Motor Control and Adaptation

This study examines how decision and action outcomes influence subsequent decision and action behaviors in humans, finding that they are coordinated rather than independent.

The paper reframes statistical learning as a dynamic stochastic process influenced by motor system dynamics, identifying two learning strategies based on environmental stability.

The paper investigates how cerebellar associative learning influences skilled reach adaptation in mice by analyzing neural responses and optogenetic perturbations.

This study tests if context-dependent switching of feedback controllers occurs in human motor control through experimental and computational analysis of visuomotor responses in two tasks.

This paper shows that probabilistic context inference affects motor adaptation and control by influencing switching-task behavior and action selection in simulated experiments.

This study investigates the functional organization of the cerebellum in mice during motor learning and identifies three operational principles that challenge the conventional view of cerebellar architecture.

This study compares motor lateralization in young and older adults during vertical arm movements, finding reduced lateralization in older adults.

This study shows that repeated unilateral handgrip contractions at 50% MVC improve contralateral hand response times and alter functional connectivity and neurochemical levels in sensorimotor areas.

The paper shows that humans can adjust confidence ratings for unaware visuomotor deviations using summary statistics, challenging the idea of limited action monitoring.

The paper identifies the bilateral frontal operculum's role in monitoring manual action performance and error processing using visual or proprioceptive feedback.

The paper investigates use-dependent learning (UDL) and finds that movement biases arise from both contaminated motor plans and execution biases, with planning dynamics showing a stronger influence.

The paper identifies common synaptic inputs to spinal motor neurons during a multi-joint task, revealing neural synergies that partially decouple from muscle innervation.

The paper shows that using endpoint feedback in a visuomotor task promotes cognitive strategy use, leading to better intermanual transfer of learning compared to continuous feedback.

This study investigates the neural mechanisms underlying adaptive motor behavior during car driving by comparing brain activity before and after a sudden change in driving conditions.

This study compares how reward affects motor performance in young and older adults during sequential reaching tasks, finding reward improves performance in both groups.

This paper presents a multimodal, synchronized system for simultaneous hand, eye, and brain recording during a visuomotor task and reports pilot results validating its use.

This study shows that motor learning homogenizes task space error distribution during reaching tasks, reducing directional error dependence and improving control efficiency.

The paper shows that sequence planning signals in the FEF are preserved in neck EMG but delayed for the second saccade, suggesting both direct and indirect pathways contribute to motor control during sequential saccades.

The paper analyzes error and successful trials in oculomotor tasks across marmosets and humans, finding similar behavioral patterns and suggesting marmosets as a model for studying human oculomotor behavior.

The paper shows humans adapt grip force to friction changes within 370ms by using skin deformation patterns detected by mechanoreceptive afferents.

The paper decodes insect flight behaviors using spike-resolved motor patterns and shows that consistent coordination patterns allow near-perfect decoding across different behaviors.

This paper introduces a hierarchical controller architecture for dynamic limb interactions, showing it can generalize motor strategies across different environmental conditions.

The paper investigates how contraction-dependent intracortical inhibition supports gravity-efficient motor control during vertical arm movements.

This paper presents a spiking neural model of the cerebellum that reproduces human motor adaptation behaviors and predicts effects of cerebellar damage.

The paper develops an artificial neural network to model finger dexterity recovery after stroke, revealing neural plasticity and pathway interactions that contribute to improved motor control.

The paper investigates how self-generated vs. externally generated movements affect the timing, duration, and amplitude of the BOLD response during visual feedback processing using fMRI.

The paper compares reinforcement learning models to human behavior in sensorimotor games, finding that Q-learning with intrinsic costs best explains the data.

The paper shows that decreased DMS direct pathway neuronal activity, dependent on dopamine, is required for motor coordination learning, while DLS activity remains stable and correlates with skill maintenance.

This study shows that augmenting variability in the task space during motor learning improves consolidation, while variability in the null space does not, due to its impact on pre-existing coordination strategies.

This study shows that intrinsic somatosensory signals from body parts controlling a robotic device support motor learning and performance, even when artificial feedback is absent.