About

Russell Poldrack is the Albert Ray Lang Professor of Psychology and, by courtesy, of Psychiatry and Behavioral Science at Stanford University. His academic appointments include memberships in the Bio-X program, the Wu Tsai Human Performance Alliance, and the Stanford Data Science faculty. He also serves as the Academic Director of the Center for Open and Reproducible Science and is a member of the Wu Tsai Neurosciences Institute. Poldrack's research focuses on cognitive science, decision making and rationality, and neurosciences. He grew up in a small town in Texas and attended Baylor University. After completing his PhD in experimental psychology at the University of Illinois in Urbana-Champaign, he spent four years as a postdoctoral researcher at Stanford. His faculty positions have included roles at Massachusetts General Hospital/Harvard Medical School, UCLA, and the University of Texas, before joining Stanford's faculty in 2014. Poldrack has held various leadership roles, including chairing the Organization for Human Brain Mapping and serving on advisory boards such as the National Institute of Mental Health and the Adolescent Brain Cognitive Development Study. His honors include the International KFJ Award from Copenhagen University Hospital, being named a Corresponding Fellow of the British Academy, and receiving the Open Science Award from the Organization for Human Brain Mapping.

Research topics

• Computer Science
• Data Mining
• Data science
• Political Science
• Neuroscience
• World Wide Web
• Computer Security
• Psychology
• Statistics
• Mathematics
• Database
• Cognitive psychology
• Clinical psychology
• Psychiatry
• Biology

Selected publications

• Responsible Research Assessment of Faculty: maximizing quality, transparency, and trustworthiness of scientific research in Research-Performing Organizations

  2026-01-28

  articleOpen access

  This multi-interest holder consensus effort highlights the essential influence and responsibility of Research-Performing Organizations (RPOs) in shaping the quality, transparency, and trustworthiness of scientific research. Despite the use of metrics to assess transparency and openness by academic journals and funders, most RPOs do not yet have metrics/indicators or monitoring that address deep-rooted shortcomings in research practice and assessment. These shortcomings manifest as restricted access to research outputs, poor availability of underlying data and materials, infrequent reproducibility checks and direct replications, and ongoing incidents of research misconduct all contributing to erosion of public trust in research. Such issues are exacerbated by institutional incentive structures that reward the number of publications and journal prestige, while neglecting research credibility, societal relevance, and transparent communication. To respond comprehensively to these challenges, our working group reached consensus on six core practices for RPOs to monitor: data, code, and material sharing; open access publishing; prospective study registration; reporting transparency; disclosures of interest and funding; and verification efforts.Collectively, these six practices offer a pragmatic and flexible framework that RPOs can tailor to their local context. The paper provides an implementation guide for these practices and calls for renewed leadership by RPOs in realigning research(er) assessment and incentives to reward quality, transparency, and trustworthiness in research, closing persistent gaps, and reinforcing science’s credibility and utility for society.

  PublisherDOI

• Responsible Research Assessment of Faculty: maximizing quality, transparency, and trustworthiness of scientific research in Research-Performing Organizations

  MetArXiv (OSF Preprints) · 2026-03-10

  preprintOpen access

  This multi-interest holder consensus effort highlights the essential influence and responsibility of Research-Performing Organizations (RPOs) in shaping the quality, transparency, and trustworthiness of scientific research. Despite the use of metrics to assess transparency and openness by academic journals and funders, most RPOs do not yet have metrics/indicators or monitoring that address deep-rooted shortcomings in research practice and assessment. These shortcomings manifest as restricted access to research outputs, poor availability of underlying data and materials, infrequent reproducibility checks and direct replications, and ongoing incidents of research misconduct all contributing to erosion of public trust in research. Such issues are exacerbated by institutional incentive structures that reward the number of publications and journal prestige, while neglecting research credibility, societal relevance, and transparent communication. To respond comprehensively to these challenges, our working group reached consensus on six core practices for RPOs to monitor: data, code, and material sharing; open access publishing; prospective study registration; reporting transparency; disclosures of interest and funding; and verification efforts. Collectively, these six practices offer a pragmatic and flexible framework that RPOs can tailor to their local context. The paper provides an implementation guide for these practices and calls for renewed leadership by RPOs in realigning research(er) assessment and incentives to reward quality, transparency, and trustworthiness in research, closing persistent gaps, and reinforcing science’s credibility and utility for society.

  Publisher

• Responsible Research Assessment of Faculty: maximizing quality, transparency, and trustworthiness of scientific research in Research-Performing Organizations

  MetArXiv (OSF Preprints) · 2026-02-09

  preprintOpen access

  This multi-interest holder consensus effort highlights the essential influence and responsibility of Research-Performing Organizations (RPOs) in shaping the quality, transparency, and trustworthiness of scientific research. Despite the use of metrics to assess transparency and openness by academic journals and funders, most RPOs do not yet have metrics/indicators or monitoring that address deep-rooted shortcomings in research practice and assessment. These shortcomings manifest as restricted access to research outputs, poor availability of underlying data and materials, infrequent reproducibility checks and direct replications, and ongoing incidents of research misconduct all contributing to erosion of public trust in research. Such issues are exacerbated by institutional incentive structures that reward the number of publications and journal prestige, while neglecting research credibility, societal relevance, and transparent communication. To respond comprehensively to these challenges, our working group reached consensus on six core practices for RPOs to monitor: data, code, and material sharing; open access publishing; prospective study registration; reporting transparency; disclosures of interest and funding; and verification efforts. Collectively, these six practices offer a pragmatic and flexible framework that RPOs can tailor to their local context. The paper provides an implementation guide for these practices and calls for renewed leadership by RPOs in realigning research(er) assessment and incentives to reward quality, transparency, and trustworthiness in research, closing persistent gaps, and reinforcing science’s credibility and utility for society.

  Publisher

• Assessing metadata privacy in neuroimaging

  Imaging Neuroscience · 2026-01-01

  articleOpen access

  The ethical and legal imperative to share research data without causing harm requires careful attention to privacy risks. While mounting evidence demonstrates that data sharing benefits science, legitimate concerns persist regarding the potential leakage of personal information that could lead to reidentification and subsequent harm. We reviewed metadata accompanying neuroimaging datasets from heterogeneous studies openly available on OpenNeuro, involving participants across the lifespan-from children to older adults-with and without clinical diagnoses, and including associated clinical score data. Using metaprivBIDS (https://github.com/CPernet/metaprivBIDS), a software application for BIDS-compliant tsv/json files that computes and reports different privacy metrics (k-anonymity, k-global, l-diversity, SUDA, PIF), we found that privacy is generally well maintained, with serious vulnerabilities being rare. Nonetheless, issues were identified in nearly all datasets and warrant mitigation. Notably, clinical score data (e.g., neuropsychological results) posed minimal reidentification risk, whereas demographic variables-age, sex assigned at birth, sexual orientations, race, income, and geolocation-represented the principal privacy vulnerabilities. We outline practical measures to address these risks, enabling safer data sharing practices.

  PublisherOA PDFDOI

• Intersections of Working Memory and Response Inhibition

  Open Science Framework · 2026-01-01

  otherOpen accessSenior author

  Series of experiments testing shared cognitive processes for working memory and response inhibition.

  PublisherDOI

• Retention and data exclusion challenges for representative longitudinal neuroimaging in the understanding of addiction

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-09

  preprintOpen access

  Abstract Head motion during resting-state functional magnetic resonance imaging (rsfMRI) poses a major challenge for neuroimaging research, often leading to data quality concerns and participant exclusions, particularly among pediatric and clinical populations. Although necessary for ensuring reliable data, motion-related exclusions may inadvertently bias samples by disproportionately excluding certain sociodemographic groups. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we employed both frequentist and Bayesian approaches to examine how head motion-related exclusions and participant retention shape sample composition over time. At baseline (ages 9-10), Black, Hispanic, and Asian youth were significantly more likely than White youth to be excluded due to excessive head motion; these disparities were not observed at the two-year follow-up (ages 11-13). In contrast, disparities in retention persisted; Black participants were less likely to return for follow-up, even after accounting for socioeconomic factors and motion. Together, these findings highlight how both motion-related exclusions and differential retention can systematically influence the representativeness of longitudinal neuroimaging samples, with important implications for the generalizability of research in addiction neuroscience.

  PublisherOA PDFDOI

• The MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia Program

  UNC Libraries · 2025-12-04

  articleOpen access
  PublisherDOI

• Corrigendum to “A multi-sample evaluation of the measurement structure and function of the modified monetary incentive delay task in adolescents” [Dev. Cogn. Neurosci. 65 (2024) 1–17]

  Developmental Cognitive Neuroscience · 2025-02-26

  erratumOpen accessSenior author
  PublisherDOI

• fMRIPrep Lifespan: Extending A Robust Pipeline for Functional MRI Preprocessing to Developmental Neuroimaging

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-18 · 5 citations

  preprintOpen access

  The adoption of a standardized preprocessing workflow is vital for fostering community, sharing, and reproducibility. fMRIPrep has been a critical advancement towards this end, however, it is limited in its capacity to be applied to data across the lifespan, starting from infancy. Here, we introduce fMRIPrep Lifespan, an extension of fMRIPrep that extends the standardized processing from childhood to senescence to include neonatal, infant, and toddler structural and functional MRI data preprocessing. This effort involves a NiPreps integration of 1) a workflow akin to fMRIPrep optimized for MRI data in the first years of life (previously NiBabies) and 2) upstream enhancements to the entire NiPreps suite, including multi-echo data processing, modularization of workflow components, and convergence of processing with other popular workflows (ABCD-BIDS, Human Connectome Project Pipelines). Using data from the Baby Connectome Project (participants 1-43 months of age), we demonstrate that fMRIPrep Lifespan produces high-quality outputs across a wide age range. Moving forward, the scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age while maintaining robust and reproducible frameworks for functional MRI research across the lifespan.

  PublisherOA PDFDOI

• Author response: An image-computable model of speeded decision-making

  2025-01-29

  peer-reviewOpen accessSenior author

  Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAMs are jointly fitted to trial-level RTs and raw (pixel-space) visual stimuli from individual subjects in a unified Bayesian framework. Models fitted to large-scale cognitive training data from a stylized flanker task captured individual differences in congruency effects, RTs, and accuracy. We find evidence that the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations, demonstrating how our framework can be used to relate visual representations to behavioral outputs. Together, our work provides a probabilistic framework for both constraining neural network models of vision with behavioral data and studying how the visual system extracts representations that guide decisions.

  PublisherDOI

Recent grants

• CRCNS Data Sharing: An open data repository for cognitive neuroscience: The OpenfMRI Project

  NSF · $262k · 2014–2016

• BIDS-Derivatives: A data standard for derived data and models in the BRAIN Initiative

  NIH · $1.4M · 2017–2020

• OpenNeuro: An open archive for analysis and sharing of BRAIN Initiative data

  NIH · $10.2M · 2018–2028

• The development of neural responses to punishment in adolescence

  NIH · $723k · 2013–2017

• The Cognitive Atlas: Developing an Interdisciplinary Knowledge Base Through Socia

  NIH · $1.9M · 2008–2014

Frequent coauthors

• Éric Miller

  Duke University Health System

  287 shared

• Anthony D. Wagner

  Stanford University

  287 shared

• Ralph Adolphs

  California Institute of Technology

  271 shared

• Marcus E. Raichle

  Washington University in St. Louis

  270 shared

• Stanislas Dehaene

  NeuroSpine Institute

  268 shared

• Renée Baillargeon

  University of Illinois Urbana-Champaign

  268 shared

• Patrick Scheffmann

  268 shared

• Nick Chater

  University of Warwick

  268 shared

Education

• Ph.D., Psychology

  University of Illinois

  1995

• B.A., Psychology

  Baylor University

  1989

Awards & honors

• KFJ Award, Copenhagen University Hospital (2023)
• Corresponding Fellow, The British Academy (2023)
• Open Science Award, Organization for Human Brain Mapping (20…
• Fellow, Organization for Human Brain Mapping (2021)
• Elected member, Society of Experimental Psychologists (2018)