Functional MRI depicts working memory circuitry in the human brain.
Credit: Emily G. Jacobs

 Research

The Cognitive Neuroscience research area is focused on understanding how distributed neural networks contribute to key cognitive abilities in humans. The area is comprised of broad faculty interests that include sensory perception, motor action, learning, memory, decision-making, categorization, attention, spatial navigation, and social cognition.

Cognitive Neuroscience researchers employ a range of techniques to study particular cognitive processes, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and transcranial magnetic stimulation (TMS).

 Affiliated Faculty

Distinguished Professor
Psychological & Brain Sciences
Professor Ashby is interested in the basic cognitive and neural mechanisms that mediate human learning. His approach combines experimental psychology, cognitive neuroscience, and mathematical modeling.
Professor
Department of Physics
Cognitive neuroscience, learning, memory, categorization, decision processes in perception and cognition.
Professor
Psychological & Brain Sciences
Dr. Eckstein is interested in how biological organisms and artificial intelligent agents visually sense the world and make decisions. He uses computational modeling, cognitive neuroscience and behavioral measurement techniques.
Distinguished Professor
Psychological & Brain Sciences
Dr. Gazzaniga is currently engaged in several scholarly projects examining how codes play a role in understanding how neurons become mind.
Professor
Psychological & Brain Sciences
Visual attention; Cognitive neuroscience; Brain Imaging; Exercise physiology.
Distinguished Professor
Psychological & Brain Sciences
Professor Grafton is interested in how people organize movement into goal-oriented action.
Assistant Professor
Psychological & Brain Sciences
Uses human brain imaging techniques (fMRI) and endocrinology to determine the impact of sex steroid hormones on brain morphology and function.
Assistant Professor
Psychological & Brain Sciences
Our lab uses neuroimaging and causal methods to investigate neural mechanisms underlying emotion perception, metacognition and emotion regulation.
Professor
Statistics and Applied Probability
Statistical Methods and Data Analyses, Including For Brain Imaging and Other Neuroscience Data; Statistical Methods for Analyzing Spatial and Temporal Processes; Computational Statistics and Machine Learning; Uncertainty Quantification.
Professor
Psychological & Brain Sciences
Dr. Miller is interested in the psychological and neural processes underlying human memory and decision-making.
Assistant Professor
Chemical Engineering
Neuroimaging, MRI, biochemical imaging of the GI tract.
Distinguished Professor
Mechanical Engineering
Computer Science
Computational methods, mathematical modeling, and machine learning, with application to a wide range of problems from systems biology, neuroscience and engineering.
Assistant Professor
Psychological & Brain Sciences
Kyle investigates how biological systems interact with social contexts to influence perceptions of the self and others.
Professor
Computer Science
Data-centric modeling of systems and he focuses on the development of new methods that can be applied to real-world applications.
Assistant Professor
Psychological & Brain Sciences
In the Perception, Cognition, and Action Lab (PCA Lab), we seek to understand how the brain encodes and transforms neural representations of sensory information in service of dynamic behavioral demands.
Professor
Electrical & Computer Engineering
Novel reporters for neural activity, CMOS image sensors and circuits for neurotechnology.
Professor
Department of Communication
In the Media Neuroscience Lab (MNL) we study attention disorders and media-multitasking, aggressive conduct disorders and media violence, moral conflict and narrative performance, and the neural mechanisms of persuasion, flow experiences, and general cognitive control.
Assistant Professor
Dr. Yu combines behavioral experiments, neuroscience and computational modeling to understand the relationship between emotion (e.g., guilt, gratitude) and morality and their neural representations.