R
Russell A. Poldrack
Researcher at Stanford University
Publications - 481
Citations - 70423
Russell A. Poldrack is an academic researcher from Stanford University. The author has contributed to research in topics: Cognition & Functional neuroimaging. The author has an hindex of 125, co-authored 452 publications receiving 58695 citations. Previous affiliations of Russell A. Poldrack include University of Illinois at Urbana–Champaign & University of Texas at Austin.
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Interpreting developmental changes in neuroimaging signals
TL;DR: It is argued that the appeal to developmental neurobiology is often misplaced, and the concept of “normative” development needs to be reexamined, as it reflects fundamental value judgments about brain development that seem inappropriate for scientific investigation.
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What Can Neuroimaging Tell Us About the Mind?: Insights From Prefrontal Cortex
TL;DR: R reverse inference, wherein activations in well-characterized neural structures serve as markers for the engagement of particular cognitive processes, is considered, which indicates that phonological and semantic processing are consistently associated with topographically distinct patterns of activity in the left inferior prefrontal cortex.
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Automaticity in motor sequence learning does not impair response inhibition
TL;DR: It is demonstrated that the ability to inhibit a motor response does not decrease with automaticity, suggesting that some aspects of automatic behavior are not ballistic.
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Challenges in phenotype definition in the whole-genome era: multivariate models of memory and intelligence
Fred W. Sabb,A. C. Burggren,R. G. Higier,J. Fox,J. He,D. S. Parker,Russell A. Poldrack,W.W. Chu,Tyrone D. Cannon,Nelson B. Freimer,Robert M. Bilder +10 more
TL;DR: This work provides a framework for defining and refining latent constructs used in neuroscience research and then applies this strategy to review known genetic contributions to memory and intelligence in healthy individuals to help build multi-level phenotype models that express the interactions between constructs necessary to understand complex neuropsychiatric diseases.
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Modeling group fMRI data
TL;DR: The need for a mixed effects model is motivated and the different stages of the mixed model used to analyze group fMRI data are outlined.