Tianhe Wang

This study shows that implicit recalibration becomes flexible after prolonged training on novel sensorimotor mappings, allowing context-specific adaptation.

This study examines Bayesian inference in cerebellar degeneration by testing patients on timing tasks and developing a model of cerebellar microcircuits.

The paper shows that motor control uses two statistical processes on different timescales to improve both speed and accuracy of movements.

The paper shows motor biases result from a misalignment between visual and proprioceptive reference frames, not from planning errors or biomechanical constraints.

This paper identifies two distinct implicit processes in sensorimotor adaptation: implicit recalibration for action execution and implicit aiming for action selection, showing different temporal stability and contextual modulation.

The paper investigates motor planning by comparing Bayesian and efficient coding models, finding that movement biases and variability support efficient coding in dynamic environments.

This study investigates how attention influences sensorimotor adaptation, finding that attention modulates learning by defining contextual cues rather than directly affecting adaptation rates.

This paper shows that perturbation variability does not affect implicit sensorimotor adaptation, as non-linear error correction explains observed effects without requiring changes in error sensitivity.

This study investigates how feedback timing affects sensorimotor adaptation, finding that optimal feedback timing occurs before hand reaching the target, similar to continuous feedback.

This study shows that binary feedback can drive implicit motor learning, as evidenced by a consistent aftereffect in a center-out reaching task.

The paper investigates serial dependence in temporal perception and shows that working memory modulates this effect, with stronger dependence observed at longer delays.

This paper introduces a population-coding model of the cerebellum to explain how sensorimotor learning is modulated by contextual variation, showing that contextual effects emerge from population dynamics rather than meta-learning.

The paper shows that time perception in humans is governed by a single mechanism that adapts to both local and global environmental statistics.

This paper shows that sensorimotor adaptation results from a rigid motor correction function, not from an implicit memory of errors, refuting a previous claim.

The paper investigates how internal reference frames are constructed in temporal perception by analyzing serial dependence in duration estimation tasks and proposes a Bayesian model to explain the findings.