Predictive steering: integration of artificial motor signals in self-motion estimation.

The brain's computations for active and passive self-motion estimation can be unified with a single model that optimally combines vestibular and visual signals with sensory predictions based on efference copies. It is unknown whether this theoretical framework also applies to the integration of artificial motor signals, such as those that occur when driving a car, or whether self-motion estimation in this situation relies on sole feedback control. Here, we examined if training humans to control a self-motion platform leads to the construction of an accurate internal model of the mapping between the steering movement and the vestibular reafference.

Cortical beta-band power modulates with uncertainty in effector selection during motor planning.

Although beta-band activity during motor planning is known to be modulated by uncertainty about where to act, less is known about its modulations to uncertainty about how to act. To investigate this issue, we recorded oscillatory brain activity with EEG while human participants ( = 17) performed a hand choice reaching task. The reaching hand was either predetermined or of participants' choice, and the target was close to one of the two hands or at about equal distance from both.