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Rachael D. Seidler

Researcher at University of Florida

Publications -  201
Citations -  13710

Rachael D. Seidler is an academic researcher from University of Florida. The author has contributed to research in topics: Spaceflight & Motor learning. The author has an hindex of 53, co-authored 179 publications receiving 11585 citations. Previous affiliations of Rachael D. Seidler include Arizona State University & Veterans Health Administration.

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Altered Resting State Cortico-Striatal Connectivity in Mild to Moderate Stage Parkinson's Disease

TL;DR: PD and l-DOPA modulate striatal resting state BOLD signal oscillations and cortico-striatal network coherence and these effects are evaluated using resting state functional connectivity MRI in mild to moderate stage Parkinson's patients on and off l- DOPA and age-matched controls.
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A spatial explicit strategy reduces error but interferes with sensorimotor adaptation

TL;DR: Early in learning, explicit instructions greatly reduced movement errors but also resulted in increased trial-to-trial variability and longer reaction times, while late in adaptation, performance was indistinguishable between the explicit and implicit groups, but the mechanisms underlying performance improvements remained fundamentally different, as revealed by catch trials.
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Visuospatial Working Memory Capacity Predicts the Organization of Acquired Explicit Motor Sequences

TL;DR: Results show that individual differences in short-term visuospatial working memory capacity, but not temporal control, predict the temporal structure of explicitly acquired motor sequences.
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Changes in multi-joint performance with age.

TL;DR: To achieve motor performance, elderly persons appear to use coactivation in a manner that is fundamentally different than young adults.
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Neurocognitive Contributions to Motor Skill Learning: The Role of Working Memory

TL;DR: The authors propose that spatial working memory is relied on for processing motor error information to update motor control for subsequent actions and suggest that working memory are relied on during learning new action sequences for chunking individual action elements together.