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Signal-dependent noise determines motor planning
TLDR
This theory provides a simple and powerful unifying perspective for both eye and arm movement control and accurately predicts the trajectories of both saccades and arm movements and the speed–accuracy trade-off described by Fitt's law.
An internal model for sensorimotor integration.
TLDR
A sensorimotor integration task was investigated in which participants estimated the location of one of their hands at the end of movements made in the dark and under externally imposed forces, providing direct support for the existence of an internal model.
Internal models in the cerebellum
A unifying computational framework for motor control and social interaction.
TLDR
The extent to which motor commands acting on the body can be equated with communicative signals acting on other people is examined and it is suggested that computational solutions for motor control may have been extended to the domain of social interaction.
Computational principles of movement neuroscience
TLDR
This goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.
Principles of sensorimotor learning
TLDR
Here a review of recent research in human motor learning with an emphasis on the computational mechanisms that are involved is reviewed.
Bayesian integration in sensorimotor learning
TLDR
This work shows that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process.
Abnormalities in the awareness and control of action.
TLDR
The location of the neural damage associated with these disorders suggests that representations of the current and predicted state of the motor system are in parietal cortex, while representations of intended actions are found in prefrontal and premotor cortex.
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