, remember, reminisce on past and future, reason in social situations, experience empathy, make moral decisions, and construct one’s self-image. Thus, vast portions of our everyday cognitive experiences appear to be unrelated to language per se.

But what about Sue? Can she really think the way we do?

While we cannot directly measure what it’s like to think like a neurotypical adult, we can probe Sue’s cognitive abilities by asking her to perform a variety of different tasks. Turns out, patients with global aphasia can solve arithmetic problems, reason about intentions of others, and engage in complex causal reasoning tasks. They can tell whether a drawing depicts a real-life event and laugh when in doesn’t. Some of them play chess in their spare time. Some even engage in creative tasks – a composer Vissarion Shebalin continued to write music even after a stroke that left him severely aphasic.

Some readers might find these results surprising, given that their own thoughts seem to be tied to language so closely. If you find yourself in that category, I have a surprise for you – research has established that not everybody has inner speech experiences. A bilingual friend of mine sometimes gets asked if she thinks in English or Polish, but she doesn’t quite get the question (“how can you think in a language?”). Another friend of mine claims that he “thinks in landscapes,” a sentiment that conveys the pictorial nature of some people’s thoughts. Therefore, even inner speech does not appear to be necessary for thought.

Have we solved the mystery then? Can we claim that language and thought are completely independent and Bertrand Russell was wrong? Only to some extent. We have shown that damage to the language system within an adult human brain leaves most other cognitive functions intact. However, when it comes to the language-thought link across the entire lifespan, the picture is far less clear. While available evidence is scarce, it does indicate that some of the cognitive functions discussed above are, at least to some extent, acquired through language.

Perhaps the clearest case is numbers. There are certain tribes around the world whose languages do not have number words – some might only have words for one through five (Munduruku), and some won’t even have those (Pirahã). Speakers of Pirahã have been shown to make mistakes on one-to-one matching tasks (“get as many sticks as there are balls”), suggesting that language plays an important role in bootstrapping exact number manipulations.

Another way to examine the influence of language on cognition over time is by studying cases when language access is delayed. Deaf children born into hearing families often do not get exposure to sign languages for the first few months or even years of life; such language deprivation has been shown to impair their ability to engage in social interactions and reason about the intentions of others. Thus, while the language system may not be directly involved in the process of thinking, it is crucial for acquiring enough information to properly set up various cognitive domains.

Even after her stroke, our patient Sue will have access to a wide range of cognitive abilities. She will be able to think by drawing on neural systems underlying many non-linguistic skills, such as numerical cognition, planning, and social reasoning. It is worth bearing in mind, however, that at least some of those systems might have relied on language back when Sue was a child. While the static view of the human mind suggests that language and thought are largely disconnected, the dynamic view hints at a rich nature of language-thought interactions across development.

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Do you have a question for The Brain? Ask it here.

Ed Boyden elected to National Academy of Sciences

by Sabbi Lall |

Ed Boyden has been elected to join the National Academy of Sciences (NAS). The organization, established by an act of Congress during the height of the Civil War, was founded to provide independent and objective advice on scientific matters to the nation, and is actively engaged in furthering science in the United States. Each year NAS members recognize fellow scientists through election to the academy based on their distinguished and continuing achievements in original research.

“I’m very honored and grateful to have been elected to the NAS,” says Boyden. “This is a testament to the work of many graduate students, postdoctoral scholars, research scientists, and staff at MIT who have worked with me over the years, and many collaborators and friends at MIT and around the world who have helped our group on this mission to advance neuroscience through new tools and ways of thinking.”

Boyden’s research creates and applies technologies that aim to expand our understanding of the brain. He notably co-invented optogenetics as an independent side collaboration, conducted in parallel to his PhD studies, a game-changing technology that has revolutionized neurobiology. This technology uses targeted expression of light-sensitive channels and pumps to activate or suppress neuronal activity in vivo using light. Optogenetics quickly swept the field of neurobiology and has been leveraged to understand how specific neurons and brain regions contribute to behavior and to disease.

His research since has an overarching focus on understanding the brain. To this end, he and his lab have the ambitious goal of developing technologies that can map, record, and manipulate the brain. This has led, as selected examples, to the invention of expansion microscopy, a super-resolution imaging technology that can capture neuron’s microstructures and reveal their complex connections, even across large-scale neural circuits; voltage-sensitive fluorescent reporters that allow neural activity to be monitored in vivo; and temporal interference stimulation, a non-invasive brain stimulation technique that allows selective activation of subcortical brain regions.

“We are all incredibly happy to see Ed being elected to the academy,” says Robert Desimone, director of the McGovern Institute for Brain Research at MIT. “He has been consistently innovative, inventing new ways of manipulating and observing neurons that are revolutionizing the field of neuroscience.”

This year the NAS, an organization that includes over 500 Nobel Laureates, elected 100 new members and 25 foreign associates. Three MIT professors were elected this year, with Paula T. Hammond (David H. Koch (1962) Professor of Engineering and Department Head, Chemical Engineering) and Aviv Regev (HHMI Investigator and Professor in the Department of Biology) being elected alongside Boyden. Boyden becomes the seventh member of the McGovern Institute faculty to join the National Academy of Sciences.

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.

 

 

 

 

 

 

 

 

Algorithms of intelligence

by Sabbi Lall |

The following post is adapted from a story featured in a recent Brain Scan newsletter.

Machine vision systems are more and more common in everyday life, from social media to self-driving cars, but training artificial neural networks to “see” the world as we do—distinguishing cyclists from signposts—remains challenging. Will artificial neural networks ever decode the world as exquisitely as humans? Can we refine these models and influence perception in a person’s brain just by activating individual, selected neurons? The DiCarlo lab, including CBMM postdocs Kohitij Kar and Pouya Bashivan, are finding that we are surprisingly close to answering “yes” to such questions, all in the context of accelerated insights into artificial intelligence at the McGovern Institute for Brain Research, CBMM, and the Quest for Intelligence at MIT.

Precision Modeling

Beyond light hitting the retina, the recognition process that unfolds in the visual cortex is key to truly “seeing” the surrounding world. Information is decoded through the ventral visual stream, cortical brain regions that progressively build a more accurate, fine-grained, and accessible representation of the objects around us. Artificial neural networks have been modeled on these elegant cortical systems, and the most successful models, deep convolutional neural networks (DCNNs), can now decode objects at levels comparable to the primate brain. However, even leading DCNNs have problems with certain challenging images, presumably due to shadows, clutter, and other visual noise. While there’s no simple feature that unites all challenging images, the quest is on to tackle such images to attain precise recognition at a level commensurate with human object recognition.

“One next step is to couple this new precision tool with our emerging understanding of how neural patterns underlie object perception. This might allow us to create arrangements of pixels that look nothing like, for example, a cat, but that can fool the brain into thinking it’s seeing a cat.”- James DiCarlo

In a recent push, Kar and DiCarlo demonstrated that adding feedback connections, currently missing in most DCNNs, allows the system to better recognize objects in challenging situations, even those where a human can’t articulate why recognition is an issue for feedforward DCNNs. They also found that this recurrent circuit seems critical to primate success rates in performing this task. This is incredibly important for systems like self-driving cars, where the stakes for artificial visual systems are high, and faithful recognition is a must.

Now you see it

As artificial object recognition systems have become more precise in predicting neural activity, the DiCarlo lab wondered what such precision might allow: could they use their system to not only predict, but to control specific neuronal activity?

To demonstrate the power of their models, Bashivan, Kar, and colleagues zeroed in on targeted neurons in the brain. In a paper published in Science, they used an artificial neural network to generate a random-looking group of pixels that, when shown to an animal, activated the team’s target, a target they called “one hot neuron.” In other words, they showed the brain a synthetic pattern, and the pixels in the pattern precisely activated targeted neurons while other neurons remained relatively silent.

These findings show how the knowledge in today’s artificial neural network models might one day be used to noninvasively influence brain states with neural resolution. Such precise systems would be useful as we look to the future, toward visual prosthetics for the blind. Such a precise model of the ventral visual stream would have been incon-ceivable not so long ago, and all eyes are on where McGovern researchers will take these technologies in the coming years.

Recurrent architecture enhances object recognition in brain and AI

by Sabbi Lall |

Your ability to recognize objects is remarkable. If you see a cup under unusual lighting or from unexpected directions, there’s a good chance that your brain will still compute that it is a cup. Such precise object recognition is one holy grail for AI developers, such as those improving self-driving car navigation. While modeling primate object recognition in the visual cortex has revolutionized artificial visual recognition systems, current deep learning systems are simplified, and fail to recognize some objects that are child’s play for primates such as humans. In findings published in Nature Neuroscience, McGovern Investigator James DiCarlo and colleagues have found evidence that feedback improves recognition of hard-to-recognize objects in the primate brain, and that adding feedback circuitry also improves the performance of artificial neural network systems used for vision applications.

Deep convolutional neural networks (DCNN) are currently the most successful models for accurately recognizing objects on a fast timescale (<100 ms) and have a general architecture inspired by the primate ventral visual stream, cortical regions that progressively build an accessible and refined representation of viewed objects. Most DCNNs are simple in comparison to the primate ventral stream however.

“For a long period of time, we were far from an model-based understanding. Thus our field got started on this quest by modeling visual recognition as a feedforward process,” explains senior author DiCarlo, who is also the head of MIT’s Department of Brain and Cognitive Sciences and Research Co-Leader in the Center for Brains, Minds, and Machines (CBMM). “However, we know there are recurrent anatomical connections in brain regions linked to object recognition.”

Think of feedforward DCNNs and the portion of the visual system that first attempts to capture objects as a subway line that runs forward through a series of stations. The extra, recurrent brain networks are instead like the streets above, interconnected and not unidirectional. Because it only takes about 200 ms for the brain to recognize an object quite accurately, it was unclear if these recurrent interconnections in the brain had any role at all in core object recognition. For example, perhaps those recurrent connections are only in place to keep the visual system in tune over long periods of time. For example, the return gutters of the streets help slowly clear it of water and trash, but are not strictly needed to quickly move people from one end of town to the other. DiCarlo, along with lead author and CBMM postdoc Kohitij Kar, set out to test whether a subtle role of recurrent operations in rapid visual object recognition was being overlooked.

Challenging recognition

The authors first needed to identify objects that are trivially decoded by the primate brain, but are challenging for artificial systems. Rather than trying to guess why deep learning was having problems recognizing an object (is it due to clutter in the image? a misleading shadow?), the authors took an unbiased approach that turned out to be critical.

Kar explained further that “we realized that AI-models actually don’t have problems with every image where an object is occluded or in clutter. Humans trying to guess why AI models were challenged turned out to be holding us back.”

Instead, the authors presented the deep learning system, as well as monkeys and humans, with images, homing in on “challenge images” where the primates could easily recognize the objects in those images, but a feed forward DCNN ran into problems. When they, and others, added appropriate recurrent processing to these DCNNs, object recognition in challenge images suddenly became a breeze.

Processing times

Kar used neural recording methods with very high spatial and temporal precision to whether these images were really so trivial for primates. Remarkably, they found that though challenge images had initially appeared to be child’s play to the human brain, they actually involve extra neural processing time (about additional 30 milliseconds), suggesting that recurrent loops operate in our brain too.

 “What the computer vision community has recently achieved by stacking more and more layers onto artificial neural networks, evolution has achieved through a brain architecture with recurrent connections.” — Kohitij Kar

Diane Beck, Professor of Psychology and Co-chair of the Intelligent Systems Theme at the Beckman Institute and not an author on the study, explained further. “Since entirely feed forward deep convolutional nets are now remarkably good at predicting primate brain activity, it raised questions about the role of feedback connections in the primate brain. This study shows that, yes, feedback connections are very likely playing a role in object recognition after all.”

What does this mean for a self-driving car? It shows that deep learning architectures involved in object recognition need recurrent components if they are to match the primate brain, and also indicates how to operationalize this procedure for the next generation of intelligent machines.

“Recurrent models offer predictions of neural activity and behavior over time,” says Kar. “We may now be able to model more involved tasks. Perhaps one day, the systems will not only recognize an object, such as a person, but also perform cognitive tasks that the human brain so easily manages, such as understanding the emotions of other people.”

This work was supported by the Office of Naval Research grant MURI-114407 (J.J.D.). Center for Brains, Minds, and Machines (CBMM) funded by NSF STC award CCF-1231216 (K.K.).

Why is the brain shaped like it is?

by Sabbi Lall |

The human brain has a very striking shape, and one feature stands out large and clear: the cerebral cortex with its stereotyped pattern of gyri (folds and convolutions) and sulci (fissures and depressions). This characteristic folded shape of the cortex is a major innovation in evolution that allowed an increase in the size and complexity of the human brain.

How the brain adopts these complex folds is surprisingly unclear, but probably involves both shape changes and movement of cells. Mechanical constraints within the overall tissue, and imposed by surrounding tissues also contribute to the ultimate shape: the brain has to fit into the skull after all. McGovern postdoc Jonathan Wilde has a long-term interest in studying how the brain develops, and explained to us how the shape of the brain initially arises.

In the case of humans, our historical reliance upon intelligence has driven a massive expansion of the cerebral cortex.

“Believe it or not, all vertebrate brains begin as a flat sheet of epithelial cells that folds upon itself to form a tube,” explains Wilde. “This neural tube is made up of a single layer of neural stem cells that go through a rapid and highly orchestrated process of expansion and differentiation, giving rise to all of the neurons in the brain. Throughout the first steps of development, the brains of most vertebrates are indistinguishable from one another, but the final shape of the brain is highly dependent upon the organism and primarily reflects that organism’s lifestyle, environment, and cognitive demands.”

So essentially, the brain starts off as a similar shape for creatures with spinal cords. But why is the human brain such a distinct shape?

“In the case of humans,” explains Wilde, “our historical reliance upon intelligence has driven a massive expansion of the cerebral cortex, which is the primary brain structure responsible for critical thinking and higher cognitive abilities. Accordingly, the human cortex is strikingly large and covered in a labyrinth of folds that serve to increase its surface area and computational power.”

The anatomical shape of the human brain is striking, but it also helps researchers to map a hidden functional atlas: specific brain regions that selectively activate in fMRI when you see a face, scene, hear music and a variety of other tasks. I asked former McGovern graduate student, and current postdoc at Boston Children’s Hospital, Hilary Richardson, for her perspective on this more hidden structure in the brain and how it relates to brain shape.

Illustration of person rappelling into the brain's sylvian fissure.
The Sylvian fissure is a prominent groove on each side of the brain that separates the frontal and parietal lobes from the temporaal lobe. McGovern researchers are studying a region near the right Sylvian fissure, called the rTPJ, which is involved in thinking about what another person is thinking. Image: Joe Laney

“One of the most fascinating aspects of brain shape is how similar it is across individuals, even very young infants and children,” explains Richardson. “Despite the dramatic cognitive changes that happen across childhood, the shape of the brain is remarkably consistent. Given this, one open question is what kinds of neural changes support cognitive development. For example, while the anatomical shape and size of the rTPJ seems to stay the same across childhood, its response becomes more specialized to information about mental states – beliefs, desires, and emotions – as children get older. One intriguing hypothesis is that this specialization helps support social development in childhood.”

We’ll end with an ode to a prominent feature of brain shape: the “Sylvian fissure,” a prominent groove on each side of the brain that separates the frontal and parietal lobes from the temporal lobe. Such landmarks in brain shape help orient researchers, and the Sylvian fissure was recently immortalized in this image, from a postcard by illustrator Joe Laney.

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Do you have a question for The Brain? Ask it here.

 

Neuroscientists reverse some behavioral symptoms of Williams Syndrome

by Anne Trafton |

Williams Syndrome, a rare neurodevelopmental disorder that affects about 1 in 10,000 babies born in the United States, produces a range of symptoms including cognitive impairments, cardiovascular problems, and extreme friendliness, or hypersociability.

In a study of mice, MIT neuroscientists have garnered new insight into the molecular mechanisms that underlie this hypersociability. They found that loss of one of the genes linked to Williams Syndrome leads to a thinning of the fatty layer that insulates neurons and helps them conduct electrical signals in the brain.

The researchers also showed that they could reverse the symptoms by boosting production of this coating, known as myelin. This is significant, because while Williams Syndrome is rare, many other neurodevelopmental disorders and neurological conditions have been linked to myelination deficits, says Guoping Feng, the James W. and Patricia Poitras Professor of Neuroscience and a member of MIT’s McGovern Institute for Brain Research.

“The importance is not only for Williams Syndrome,” says Feng, who is one of the senior authors of the study. “In other neurodevelopmental disorders, especially in some of the autism spectrum disorders, this could be potentially a new direction to look into, not only the pathology but also potential treatments.”

Zhigang He, a professor of neurology and ophthalmology at Harvard Medical School, is also a senior author of the paper, which appears in the April 22 issue of Nature Neuroscience. Former MIT postdoc Boaz Barak, currently a principal investigator at Tel Aviv University in Israel, is the lead author and a senior author of the paper.

Impaired myelination

Williams Syndrome, which is caused by the loss of one of the two copies of a segment of chromosome 7, can produce learning impairments, especially for tasks that require visual and motor skills, such as solving a jigsaw puzzle. Some people with the disorder also exhibit poor concentration and hyperactivity, and they are more likely to experience phobias.

In this study, the researchers decided to focus on one of the 25 genes in that segment, known as Gtf2i. Based on studies of patients with a smaller subset of the genes deleted, scientists have linked the Gtf2i gene to the hypersociability seen in Williams Syndrome.

Working with a mouse model, the researchers devised a way to knock out the gene specifically from excitatory neurons in the forebrain, which includes the cortex, the hippocampus, and the amygdala (a region important for processing emotions). They found that these mice did show increased levels of social behavior, measured by how much time they spent interacting with other mice. The mice also showed deficits in fine motor skills and increased nonsocial related anxiety, which are also symptoms of Williams Syndrome.

Next, the researchers sequenced the messenger RNA from the cortex of the mice to see which genes were affected by loss of Gtf2i. Gtf2i encodes a transcription factor, so it controls the expression of many other genes. The researchers found that about 70 percent of the genes with significantly reduced expression levels were involved in the process of myelination.

“Myelin is the insulation layer that wraps the axons that extend from the cell bodies of neurons,” Barak says. “When they don’t have the right properties, it will lead to faster or slower electrical signal transduction, which affects the synchronicity of brain activity.”

Further studies revealed that the mice had only about half the normal number of mature oligodendrocytes — the brain cells that produce myelin. However, the number of oligodendrocyte precursor cells was normal, so the researchers suspect that the maturation and differentiation processes of these cells are somehow impaired when Gtf2i is missing in the neurons.

This was surprising because Gtf2i was not knocked out in oligodendrocytes or their precursors. Thus, knocking out the gene in neurons may somehow influence the maturation process of oligodendrocytes, the researchers suggest. It is still unknown how this interaction might work.

“That’s a question we are interested in, but we don’t know whether it’s a secreted factor, or another kind of signal or activity,” Feng says.

In addition, the researchers found that the myelin surrounding axons of the forebrain was significantly thinner than in normal mice. Furthermore, electrical signals were smaller, and took more time to cross the brain in mice with Gtf2i missing.

The study is an example of pioneering research into the contribution of glial cells, which include oligodendrocytes, to neuropsychiatric disorders, says Doug Fields, chief of the nervous system development and plasticity section of the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

“Traditionally myelin was only considered in the context of diseases that destroy myelin, such as multiple sclerosis, which prevents transmission of neural impulses. More recently it has become apparent that more subtle defects in myelin can impair neural circuit function, by causing delays in communication between neurons,” says Fields, who was not involved in the research.

Symptom reversal

It remains to be discovered precisely how this reduction in myelination leads to hypersociability. The researchers suspect that the lack of myelin affects brain circuits that normally inhibit social behaviors, making the mice more eager to interact with others.

“That’s probably the explanation, but exactly which circuits and how does it work, we still don’t know,” Feng says.

The researchers also found that they could reverse the symptoms by treating the mice with drugs that improve myelination. One of these drugs, an FDA-approved antihistamine called clemastine fumarate, is now in clinical trials to treat multiple sclerosis, which affects myelination of neurons in the brain and spinal cord. The researchers believe it would be worthwhile to test these drugs in Williams Syndrome patients because they found thinner myelin and reduced numbers of mature oligodendrocytes in brain samples from human subjects who had Williams Syndrome, compared to typical human brain samples.

“Mice are not humans, but the pathology is similar in this case, which means this could be translatable,” Feng says. “It could be that in these patients, if you improve their myelination early on, it could at least improve some of the conditions. That’s our hope.”

Such drugs would likely help mainly the social and fine-motor issues caused by Williams Syndrome, not the symptoms that are produced by deletion of other genes, the researchers say. They may also help treat other disorders, such as autism spectrum disorders, in which myelination is impaired in some cases, Feng says.

“We think this can be expanded into autism and other neurodevelopmental disorders. For these conditions, improved myelination may be a major factor in treatment,” he says. “We are now checking other animal models of neurodevelopmental disorders to see whether they have myelination defects, and whether improved myelination can improve some of the pathology of the defects.”

The research was funded by the Simons Foundation, the Poitras Center for Affective Disorders Research at MIT, the Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard, and the Simons Center for the Social Brain at MIT.

How our gray matter tackles gray areas

by Gina Vitale | MIT News correspondent |

When Katie O’Nell’s high school biology teacher showed a NOVA video on epigenetics after the AP exam, he was mostly trying to fill time. But for O’Nell, the video sparked a whole new area of curiosity.

She was fascinated by the idea that certain genes could be turned on and off, controlling what traits or processes were expressed without actually editing the genetic code itself. She was further excited about what this process could mean for the human mind.

But upon starting at MIT, she realized that she was less interested in the cellular level of neuroscience and more fascinated by bigger questions, such as, what makes certain people generous toward certain others? What’s the neuroscience behind morality?

“College is a time you can learn about anything you want, and what I want to know is why humans are really, really wacky,” she says. “We’re dumb, we make super irrational decisions, it makes no sense. Sometimes it’s beautiful, sometimes it’s awful.”

O’Nell, a senior majoring in brain and cognitive sciences, is one of five MIT students to have received a Marshall Scholarship this year. Her quest to understand the intricacies of the wacky human brain will not be limited to any one continent. She will be using the funding to earn her master’s in experimental psychology at Oxford University.

Chocolate milk and the mouse brain

O’Nell’s first neuroscience-related research experience at MIT took place during her sophomore and junior year, in the lab of Institute Professor Ann Graybiel at the McGovern Institute.

The research studied the neurological components of risk-vs-reward decision making, using a key ingredient: chocolate milk. In the experiments, mice were given two options — they could go toward the richer, sweeter chocolate milk, but they would also have to endure a brighter light. Or, they could go toward a more watered-down chocolate milk, with the benefit of a softer light. All the while, a fluorescence microscope tracked when certain cell types were being activated.

“I think that’s probably the closest thing I’ve ever had to a spiritual experience … watching this mouse in this maze deciding what to do, and watching the cells light up on the screen. You can see single-cell evidence of cognition going on. That’s just the coolest thing.”

In her junior spring, O’Nell delved even deeper into questions of morality in the lab of Professor Rebecca Saxe. Her research there centers on how the human brain parses people’s identities and emotional states from their faces alone, and how those computations are related to each other. Part of what interests O’Nell is the fact that we are constantly making decisions, about ourselves and others, with limited information.

“We’re always solving under uncertainty,” she says. “And our brain does it so well, in so many ways.”

International intrigue

Outside of class, O’Nell has no shortage of things to do. For starters, she has been serving as an associate advisor for a first-year seminar since the fall of her sophomore year.

“Basically it’s my job to sit in on a seminar and bully them into not taking seven classes at a time, and reminding them that yes, your first 8.01 exam is tomorrow,” she says with a laugh.

She has also continued an activity she was passionate about in high school — Model United Nations. One of the most fun parts for her is serving on the Historical Crisis Committee, in which delegates must try to figure out a way to solve a real historical problem, like the Cuban Missile Crisis or the French and Indian War.

“This year they failed and the world was a nuclear wasteland,” she says. “Last year, I don’t entirely know how this happened, but France decided that they wanted to abandon the North American theater entirely and just took over all of Britain’s holdings in India.”

She’s also part of an MIT program called the Addir Interfaith Fellowship, in which a small group of people meet each week and discuss a topic related to religion and spirituality. Before joining, she didn’t think it was something she’d be interested in — but after being placed in a first-year class about science and spirituality, she has found discussing religion to be really stimulating. She’s been a part of the group ever since.

O’Nell has also been heavily involved in writing and producing a Mystery Dinner Theater for Campus Preview Weekend, on behalf of her living group J Entry, in MacGregor House. The plot, generally, is MIT-themed — a physics professor might get killed by a swarm of CRISPR nanobots, for instance. When she’s not cooking up murder mysteries, she might be running SAT classes for high school students, playing piano, reading, or spending time with friends. Or, when she needs to go grocery shopping, she’ll be stopping by the Trader Joe’s on Boylston Avenue, as an excuse to visit the Boston Public Library across the street.

Quite excited for the future

O’Nell is excited that the Marshall Scholarship will enable her to live in the country that produced so many of the books she cherished as a kid, like “The Hobbit.” She’s also thrilled to further her research there. However, she jokes that she still needs to get some of the lingo down.

“I need to learn how to use the word ‘quite’ correctly. Because I overuse it in the American way,” she says.

Her master’s research will largely expand on the principles she’s been examining in the Saxe lab. Questions of morality, processing, and social interaction are where she aims to focus her attention.

“My master’s project is going to be basically taking a look at whether how difficult it is for you to determine someone else’s facial expression changes how generous you are with people,” she explains.

After that, she hopes to follow the standard research track of earning a PhD, doing postdoctoral research, and then entering academia as a professor and researcher. Teaching and researching, she says, are two of her favorite things — she’s excited to have the chance to do both at the same time. But that’s a few years ahead. Right now, she hopes to use her time in England to learn all she can about the deeper functions of the brain, with or without chocolate milk.

3Q: The interface between art and neuroscience

by Connie Blaszczyk | Center for Art, Science, and Technology |

CBMM postdoc Sarah Schwettman

Computational neuroscientist Sarah Schwettmann, who works in the Center for Brains, Minds, and Machines at the McGovern Institute, is one of three instructors behind the cross-disciplinary course 9.S52/9.S916 (Vision in Art and Neuroscience), which introduces students to core concepts in visual perception through the lenses of art and neuroscience.

Supported by a faculty grant from the Center for Art, Science and Technology at MIT (CAST) for the past two years, the class is led by Pawan Sinha, a professor of vision and computational neuroscience in the Department of Brain and Cognitive Sciences. They are joined in the course by Seth Riskin SM ’89, a light artist and the manager of the MIT Museum Studio and Compton Gallery, where the course is taught. Schwettman discussed the combination of art and science in an educational setting.

Q: How have the three of you approached this cross-disciplinary class in art and neuroscience?

A: Discussions around this intersection often consider what each field has to offer the other. We take a different approach, one I refer to as occupying the gap, or positioning ourselves between the two fields and asking what essential questions underlie them both. One question addresses the nature of the human relationship to the world. The course suggests one answer: This relationship is fundamentally creative, from the brain’s interpretation of incoming sensory data in perception, to the explicit construction of experiential worlds in art.

Neuroscience and art, therefore, each provide a set of tools for investigating different levels of the constructive process. Through neuroscience, we develop a specific understanding of the models of the world that the brain uses to make sense of incoming visual data. With articulation of those models, we can engineer types of inputs that interact with visual processing architecture in particularly exquisite ways, and do so reliably, giving artists a toolkit for remixing and modulating experience. In the studio component of the course, we experiment with this toolkit and collectively move it forward.

While designing the course, Pawan, Seth, and I found that we were each addressing a similar set of questions, the same that motivate the class, through our own research and practice. In parallel to computational vision research, Professor Sinha leads a humanitarian initiative called Project Prakash, which provides treatment to blind children in India and explores the development of vision following the restoration of sight. Where does structure in perception originate? As an artist in the MIT Museum Studio, Seth works with articulated light to sculpt structured visual worlds out of darkness. I also live on this interface where the brain meets the world — my research in the Department of Brain and Cognitive Sciences examines the neural basis of mental models for simulating physics. Linking our work in the course is an experiment in synthesis.

Q: What current research in vision, neuroscience, and art are being explored at MIT, and how does the class connect it to hands-on practice?

A: Our brains build a rich world of experience and expectation from limited and noisy sensory data with infinite potential interpretations. In perception research, we seek to discover how the brain finds more meaning in incoming data than is explained by the signal alone. Work being done at MIT around generative models addresses this, for instance in the labs of Josh Tenenbaum and Josh McDermott in the Department of Brain and Cognitive Sciences. Researchers present an ambiguous visual or auditory stimulus and by probing someone’s perceptual interpretation, they get a handle on the structures that the mind generates to interpret incoming data, and they can begin to build computational models of the process.

In Vision in Art and Neuroscience, we focus on the experiential as well as the experimental, probing the perceiver’s experience of structure-generating process—perceiving perception itself.

As instructors, we face the pedagogical question: what exercises, in the studio, can evoke so striking an experience of students’ own perception that cutting edge research takes on new meaning, understood in the immediacy of seeing? Later in the semester, students face a similar question as artists: How can one create visual environments where viewers experience their own perceptual processing at work? Done well, this experience becomes the artwork itself. Early in the course, students explore the Ganzfeld effect, popularized by artist James Turrell, where the viewer is exposed to an unstructured visual field of uniform illumination. In this experience, one feels the mind struggling to fit models of the world to unstructured input, and attempting this over and over again — an interpretation process which often goes unnoticed when input structure is expected by visual processing architecture. The progression of the course modules follows the hierarchy of visual processing in the brain, which builds increasingly complex interpretations of visual inputs, from brightness and edges to depth, color, and recognizable form.

MIT students first encounter those concepts in the seminar component of the course at the beginning of each week. Later in the week, students translate findings into experimental approaches in the studio. We work with light directly, from introducing a single pinpoint of light into an otherwise completely dark room, to building intricate environments using programmable electronics. Students begin to take this work into their own hands, in small groups and individually, culminating in final projects for exhibition. These exhibitions are truly a highlight of the course. They’re often one of the first times that students have built and shown artworks. That’s been a gift to share with the broader MIT community, and a great learning experience for students and instructors alike.

Q: How has that approach been received by the MIT community?

A: What we’re doing has resonated across disciplines: In addition to neuroscience, we have students and researchers joining us from computer science, mechanical engineering, mathematics, the Media Lab, and ACT (the Program in Art, Culture, and Technology). The course is growing into something larger, a community of practice interested in applying the scientific methodology we develop to study the world, to probe experience, and to articulate models for its generation and replication.

With a mix of undergraduates, graduates, faculty, and artists, we’ve put together installations and symposia — including three on campus so far. The first of these, “Perceiving Perception,” also led to a weekly open studio night where students and collaborators convene for project work. Our second exhibition, “Dessert of the Real,” is on display this spring in the Compton Gallery. This April we’re organizing a symposium in the studio featuring neuroscientists, computer scientists, artists and researchers from MIT and Harvard. We’re reaching beyond campus as well, through off-site installations, collaborations with museums — including the Metropolitan Museum of Art and the Peabody Essex Museum — and a partnership with the ZERO Group in Germany.

We’re eager to involve a broad network of collaborators. It’s an exciting moment in the fields of neuroscience and computing; there is great energy to build technologies that perceive the world like humans do. We stress on the first day of class that perception is a fundamentally creative act. We see the potential for models of perception to themselves be tools for scaling and translating creativity across domains, and for building a deeply creative relationship to our environment.

Halassa named Max Planck Fellow

by Sabbi Lall |

Michael Halassa was just appointed as one of the newest Max Planck Fellows. His appointment comes through the Max Planck Florida Institute for Neuroscience (MPFI), which aims to forge collaborations between exceptional neuroscientists from around the world to answer fundamental questions about brain development and function. The Max Planck Society selects cutting edge, active researchers from other institutions to fellow positions for a five-year period to promote interactions and synergies. While the program is a longstanding feature of the Max Planck Society, Halassa, and fellow appointee Yi Guo of the University of California, Santa Cruz, are the first selected fellows that are based at U.S. institutions.

Michael Halassa is an associate investigator at the McGovern Institute and an assistant professor in the Department of Brain and Cognitive Sciences at MIT. Halassa’s research focuses on the neural architectures that underlie complex cognitive processes. He is particularly interested in goal-directed attention, our ability to rapidly switch attentional focus based on high level objectives. For example, when you are in a roomful of colleagues, the mention of your name in a distant conversation can quickly trigger your ‘mind’s ear’ to eavesdrop into that conversation. This contrasts with hearing a name that sounds like yours on television, which does not usually grab your attention in the same way. In certain mental disorders such as schizophrenia, the ability to generate such high-level objectives, while also accounting for context, is perturbed. Recent evidence strongly suggests that impaired function of the prefrontal cortex and its interactions with a region of the brain called the thalamus may be altered in such disorders. It is this thalamocortical network that Halassa has been studying in mice, where his group has uncovered how the thalamus supports the ability of the prefrontal cortex to generate context-appropriate attentional signals.

The fellowship will support extending Halassa’s work into the tree shrew (Tupaia belangeri), which has been shown to have advanced cognitive abilities compared to mice while also offering many of the circuit-interrogation tools that make the mouse an attractive experimental model.

The Max Planck Florida Institute for Neuroscience (MPFI), a not-for-profit research organization, is part of the world-renowned Max Planck Society, Germany’s most successful research organization. The Max Planck Society was founded in 1911, and comprises 84 institutes and research facilities. While primarily located in Germany, there are 4 institutes and one research facility located aboard, including the Florida Institute that Halassa will collaborate with. The fellow positions were created with the goal of increasing interactions between the Max Planck Society and its institutes with faculty engaged in active research at other universities and institutions, which with this appointment now include MIT.

How the brain decodes familiar faces

by Sabbi Lall |

Our brains are incredibly good at processing faces, and even have specific regions specialized for this function. But what face dimensions are we observing? Do we observe general properties first, then look at the details? Or are dimensions such as gender or other identity details decoded interdependently? In a study published today in Nature Communications, the Kanwisher lab measured the response of the brain to faces in real time, and found that the brain first decodes properties such as gender and age before drilling down to the specific identity of the face itself.

While functional magnetic resonance imaging (fMRI) has revealed an incredible level of detail about which regions of the brain respond to faces, the technology is less effective at telling us when these brain regions become activated. This is because fMRI measures brain activity by detecting changes in blood flow; when neurons become active, local blood flow to those brain regions increases. However, fMRI works too slowly to keep up with the brain’s millisecond-by-millisecond dynamics. Enter magnetoencephalography (MEG), a technique developed by MIT physicist David Cohen that detects the minuscule fluctuations in magnetic field that occur with the electrical activity of neurons. This allows better temporal resolution of neural activity.

McGovern Investigator Nancy Kanwisher and postdoc Katharina Dobs, along with their co-authors Leyla Isik and Dimitrios Pantazis, selected this temporally precise approach to measure the time it takes for the brain to respond to different dimensional features of faces.

“From a brief glimpse of a face, we quickly extract all this rich multidimensional information about a person, such as their sex, age, and identity,” explains Dobs. “I wanted to understand how the brain accomplishes this impressive feat, and what the neural mechanisms are that underlie this effect, but no one had measured the time scales of responses to these features in the same study.”

Previous studies have shown that people with prosopagnosia, a condition characterized by the inability to identify familiar faces, have no trouble determining gender, suggesting these features may be independent. “But examining when the brain recognizes gender and identity, and whether these are interdependent features is less clear,” explains Dobs.

By recording the brain activity of subjects in the MEG, Dobs and her co-authors found that the brain responds to coarse features, such as the gender of a face, much faster than the identity of the face itself. Their data showed that, in as little as 60-70 milliseconds, the brain begins to decode the age and gender of a person. Roughly 30 milliseconds later — at around 90 milliseconds — the brain begins processing the identity of the face.

After establishing a paradigm for measuring responses to these face dimensions, the authors then decided to test the effect of familiarity. It’s generally understood that the brain processes information about “familiar faces” more robustly than unfamiliar faces. For example, our brains are adept at recognizing actress Scarlett Johansson across multiple photographs, even if her hairstyle is different in each picture. Our brains have a much harder time, however, recognizing two images of the same person if the face is unfamiliar.

“Actually, for unfamiliar faces the brain is easily fooled,” Dobs explains, “variations in images, shadows, changes in hair color or style, quickly lead us to think we are looking at a different person. Conversely, we have no problem if a familiar face is in shadow, or a friend changes their hair style. But we didn’t know why familiar face perception is much more robust, whether this is due to better feed forward processing, or based on later memory retrieval.”

Familiar and unfamiliar celebrity faces side by side
Perception of a familiar face, Scarlett Johansson, is more robust than for unfamiliar faces, in this study German celebrity Karoline Herfurth (images: Wikimedia commons).

To test the effect of familiarity, the authors measured brain responses while the subjects viewed familiar faces (American celebrities) and unfamiliar faces (German celebrities) in the MEG. Surprisingly, they found that subjects recognize gender more quickly in familiar faces than unfamiliar faces. For example our brains decode that actor Scarlett Johansson is female, before we even realize she is Scarlett Johansson. And for the less familiar German actor, Karoline Herfurth, our brains unpack the same information less well.

Dobs and co-authors argue that better gender and identity recognition is not “top-down” for familiar faces, meaning that improved responses to familiar faces is not about retrieval of information from memory, but rather, a feed-forward mechanism. They found that the brain responds to facial familiarity at a much slower time scale (400 milliseconds) than it responds to gender, suggesting that the brain may be remembering associations related to the face (Johansson = Lost in Translation movie) in that longer timeframe.

This is good news for artificial intelligence. “We are interested in whether feed-forward deep learning systems can learn faces using similar mechanisms,” explains Dobs, “and help us to understand how the brain can process faces it has seen before in the absence of pulling on memory.”

When it comes to immediate next steps, Dobs would like to explore where in the brain these facial dimensions are extracted, how prior experience affects the general processing of objects, and whether computational models of face processing can capture these complex human characteristics.