Samuel D. McDougle

This study investigates how human premotor cortex encodes sensorimotor transformations and finds that it represents both movement direction and transformation context, with performance linked to transformation context encoding.

This study shows that learned action values modulate error-based motor adaptation, revealing an interaction between reinforcement learning and motor memory.

This study investigates how temporal intervals act as contextual cues for retrieving distinct motor memories, showing that elapsed time can be used to index and generalize motor adaptations.

This study shows that motor abstraction training leads to skill improvements comparable to repetitive practice, demonstrating a causal role for motor abstractions in learning.

This study shows the cerebellum encodes prediction errors during nonmotor statistical learning, suggesting a domain-general role in error-based learning.

This study shows that speech adaptation occurs in response to auditory prediction errors even when no movement is executed, challenging models that rely on corrective motor commands.

This study identifies temporally sensitive reinforcement learning signals in human cerebellar regions during an RL task with controlled feedback delays.

This study shows humans can detect rising and falling pitch using positive and negative spectrotemporal intensity correlations, analogous to visual motion detection.

The paper investigates how motor working memory (MWM) contributes to explicit and implicit motor learning by testing correlations between effector-independent and effector-specific MWM and learning outcomes.

The paper decouples movement distance and visual distance in Fitts' Law, showing both influence movement times and that their weighting depends on sensory context.

The paper investigates an optimal error rate for motor skill learning and finds that performance improves most at around 30% error rate.

This study models how confidence judgments during visuomotor learning integrate recent error history and environmental volatility.

This study applies associative learning principles to sensorimotor adaptation, finding three key signatures of associative learning and a negative correlation consistent with the additivity principle.

This paper shows that motor learning can occur without actual movement by demonstrating implicit adaptation based on planned movements and simulated sensory feedback.

This study investigates the role of the cerebellum in cognitive tasks by comparing performance of individuals with cerebellar degeneration on tasks requiring continuous mental operations versus discrete operations.

The paper examines how the brain processes selection and execution errors using EEG, finding distinct neural signatures for each type of error.

This study investigates motor generalization by examining the role of top-down decisions in transferring learned movements to novel contexts, finding a strong link between subjective similarity ratings and motor performance.

The paper shows that prolonged reaction times allow for full compensation in visuomotor adaptation tasks by reducing residual errors through increased planning time.

This study quantifies surprise during naturalistic sports viewing and identifies its behavioral, physiological, and neural correlates using basketball analytics and human observers.

The paper investigates how the brain learns from transient goals using behavior, modeling, and neuroimaging, finding that executive function and reward circuits interact to support flexible learning.

The paper investigates the role of explicit learning in force field adaptation using two novel experimental approaches and finds that learners can voluntarily control and report aspects of force production.

The paper investigates how prediction errors in decision-making tasks are influenced by action execution failures using fMRI and behavioral experiments.

This study identifies two distinct working memory strategies in sensorimotor learning: discrete response caching and parametric computations, showing how they trade off based on task complexity and learning progress.