Research Area: Cognitive Neuroscience

​perception, memory, language, social cognition, attention, empathy, brain disorders, aging, cognitive flexibility, developing brain

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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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 does the brain focus?

by Sabbi Lall |

This is a very interesting question, and one that researchers at the McGovern Institute for Brain Research are actively pursuing. It’s also important for understanding what happens in conditions such as ADHD. There are constant distractions in the world, a cacophony of noise and visual stimulation. How and where we focus our attention, and what the brain attends to vs. treating as background information, is a big question in neuroscience. Thanks to work from researchers, including Robert Desimone, we understand quite a bit about how this works in the visual system in particular. What his lab has found is that when we pay attention to something specific, neurons in the visual cortex responding to the object we’re focusing upon fire in synchrony, whereas those responding to irrelevant information become suppressed. It’s almost as if this synchrony “increases the volume” so that the responding neurons rise above general noise.

Synchronized activity of neurons occurs as they oscillate together at a particular frequency, but the frequency of oscillation really matters when it comes to attention and focus vs. inattention and distraction. To find out more about this, I asked a postdoc in the Desimone lab, Yasaman Bagherzadeh about the role of different “brainwaves,” or oscillations at different frequencies, in attention.

“Studies in humans have shown that enhanced synchrony between neurons in the alpha range –8–12 Hz— is actually associated with inattention and distracting information,” explains Bagherzadeh, “whereas enhanced gamma synchrony (about 30-150 Hz) is associated with attention and focus on a target. For example, when a stimulus (through the ears or eyes) or its location (left vs. right) is intentionally ignored, this is preceded by a relative increase in alpha power, while a stimulus you’re attending to is linked to an increase in gamma power.”

Attention in the Desimone lab (no pun intended) has also recently been focused on covert attention. This type of spatial attention was traditionally thought to occur through a mental shift without a glance, but the Desimone lab recently found that even during these mental shifts, animal sneakily glance at objects that attention becomes focused on. Think now of something you know is nearby (a cup of coffee for example), but not in the center of your field of vision. Chances are that you just sneakily glanced at that object.

Previously these sneaky glances/small eye movements, called microsaccades (MS for short), were considered to be involuntary movements without any functional role. However, in the recent Desimone lab study, it was found that a MS significantly modulates neural activity during the attention period. This means that when you glance at something, even sneakily, it is intimately linked to attention. In other words, when it comes to spatial attention, eye movements seem to play a significant role.

Various questions arise about the mechanisms of spatial attention as a result this study, as outlined by Karthik Srinivasan, a postdoctoral associate in the Desimone lab.

“How are eye movement signals and attentional processing coordinated? What’s the role of the different frequencies of oscillation for such coordination? Is there a role for them or are they just the frequency domain representation (i.e., an epiphenomenon) of a temporal/dynamical process? Is attention a sustained process or rhythmic or something more dynamic?” Srinivasan lists some of the questions that come out of his study and goes on to explain the implications of the study further. “It is hard to believe that covert attention is a sustained process (the so-called ‘spotlight theory of attention’), given that neural activity during the attention period can be modulated by covert glances. A few recent studies have supported the idea that attention is a rhythmic process that can be uncoupled from eye movements. While this is an idea made attractive by its simplicity, it’s clear that small glances can affect neural activity related to attention, and MS are not rhythmic. More work is thus needed to get to a more unified theory that accounts for all of the data out there related to eye movements and their close link to attention.”

Answering some of the questions that Bagherzadeh, Srinivasan, and others are pursuing in the Desimone lab, both experimentally and theoretically, will clear up some of the issues above, and improve our understanding of how the brain focuses attention.

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

 

How motion conveys emotion in the face

by Sabbi Lall |

While a static emoji can stand in for emotion, in real life we are constantly reading into the feelings of others through subtle facial movements. The lift of an eyebrow, the flicker around the lips as a smile emerges, a subtle change around the eyes (or the sudden rolling of the eyes), are all changes that feed into our ability to understand the emotional state, and the attitude, of others towards us. Ben Deen and Rebecca Saxe have now monitored changes in brain activity as subjects followed face movements in movies of avatars. Their findings argue that we can generalize across individual face part movements in other people, but that a particular cortical region, the face-responsive superior temporal sulcus (fSTS), is also responding to isolated movements of individual face parts. Indeed, the fSTS seems to be tied to kinematics, individual face part movement, more than the implied emotional cause of that movement.

We know that the brain responds to dynamic changes in facial expression, and that these are associated with activity in the fSTS, but how do calculations of these movements play out in the brain?

Do we understand emotional changes by adding up individual features (lifting of eyebrows + rounding of mouth= surprise), or are we assessing the entire face in a more holistic way that results in more generalized representations? McGovern Investigator Rebecca Saxe and her graduate student Ben Deen set out to answer this question using behavioral analysis and brain imaging, specifically fMRI.

“We had a good sense of what stimuli the fSTS responds strongly to,” explains Ben Deen, “but didn’t really have any sense of how those inputs are processed in the region – what sorts of features are represented, whether the representation is more abstract or more tied to visual features, etc. The hope was to use multivoxel pattern analysis, which has proven to be a remarkably useful method for characterizing representational content, to address these questions and get a better sense of what the region is actually doing.”

Facial movements were conveyed to subjects using animated “avatars.” By presenting avatars that made isolated eye and eyebrow movements (brow raise, eye closing, eye roll, scowl) or mouth movements (smile, frown, mouth opening, snarl), as well as composites of these movements, the researchers were able to assess whether our interpretation of the latter is distinct from the sum of its parts. To do this, Deen and Saxe first took a behavioral approach where people reported on what combinations of eye and mouth movements in a whole avatar face, or one where the top and bottom parts of the face were misaligned. What they found was that movement in the mouth region can influence perception of movement in the eye region, arguably due to some level of holistic processing. The authors then asked whether there were cortical differences upon viewing isolated vs. combined face part movements. They found that changes in fSTS, but not other brain regions, had patterns of activity that seemed to discriminate between different facial movements. Indeed, they could decode which part of the avatar’s face is being perceived as moving from fSTS activity. The researchers could even model the fSTS response to combined features linearly based on the response to individual face parts. In short, though the behavorial data indicate that there is holistic processing of complex facial movement, it is also clear that isolated parts-based representations are also present, a sort of intermediate state.

As part of this work, Deen and Saxe took the important step of pre-registering their experimental parameters, before collecting any data, at the Open Science Framework. This step allows others to more easily reproduce the analysis they conducted, since all parameters (the task that subjects are carrying out, the number of subjects needed, the rationale for this number, and the scripts used to analyze data) are openly available.

“Preregistration had a big impact on our workflow for the study,” explained Deen. “More of the work was done up front, in coming up with all of the analysis details and agonizing over whether we were choosing the right strategy, before seeing any of the data. When you tie your hands by making these decisions up front, you start thinking much more carefully about them.”

Pre-registration does remove post-hoc researcher subjectivity from the analysis. As an example, because Deen and Saxe predicted that the people would be accurately able to discriminate between faces per se, they decided ahead of the experiment to focus on analyzing reaction time, rather than looking at the collected data and deciding to focus on this number after the fact. This adds to the overall objectivity of the experiment and is increasingly seen as a robust way to conduct such experiments.

Josh McDermott

The Science of Hearing

Hearing enables us to make sense of our whereabouts, understand the emotional state of others, and enjoy musical experiences. Acoustic information relays vital cues about the world—yet much of the sophisticated brain system that decodes this information is poorly understood.

Josh McDermott’s research is in search of foundational principles of sound perception. Groundbreaking discoveries from the McDermott lab have clarified how people hear and process sounds. His research informs new treatments for those with hearing loss, and paves the way for machine systems that emulate the human ability to recognize and interpret sound. McDermott’s lab has also pioneered new approaches for understanding music perception. His lab deconstructs the neural ensembles that allow us to appreciate music, while also studying the often striking variation that can occur across cultures.

Virtual Tour of McDermott Lab

Rebecca Saxe

Mind Reading

How do we think about the thoughts of other people? How are some thoughts universal and others specific to a culture or an individual?

Rebecca Saxe is tackling these and other thorny questions surrounding human thought in adults, children, and infants. Leveraging behavioral testing, brain imaging, and computational modeling, her lab is focusing on a diverse set of research questions including what people learn from punishment, the role of generosity in social relationships, and the navigation and language abilities in toddlers. The team is also using computational models to deconstruct complex thought processes, such as how humans predict the emotions of others. This research not only expands the junction of sociology and neuroscience, but also unravels—and gives clarity to—the social threads that form the fabric of society.

Virtual Tour of Saxe Lab

Mehrdad Jazayeri

Neurobiology of Mental Computations

How does the brain give rise to the mind? How do neurons, circuits, and synapses in the brain encode knowledge about objects, events, and other structural and causal relationships in the environment? Research in Mehrdad Jazayeri’s lab brings together ideas from cognitive science, neuroscience, and machine learning with experimental data in humans, animals, and computer models to develop a computational understanding of how the brain create internal representations, or models, of the external world.