Barbara Shinn-Cunningham

Professor Carnegie Mellon University

  • Pittsburgh PA

Barbara Shinn-Cunningham's research explores such issues as how do we make sense of speech and other sounds.

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Carnegie Mellon University

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Biography

Barbara Shinn-Cunningham is the director of the Carnegie Mellon Neuroscience Institute. Her research explores such issues as how do we make sense of speech and other sounds, how our brain networks allow us to focus attention and suppress uninteresting sound and whether we can develop new assistive communication devices and technologies that leverage knowledge from auditory neuroscience to aid listeners with hearing impairment or other communication disorders. Her work uses behavioral, neuroimaging and computational methods to understand auditory processing, from how sound is encoded in the inner ear to how cognitive networks modulate the representation of auditory information in the brain.

Areas of Expertise

Non-Invasive Brain Monitoring
Mathematical & Statistical Methods
Computational
Cognitive Neuroscience
Characterization of Neural Circuits
Auditory Research
Behavioral Methods
Computational Neuroscience
Executive Control & Memory
Spatial Cognition & Attention
Sensation & Perception

Media Appearances

New dean joins Carnegie Mellon science college

Trib Live  online

2024-10-01

Barbara Shinn-Cunningham came to Carnegie Mellon in 2018 as founding director of the Neuroscience Institute. A faculty researcher and an engineer by training, she is a professor of auditory neuroscience.

“Interdisciplinary approaches erase boundaries that have traditionally separated fields of study, thereby accelerating scientific discovery,” she said. “Such collaboration is part of CMU’s DNA, a fact that attracts some of the most creative and broad-thinking scientists to the Mellon College of Science.”

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Neuroscientists and Game Designers Play Well Together

Carnegie Mellon University  online

2022-11-02

"Neuroscience is trending in the direction of using richer, more natural stimuli and less constrained behavior," Shinn-Cunningham said. "To get good data, past research often has been repetitive and dull, dividing tasks into brief 'trials' that constrain what happens. Acquiring information isn't fun or meaningful like it is in real life. One of the things game designers can teach us is how to make tasks fun, which can change how the brain functions."

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Pittsburgh’s ‘neighborly playground’ for neuroscience has new leadership

Pittwire - University of Pittsburgh  online

2022-08-31

“We went through many exercises to try to figure out what people need and want out of the center and were able to generate feedback in a bottom up, grassroots way,” Shinn-Cunningham said. “The neuroscience programs at both universities have grown substantially over the years — but everyone still recognizes how much they gain from being part of the larger, more diverse community.”

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Social

Industry Expertise

Education/Learning

Accomplishments

Helmholtz-Rayleigh Interdisciplinary Silver Medal

2019

David T. Blackstock Mentorship Award

2013

Education

Massachusetts Institute of Technology

Ph.D.

Electrical & Computer Engineering

Massachusetts Institute of Technology

M.S.

Electrical & Computer Engineering

Brown University

B.S.

Electrical Engineering

Affiliations

  • American Institute for Medical and Biological Engineers : Fellow
  • National Research Council : Associate Member
  • American Statistical Association : Fellow

Articles

Induced alpha and beta electroencephalographic rhythms covary with single-trial speech intelligibility in competition

Scientific Reports

2023

Neurophysiological studies suggest that intrinsic brain oscillations influence sensory processing, especially of rhythmic stimuli like speech. Prior work suggests that brain rhythms may mediate perceptual grouping and selective attention to speech amidst competing sound, as well as more linguistic aspects of speech processing like predictive coding. However, we know of no prior studies that have directly tested, at the single-trial level, whether brain oscillations relate to speech-in-noise outcomes. Here, we combined electroencephalography while simultaneously measuring intelligibility of spoken sentences amidst two different interfering sounds: multi-talker babble or speech-shaped noise.

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Statistical learning across passive listening adjusts perceptual weights of speech input dimensions

Cognition

2023

Statistical learning across passive exposure has been theoretically situated with unsupervised learning. However, when input statistics accumulate over established representations – like speech syllables, for example – there is the possibility that prediction derived from activation of rich, existing representations may support error-driven learning. Here, across five experiments, we present evidence for error-driven learning across passive speech listening. Young adults passively listened to a string of eight beer - pier speech tokens with distributional regularities following either a canonical American-English acoustic dimension correlation or a correlation reversed to create an accent. A sequence-final test stimulus assayed the perceptual weight – the effectiveness – of the secondary dimension in signaling category membership as a function of preceding sequence regularities.

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Diffuse Optical Tomography Spatial Prior for EEG Source Localization in Human Visual Cortex

NeuroImage

2023

Electroencephalography (EEG) and diffuse optical tomography (DOT) are imaging methods which are widely used for neuroimaging. While the temporal resolution of EEG is high, the spatial resolution is typically limited. DOT, on the other hand, has high spatial resolution, but the temporal resolution is inherently limited by the slow hemodynamics it measures. In our previous work, we showed using computer simulations that when using the results of DOT reconstruction as the spatial prior for EEG source reconstruction, high spatio-temporal resolution could be achieved. In this work, we experimentally validate the algorithm by alternatingly flashing two visual stimuli at a speed that is faster than the temporal resolution of DOT.

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