Marvin Chun

About

Marvin Chun is the Richard M. Colgate Professor of Psychology and Professor of Neuroscience at Yale University. He earned his Ph.D. from MIT in 1994. His laboratory employs functional magnetic resonance imaging (fMRI) to study various aspects of cognition, including visual attention, memory, decision-making, perception, and performance. A primary focus of his research is to use fMRI to decode brain activity in order to understand how people perceive, remember, and make decisions. For example, his work involves guessing which faces people are viewing or determining whether individuals are attentive or distracted based on brain activity patterns. Additionally, he aims to use fMRI to predict individual differences in behavior, such as whether it is possible to forecast how well someone will perform a task even when they are not actively engaged in it while being scanned.

Research topics

• Psychology
• Cognitive psychology
• Computer science
• Artificial intelligence
• Neuroscience

Selected publications

• Functional connectivity predicts changes in attention observed across minutes, days, and months

  Proceedings of the National Academy of Sciences · 2020-02-04 · 191 citations

  articleOpen accessSenior author

  The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention within individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attent…

  PublisherOA PDFDOI

• Connectome-based neurofeedback: A pilot study to improve sustained attention

  NeuroImage · 2020-02-27 · 50 citations

  articleOpen access

  Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is a non-invasive, non-pharmacological therapeutic tool that may be useful for training behavior and alleviating clinical symptoms. Although previous work has used rt-fMRI to target brain activity in or functional connectivity between a small number of brain regions, there is growing evidence that symptoms and behavior emerge from interactions between a number of distinct brain areas. Here, we propose a new method for rt-fMR…

  PublisherDOI

• Differences in the functional brain architecture of sustained attention and working memory in youth and adults

  PLoS Biology · 2022-12-21 · 41 citations

  articleOpen access

  Sustained attention (SA) and working memory (WM) are critical processes, but the brain networks supporting these abilities in development are unknown. We characterized the functional brain architecture of SA and WM in 9- to 11-year-old children and adults. First, we found that adult network predictors of SA generalized to predict individual differences and fluctuations in SA in youth. A WM model predicted WM performance both across and within children-and captured individual differences in later…

  PublisherDOI

• A generalizable connectome-based marker of in-scan sustained attention in neurodiverse youth

  Cerebral Cortex · 2022-12-07 · 12 citations

  articleOpen access

  Difficulty with attention is an important symptom in many conditions in psychiatry, including neurodiverse conditions such as autism. There is a need to better understand the neurobiological correlates of attention and leverage these findings in healthcare settings. Nevertheless, it remains unclear if it is possible to build dimensional predictive models of attentional state in a sample that includes participants with neurodiverse conditions. Here, we use 5 datasets to identify and validate func…

  PublisherOA PDFDOI

• Edge-Based General Linear Models Capture Moment-to-Moment Fluctuations in Attention

  Journal of Neuroscience · 2024-02-05 · 11 citations

  articleOpen access

  Although we must prioritize the processing of task-relevant information to navigate life, our ability to do so fluctuates across time. Previous work has identified fMRI functional connectivity (FC) networks that predict an individual's ability to sustain attention and vary with attentional state from 1 min to the next. However, traditional dynamic FC approaches typically lack the temporal precision to capture moment-to-moment network fluctuations. Recently, researchers have "unfurled" traditiona…

  PublisherOA PDFDOI

Recent grants

• Whole Brain Functional Connectivity Measures of Attention

  NSF · $492k · 2016–2019

• NIH Grant R01EY014193

  NIH · $3.2M · 2012

• Whole Brain Functional Connectivity Measures of Attention

  NIH · $1.7M · 2016–2021

Frequent coauthors

• Monica D. Rosenberg

  48 shared

• Kwangsun Yoo

  Sungkyunkwan University

  28 shared

• R. Todd Constable

  Yale University

  26 shared

• Dustin Scheinost

  Yale University

  23 shared

• Yuhong Jiang

  China University of Geosciences

  23 shared

• Nicholas B. Turk‐Browne

  Yale University

  20 shared

• Do-Joon Yi

  19 shared

• Yaoda Xu

  Yale University

  18 shared

Education

• Ph.D., Brain and Cognitive Science

  Massachusetts Institute of Technology

Similar researchers at Yale University

• Alan Kazdinh-142
• John Dovidioh-124
• Carlos Griloh-116