About
Dr. Jason Yeatman is an Associate Professor in the Graduate School of Education and Department of Psychology at Stanford University, as well as a member of the Division of Developmental and Behavioral Pediatrics at Stanford University School of Medicine. His research focuses on understanding the neurobiology of literacy and how brain plasticity relates to learning. He developed new brain imaging methods to study these relationships and aims to elucidate the mechanisms underlying learning to read, including how these processes differ in children with dyslexia. As the director of the Brain Development and Education Lab, Dr. Yeatman's overarching goal is to investigate how reading instruction influences the development of brain circuits specialized for reading. His work employs structural and functional neuroimaging measurements to examine how children's experiences with reading shape brain development. His research also explores neural mechanisms of successful intervention in children with dyslexia, with the aim of informing personalized treatment approaches. Dr. Yeatman has contributed to advancing knowledge in brain and learning sciences, child development, and educational technology, emphasizing the importance of neuroimaging and data sciences in understanding literacy and learning differences.
Research topics
- Computer Science
- Artificial Intelligence
- Data Mining
- Computer vision
- Psychology
- Database
- Medicine
- Mathematics education
- Physics
- Statistics
- Developmental psychology
- Medical education
- Linguistics
- Cognitive psychology
- Radiology
- Mathematics
Selected publications
The Effect of COVID on Oral Reading Fluency During the 2020–2021 Academic Year
AERA Open · 2022 · 38 citations
- Psychology
- Medical education
- Mathematics education
Education has faced unprecedented disruption during the COVID pandemic. Understanding how students have adapted as we have entered a different phase of the pandemic and some communities have returned to more typical schooling will inform a suite of policy interventions and subsequent research. We use data from an oral reading fluency (ORF) assessment—a rapid assessment taking only a few minutes that measures a fundamental reading skill—to examine COVID’s effects on children’s reading ability during the pandemic. We find that students in the first 200 days of the 2020–2021 school year tended to experience slower growth in ORF relative to prepandemic years. We also observe slower growth in districts with a high percentage of English language learners and/or students eligible for free and reduced-price lunch. These findings offer valuable insight into the effects of COVID on one of the most fundamental skills taught to children.
QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data
Nature Methods · 2021 · 315 citations
- Computer Science
- Computer Science
- Data Mining
QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI
bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 25 citations
- Computer Science
- Computer Science
- Artificial Intelligence
ABSTRACT Diffusion-weighted magnetic resonance imaging (dMRI) has become the primary method for non-invasively studying the organization of white matter in the human brain. While many dMRI acquisition sequences have been developed, they all sample q-space in order to characterize water diffusion. Numerous software platforms have been developed for processing dMRI data, but most work on only a subset of sampling schemes or implement only parts of the processing workflow. Reproducible research and comparisons across dMRI methods are hindered by incompatible software, diverse file formats, and inconsistent naming conventions. Here we introduce QSIPrep, an integrative software platform for the processing of diffusion images that is compatible with nearly all dMRI sampling schemes. Drawing upon a diverse set of software suites to capitalize upon their complementary strengths, QSIPrep automatically applies best practices for dMRI preprocessing, including denoising, distortion correction, head motion correction, coregistration, and spatial normalization. Throughout, QSIPrep provides both visual and quantitative measures of data quality as well as “glass-box” methods reporting. Taken together, these features facilitate easy implementation of best practices for processing of diffusion images while simultaneously ensuring reproducibility.
Scientific Reports · 2020 · 199 citations
- Computer Science
- Artificial Intelligence
- Computer Science
While online experiments have shown tremendous potential to study larger and more diverse participant samples than is possible in the lab, the uncontrolled online environment has prohibited many types of psychophysical studies due to difficulties controlling the viewing distance and stimulus size. We introduce the Virtual Chinrest, a method that measures a participant's viewing distance in the web browser by detecting a participant's blind spot location. This makes it possible to automatically adjust stimulus configurations based on an individual's viewing distance. We validated the Virtual Chinrest in two laboratory studies in which we varied the viewing distance and display size, showing that our method estimates participants' viewing distance with an average error of 3.25 cm. We additionally show that by using the Virtual Chinrest we can reliably replicate measures of visual crowding, which depends on a precise calculation of visual angle, in an uncontrolled online environment. An online experiment with 1153 participants further replicated the findings of prior laboratory work, demonstrating how visual crowding increases with eccentricity and extending this finding by showing that young children, older adults and people with dyslexia all exhibit increased visual crowding, compared to adults without dyslexia. Our method provides a promising pathway to web-based psychophysical research requiring controlled stimulus geometry.
Recent grants
NIH · $5.4M · 2015
Neural mechanisms of successful intervention in children with dyslexia
NIH · $3.1M · 2024–2025
Biological origins of literacy
NIH · $435k · 2018–2022
NSF/SBE-BSF: Biological mechanisms underlying the acquisition of reading skills
NSF · $630k · 2016–2020
Frequent coauthors
- 61 shared
Ariel Rokem
University of Washington
- 50 shared
Sendy Caffarra
University of Modena and Reggio Emilia
- 38 shared
Alex L. White
Barnard College
- 37 shared
Adam Richie-Halford
Stanford University
- 33 shared
Heidi M. Feldman
Stanford University
- 28 shared
John Kruper
University of Washington
- 27 shared
Kendrick Kay
Resonance Research (United States)
- 27 shared
Liesbeth Gijbels
University of Washington
Labs
Education
Ph.D., Psychology
Stanford University
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