CNBC faculty member and associate director (CMU) Wayne Wu has just completed a survey of the neuroscience of consciousness in a review article for the Stanford Encyclopedia of Philosophy. The comprehensive review clarifies how consciousness can be empirically dissected. For philosophers, it provides an introduction to some important empirical work. For neuroscientists, it will help orient them in a conceptually difficult area of investigation.

“Consciousness studies is often confusing and unruly,” says Wu. “I think it critical to highlight clear questions about consciousness that science can answer.”

In the article, Wu focuses on two basic questions. First, what differentiates states that are conscious from those that are not, what is called generic consciousness. Second, in a conscious state, what determines what one is conscious of, what is called specific consciousness. “These concrete questions provide an organizing principle for empirical research,” Wu continues. “There is a lot of illuminating work done that has helped us understand facets of consciousness, including here at the CNBC.”

For example, work by CNBC faculty members Julie Fiez and Avniel Ghuman have probed the role of the left midfusiform gyrus (lmFG) in word recognition and individuation, and demonstrated disruption of awareness of words by modulating activity in lmFG. “These sorts of direct manipulation, based on our understanding of underlying functional circuity and information processing, will be crucial to explaining specific conscious awareness,” Wu concludes.

Adolescence is a period of development where complex cognitive abilities like working-memory and inhibitory control mature, becoming more consistent and reliable. These improvements in cognition are occurring at the same time that brain structures that support these cognitive functions are undergoing continued developmental plasticity. Though this pattern of development has been well-described, the neurobiological mechanisms that drive adolescent developmental plasticity remain to be explained.

In a recent review in Neuroscience and Behavioral Reviews, “Adolescence as a neurobiological critical period for the development of higher-order cognition,” Bart Larsen (now a post-doc at the University of Pennsylvania) and Bea Luna (Pitt Psychiatry and CNBC) argue that the nature of these developmental changes can be understood as a critical period—a specific time window during which experience and neurobiological factors interact to shape normative brain development and profoundly alter behavior.

“To assess this hypothesis, we synthesize recent adolescent neurodevelopmental findings that span cellular, circuit, and systems levels and evaluate them through the lens of the established critical period mechanisms that are well-understood in guiding early sensory system development,” notes Larsen. “We discovered remarkable correspondence between adolescent neurodevelopmental processes and the mechanisms driving early critical period plasticity, supporting the hypothesis that adolescent development is driven by critical period mechanisms that guide the rapid development of neurobiology and cognitive ability during adolescence and their subsequent stability in adulthood.”

Larsen and Luna also highlight the role of dopamine as a potential trigger for the opening of adolescent critical period window. Understanding adolescence as a critical period not only provides a mechanism for healthy adolescent development, it provides a framework for understanding the role of experience and neurobiology in the emergence of psychopathology (such as schizophrenia, mood disorders, and anxiety disorders) that occurs during this developmental period.

The brain can anticipate stimuli that are behaviorally relevant, and attention can be deployed in advance of the stimuli’s appearance. The prevailing view is that attention works by acting as a sort of gain on neural activity leading to an amplification of a signal. This gain influence is thought to also apply to “spontaneous” activity before a stimulus appears in the neuron’s receptive field, putting the neuron into a more excited state. The problem, however, is that attention increasing spontaneous activity of a visual neuron might be interpreted by postsynaptic populations as the appearance of a weak visual stimulus when no stimulus is present. 

“Our alternative idea is that for every neuron that increases it’s firing rate with attention in anticipation of an imminent stimulus, there is another neuron in the population for which the firing rate actually decreases with anticipatory attention,” notes Adam Snyder (Rochester, Neuroscience), a former post-doc with Matt Smith (Ophthalmology and CNBC, Pitt) and Byron Yu (Biomedical Engineering, ECE and CNBC, CMU). “In this way, the total amount of activity across the population is unchanged, reducing the chance of sending the wrong message to downstream brain areas.”

In a recent paper published in Nature Communications, “Distinct population codes for attention in the absence and presence of visual stimulation”, the researchers’ experimental data provide support for this latter view, raising questions on the standard idea that attention is simply a gain on the neural population activity that is independent of the sensory context. An alternative is that attentional modulations are context dependent, differing between unstimulated and stimulated contexts.

Nine sample neurons that show suppression during the prestimulus interval (the red curve is below the blue curve) but then gain modulation once the stimulus is presented (the red curve is above the blue curve, stimulus presentation at time=0). The standard gain model predicts that the red curve is always above the blue curve.

 

The trial-to-trial variability of neuronal responses provides detailed information on how the circuit structure connecting neurons drives brain activity and behavior.  This idea combined with the widespread use of population recordings has prompted deep interest in how variability is distributed over a population.  A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional with all neurons fluctuating en masse.  Previous model cortical networks are at loss to explain this low dimensional shared variability. Rather than explain this variability within the network, these models typically explain it by appeal to external sources such as global alertness of the animal, wandering of attention, or just noise from other brain areas.

Further, attention-mediated modulations in population variability provide constraints on how shared variability is distributed within and between neuronal populations.  Attention reduces within area correlations (Cohen & Maunsell, 2009, Nat Neurosci 12, 1594–1600) while simultaneously increasing between area correlations (Ruff & Cohen, 2016, J Neurosci 36, 7523-7534).  In a recent paper in Neuron “Circuit Models of Low-Dimensional Shared Variability in Cortical Networks”, a collaboration between the labs of CNBC faculty Marlene Cohen and Brent Doiron, the authors show that such a differential correlation modulation is a difficult constraint to satisfy with a model where the source of the fluctuations are strictly external to the network.

Lead author, postdoctoral fellow Chengcheng Huang notes: “We argue that a global source of fluctuations is not able to explain the differential modulation of within and between area correlations and that the observed low-dimensional variability can be internally generated within the recurrent circuit.” The group develop spiking neuron network models where population-wide shared variability is internally generated.   

The authors studied networks of spiking neurons with spatially ordered connections, meaning that nearby neurons are connected with higher probability. These networks showed  that if the spatial and temporal scales of inhibitory coupling match known physiology in monkeys, the network exhibits spatiotemporal dynamics over large spatial scale, which results in low dimensional shared variability. This matches the observed variability of in vivo population recordings in cortex.   A top-down modulation of inhibitory neurons in the authors’ network provides a parsimonious mechanism for attentional modulation on both within and between area correlations, providing support for the author’s theory of cortical variability. The work provides a critical and previously missing mechanistic link between observed cortical circuit structure and realistic population-wide shared neuronal variability and its modulation.

Figure caption: Top-down depolarization of MT inhibitory neurons capture the differential attentional modulation of shared variability within and across V1 and MT (C, D) and attentional modulation on the low-dimensional structure of shared variability (E).

The Dynamics of the Unconscious Brain Under General Anesthesia

General anesthesia is a neurophysiological state in which a patient is rendered unconscious, insensitive to pain, amnestic and immobile, while being maintained physiologically stable. General anesthesia has been administered in the U.S. for more than 170 years, and daily, more than 700,000 people worldwide receive general anesthesia for surgery and invasive diagnostic procedures. The advent of general anesthesia transformed surgery from trauma and butchery to a humane and often life-saving therapy. The mechanism by which anesthetic drugs create the stated general anesthesia has been considered one of the most significant mysteries of modern medicine.

When: Thursday, January 31, 2019 at 4:30 PM – 5:30 PM EST
Where: Simmons Auditorium A, Tepper Building, 4765 Forbes Avenue, Pittsburgh, PA

A reception will immediately follow.

cmu.edu/dicksonprize
cmu.edu/uls

CNBC CMU faculty members Barbara Shinn-Cunningham, Lori Holt and Eric Yttri received grants from a gift to the Mellon College of Science from the DSF Charitable Foundation.

Next-generation High-density Digital Neural Interfaces Based on Scalable CMOS Technology for High-resolution Recording

Eric Yttri, assistant professor of biological sciences, Maysam Chamanzar, assistant professor of electrical and computer engineering, and Lawrence Pileggi, professor of electrical and computer engineering, will design and test high-density, flexible neural probes that can record and digitize neural signals from the central nervous system. Using complementary metal oxide–semiconductor technology and microelectromechanical systems fabrication, these probes will enable multi-scale, high-throughput and distributed neural recordings that can help researchers understand how the brain works in health, and how the brain doesn’t work right in people with neurological disorders.

Cognitive Processing in Human and Machine Learning

Barbara Shinn-Cunningham, director of the Carnegie Mellon University Neuroscience Institute and professor of psychology, electrical and computer engineering and biomedical engineering, and Lori Holt, professor of psychology, will organize a workshop to create an inclusive community focused on human cognitive auditory neuroscience. The Pittsburgh Cognitive Auditory Neuroscience Workshop will use brainstorming and problem-solving exercises along with planning sessions and the advisement of senior experts to chart future areas of research for the field and help create the collaborative networks necessary to grapple with those areas. Participants will also help produce an outreach module for middle- and high-school students about the “Science of Sound.”

More about the DSF Foundation grants can be read here: https://www.cmu.edu/mcs/news-events/2017/1015_DSF_Block_Grants.html

Speech is a highly variable signal. For example, acoustics of the same word spoken by different speakers are highly distinct; nevertheless, listeners can easily recognize the sounds to carry the same linguistic message. Human listeners and even infants over their first year of life, can perceive speech categories of their native language amidst remarkable acoustic variability present in speech signals. But how does the human brain naturally learn to group physically distinct sounds into their native speech categories without directed training or explicit feedback?

One candidate neural substrate is the striatum and its interaction with the cortex (i.e., cortico-striatal loops), which have been mostly implicated in visual category learning. However, this evidence was found using explicit feedback-based categorization tasks. Likewise, most speech learning studies typically use standard feedback-based tasks involving explicit categorization of sounds, so do not model how learning occurs in real-world. Thus, our understanding has been limited in regards to how the brain learns sound categories incidentally from complex naturalistic environments. Does the striatum plays any role in a more ecologically-valid category learning?

In order to investigate the neural basis of incidental sound category learning, Sung-Joo Lim (CMU Psychology/CNBC alumna and currently at Boston University as a Research Assistant Professor), worked with Julie Fiez (Pitt Psychology and CNBC) and Lori Holt (CMU Psychology and CNBC), used fMRI as participants actively played a rich multimodal and first-person videogame. This videogame provides a unique way to examine how sound category learning occurs incidentally without overt categorization; it does not require explicit attention to sounds, but sound category exemplars have functional utility in predicting upcoming gaming events, and come to incidentally guiding successful game actions in players. When participants were exposed to statistically coherent sound exemplars associated with a specific gaming event (i.e., appearance of a specific alien character), they learned functionally relevant sound categories by simply playing the game without explicit categorization of sounds.

“Critically, we found that the posterior striatum was engaged and functionally connected to the auditory cortex (i.e., left posterior superior temporal sulcus) during game play,” Lim reports. “The magnitudes of its activation and connectivity predicted the learning outcome assessed after the game training.” In contrast, the authors did not observe the same pattern of results in participants who played the same game but with less statistically coherent sound category exemplars, even though they exhibited similar game performance in the scanner as those who consistently heard coherent sounds in the game. These results provide evidence that the striatum is sensitive to the presence of statistical regularities in the behaviorally relevant categories even though subjects are not directed at categorization per se. “Through its interactions with the auditory cortex, the striatum contributes to incidental acquisition of sound category representations emerging from naturalistic learning environments,” Lim concludes.

Their paper, “Role of the striatum in incidental learning of sound categories”, was published in the Proceedings of the National Academy of Sciences.

Read more…

Renowned auditory neuroscientist Barbara Shinn-Cunningham has been named the 2019 recipient of the Helmholtz Rayleigh Interdisciplinary Silver Medal in Psychological and Physiological Acoustics and Speech Communication “for understanding the cognitive and neural bases of speech perception in complex acoustic environments.”

Shinn-Cunningham is director of Carnegie Mellon University’s Neuroscience Institute, co-director of the Center for the Neural Basis of Cognition, and a professor of psychology, biomedical engineering, and electrical and computer engineering.

Read more…

Automated, robotic training device developed by Carnegie Mellon undergraduate student advances neuroscience research.

We’ve all heard the saying that individuals learn at their own pace. Researchers at Carnegie Mellon University have developed an automated, robotic training device that allows mice to learn at their leisure. The technology stands to further neuroscience research by allowing researchers to train animals under more natural conditions and identify mechanisms of circuit rewiring that occur during learning.

A research team led by Carnegie Mellon neuroscientist Alison Barth has used the automated technology to identify new, previously unidentified pathways activated when the brain rewires its circuits in response to experience. Their findings are published online in Neuron.

Barth’s lab focuses on understanding the process by which cortical circuits receive sensory information and adapt to it in order to learn. Understanding the algorithm that underlies the changes in the brain’s learning circuitry will have important implications for creating engineered systems that use deep learning and artificial intelligence.

“Neural circuits in the cerebral cortex have had 3.5 billion years to evolve to become perfectly adapted to learning things,” said Barth, a professor of biological sciences and member of the Carnegie Mellon Neuroscience Institute and CNBC.  “There is valuable information about what happens in the brain that can be used to inform computations that need to change based on experience.”

To better study how the brain changes during sensory learning, the researchers constructed an army of automated robotic devices, in an effort that was spearheaded by Sarah Bernhard, an undergraduate student in Carnegie Mellon’s Department of Biological Sciences. These devices allowed mice to voluntarily approach a water port in their home cage where they would receive a gentle puff of air to their whiskers followed by a drop of water. If they approached the port and didn’t feel a puff of air, they wouldn’t get a drop of water. Eventually, they learned that a puff of air meant water and they would start to drink when they felt it. Read more…

Rob Kass, Maurice Falk Professor in the Department of Statistics (CMU) and former interim co-director of the CNBC recently gave a lecture on “Uncertainty, Information and Narrative” to the English Department at CMU. Kass notes:

Like many other human endeavors, science is built on story telling: the aim of science is to explain how things work. But when science is reported, either in the popular press or in scientific journals, the stories almost always blend results with speculation. Too often the distinction between scientific findings and speculation gets blurred, and the reporting of speculative theories as if they are necessary consequences of results helps erode public confidence in science.

One reason authors de-emphasize such subtleties is widespread discomfort with uncertainty and the procedural nature of scientific knowledge, but these are the everyday business of statistics. Using examples from neuroscience, history, and the “bible codes” controversy, this non-technical lecture (aimed at a broad audience of humanities and science students and faculty) summarizes several of the most basic statistical lessons for our citizenry, and indicates how these lessons could inform narratives. It also modernizes the definition of scientific method, incorporating widely accepted features that are too frequently ignored in interpretive summaries of scientific discoveries.

The lecture can be viewed here: https://youtu.be/N45vrpY_J2g

Using sensors in helmets and MRI scans of college football players, Carnegie Mellon University Psychology and CNBC faculty member Brad Mahon and researchers at the University of Rochester Medical Center have found that routine hits to the head sustained during just one season can cause damage to the brain. The study followed 38 players at the University of Rochester, measuring the forces of hits taken during all games and practices.

The study, “A common neural signature of brain injury in concussion and subconcussion” was published in Science Advance”

Read more at the CMU press release located here.

Caroline Runyan, an assistant Professor in Neuroscience at the University of Pittsburgh and a member of the CNBC, has been awarded an NIH Director’s New Innovator Award. According to the NIH, the award ” supports unusually innovative research from early career investigators who are within 10 years of their final degree or clinical residency and have not yet received a research project grant or equivalent NIH grant.” Runyan’s work focuses on the circuit mechanisms controlling signal transmission between brain regions. Her lab uses optical tools to monitor and manipulate the activity of defined neuronal populations across behavioral contexts. A specific research focus of her Innovator Award project concerns the role of inhibitory neurons in gating information flow in the brain. More information about the award, part of the NIH’s High-Risk, High-Reward Research Program, can be found here .

What Is the High-Risk, High-Reward Research Program? (1 minute video)

An audio-described version of this video is also available.

Former CNBC and CMU graduate student, Matthew Botvinick (2001) will be giving a talk on Thursday titled, “A distributional code for value in dopamine-based reinforcement learning”. Since leaving Princeton University where he was a professor of psychology, Botvinick currently leads the neuroscience team at DeepMind. You can read more on him here.

The abstract of his talk is as follows:
“A distributional code for value in dopamine-based reinforcement learning”

Matthew Botvinick, MD, PhD
Director of Neuroscience, DeepMind

Thursday, November 21, 2019
4:00pm
328 Mellon Institute

Twenty years ago, a link was discovered between the neurotransmitter dopamine and the computational framework of reinforcement learning. Since then, it has become well established that dopamine release reflects a reward prediction error, a surprise signal that drives learning of reward predictions and shapes future behavior. According to the now canonical theory, reward predictions are represented as a single scalar quantity, which supports learning about the expectation, or mean, of stochastic outcomes. I’ll present recent work in which we have proposed a novel account of dopamine-based reinforcement learning, and adduced experimental results which point to a significant modification of the standard reward prediction error theory. Inspired by recent artificial intelligence research on distributional reinforcement learning, we hypothesized that the brain represents possible future rewards not as a single mean, but instead as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. This idea leads immediately to a set of empirical predictions, which we tested using single-unit recordings from mouse ventral tegmental area. Our findings provide strong evidence for a neural realization of distributional reinforcement learning.

Carnegie Mellon University Neuroscience Institute researchers Timothy Verstynen and Eric Yttri  and their collaborators have been granted over $1 million from the National Institutes for Health. The grant is through the Collaborative Research in Computational Neuroscience program, an international effort that supports collaborative activities that will advance the understanding of the nervous system.

The CMU researchers are teaming up with the University of Pittsburgh’s Jonathan Rubin and Catalina Vich from the Universitat de les Illes Balears in Spain. Together, they plan to learn more about the neural mechanisms underpinning how organisms make decisions and how they change their minds.

“We are going to examine what happens when the brain shifts gears,” Verstynen, an associate professor of psychology, explained. “What are the dynamics that happen when the brain either learns something new or relies on prior knowledge?”

Verstynen is most interested in the dynamic decision-making process that happens in the brain as it balances between taking actions that are likely to produce desired results and taking a risk on those that are less certain.

Over the last few years, Verstynen and his team has been collecting data on what are called “bandit tasks,” simple tasks where a subject has a limited number of decisions to make. For example, think of a Las Vegas slot machine, or “one armed bandit.” Though simple, bandit tasks can tell us a lot about how our brains make any kind of decisions.

“When you play the slots, you can only play on one machine at a time, with one coin at a time. How do you choose which machine to put your coin in? That’s the fundamental decision of the bandit task,” Verstynen said. “You can use that very simple paradigm to get at almost any goal-directed decision because the dynamics are almost always the same. The sense of the value of an action drives a lot of our voluntary goal-directed behavior. These bandit tasks distill it down to its fundamental parts.”

Verstynen and his team will use artificial intelligence agents to test decision-making models. Using this technology, the researchers will be able to build and improve models that show what happens in the brain when contingencies change.

In parallel, Yttri and his lab will investigate how these models perform in living organisms. They will observe how mice trained to perform similar bandit tasks choose their behaviors.

“We have some good models for how the brain makes decisions, how organisms place value on different options and make a choice,” Yttri, an assistant professor of biological sciences, said. “But models will only get you so far. We hope to prove those models through direct experimental testing.”

In the big picture, this work could help us understand how humans make choices, and even give researchers clues on disease states, including obsessive compulsive disorder and addiction, Yttri said.

“By recording and manipulating neuronal activity, we can understand the computations those neurons are producing at a mechanistic level,” Yttri said. “This could help us understand decision making at a fundamental level.”

Author: Caroline Sheedy

Original post

Barbara Shinn-Cunningham, director of the Neuroscience Institute and George A. and Helen Dunham Cowan Professor of Auditory Neuroscience is a 2020 co-recipient of the Bernice Grafstein Award for Outstanding Accomplishments in Mentoring from the Society for Neuroscience.

The award recognizes individuals who are dedicated to promoting women’s advancement in the field of neuroscience and who have made outstanding accomplishments in mentoring.

Shinn-Cunningham was nominated by her colleague Lori Holt, a professor of psychology in the Dietrich College of Humanities and Social Sciences. Forty-four of her current and former trainees also submitted a letter of recommendation.

“Not a week goes by without me witnessing an example of Professor Shinn-Cunningham’s tireless efforts on behalf of women’s advancement in neuroscience,” Holt said. “She has an approach to mentoring that is inclusive, playful and individualized even as it is rigorous and scientifically top-notch.”

In their letter of recommendation, her mentees noted Shinn-Cunningham’s commitment to them.

“Barb’s mentorship has been particularly influential for the women who have worked with her. Whether it’s her commitment to putting women in roles of influence and prestige … her sheer enthusiasm for young scientists having children … her frank admission that sexism in science is real and affects all of us, or simply her unwavering belief that we are good scientists and our work is valuable, Barb’s mentorship has kept many of us in science at times when we wondered whether we belonged,” the letter reads.

Shinn-Cunningham was honored to be a co-recipient.

“Nurturing the next generation of scientists is the most rewarding, and honestly, the most important, part of my work,” Shinn-Cunningham said. “Talking openly and directly with each individual about their goals as well as the doubts they have and the challenges they face, all while championing their strengths, guides them to make intentional decisions about their path and empowers them to aim high rather than being held back by a fear of failure.”

In her research, Shinn-Cunningham combines behavioral, neuroimaging, and computational methods to understand how the brain processes sound. She will accept the award in a virtual event during the Society for Neuroscience’s Awards Announcement Week.

Shinn-Cunningham and her colleagues at a virtual PhD celebration. 

Author: Caroline Sheedy

Original post

The Cognitive Science Society announced seven research scientists, including Carnegie Mellon University’s Cleotilde Gonzalez, as 2021 Fellows. Gonzalez joins a selected group of 120 researchers, who have been recognized for their sustained excellence and impact on the cognitive science community.

“I have dedicated my career to understanding the cognitive processes by which people make decisions in dynamic environments,” said Gonzalez, a research professor in Social and Decision Sciences at CMU’s Dietrich College of Humanities and Social Sciences. “This is a unique honor for me, a woman from Mexico, belonging to a large family of nine kids, the first woman in the family to get a Ph.D., and who had to confront a number of obstacles being an underrepresented minority in a very competitive environment. To me, it is this part of my story that makes the recognition more significant.”

Gonzalez’s research focuses on how people and machines make multiple, interdependent, real-time decisions while adapting to external changes and using past experience. She conducts experimental studies using games and computational cognitive models. Her work has led to the development of a theory of decisions from experience, called Instance-Based Learning (IBL) Theory. She and her colleagues have promoted IBL computational models that are able to generate predictions of decisions humans would make in particular domains and according to individualized experiences. These results are used to help decision makers make better choices.

“Computational representations of human decision-making are very powerful,” said Gonzalez. “We [can] apply these ideas in areas as diverse as cybersecurity, [including] phishing and defense resource allocation, social dilemmas, human-machine teaming, recommender systems and other domains.”

Gonzalez received her Bachelor of Science and Master of Business Administration from Universidad de las Americas-Puebla, Mexico and a Master of Science and Doctoral degrees from Texas Tech University. Shortly after completing her doctoral degree, she secured a postdoctoral fellowship at the Tepper School of Business at CMU. She obtained an assistant professor position with SDS in 2000 and advanced to an associate research professor position seven years later. Gonzalez was named a research professor in SDS in 2015. She has advised more than 30 postdoctoral fellows and four doctoral students.

Gonzalez was elected to the Governing Board of the Cognitive Science Society in 2019. She was selected as a society fellow with the Human Factors and Ergonomics Society in 2014. She is the principal investigator or co-investigator on more than 20 grants. She has published more than 120 research papers.

“SDS is exceedingly fortunate to have someone of Coty’s caliber on our faculty,” said Professor of Social and Decision Sciences Gretchen Chapman, who is serving as interim department head. “Not only is she brilliant and productive, but she is also generous with her time and committed to enhancing Dietrich College.”

The Cognitive Science Society brings together researchers from around the world to promote cognitive science as a discipline and to foster scientific interchange among researchers in various areas of study, including artificial intelligence, linguistics, anthropology, psychology, neuroscience, philosophy and education.

Author: Stacy Kish

Original post

Lori Holt Talks Crowd Noise on CBS News Sunday Morning

Lori Holt speaks with CBS’ David Pogue about the sounds of the Super Bowl.

Although sporting events have continued in the time of COVID-19, stadiums have largely remained empty while fans watch from the safety of their homes. In the past, most of us have taken the noise of a cheering crowd for granted, but now there are no fans who are physically present to create this auditory backdrop. Professional sports leagues have solved this problem by playing pre-recorded crowd noise to simulate the environment of a usual game.

Lori Holt, professor of psychology and core faculty in the Neuroscience Institute, studies the cognitive processes that underlie auditory processing. She appeared on CBS News Sunday Morning to talk with David Pogue about the importance of crowd noise at sporting events.

Watch the video on the CBS News website.