The theory of dynamic patterns suggests that during continuous motion, the motor control system state evolves over time in search of stable patterns.
In summary, whereas MCI 1 seems to represent a state of cognitive motor control [ 35], MCI 2 suggests a state of more general alertness and automatic motor control as supported by the participation of parts of the default mode network [ 57].
His PhD thesis, entitled "Motor control and learning by the state space model," used robotics techniques to model biological behavior.
In mammals, serotonergic neurotransmission regulates a wide variety of neurobehavioral processes, including cognition, affective states, ingestive behavior, motor control, and sensorimotor integration [1] [6].
A multi-disciplinary look at the current state of knowledge regarding motor control and movement from molecular biology to robotics The last two decades have seen a dramatic increase in the number of sophisticated tools and methodologies for exploring motor control and movement.
A large body of literature demonstrates that information regarding body state is crucial for motor control.[ 4, 6, 31- 33] In this study, we sought to determine whether this was also the case for motor sequence learning.
Optimal motor control and learning require predictions of both the future states of the motor system and the sensory consequences of movement (Wolpert & Ghahramani, 2000).
Oztop et al. [22] developed a computational model of mental-state inference that used the circuitry that underlay motor control.
This is a bit counter-intuitive, as much recent theory of motor control has shifted towards a more dominant role for prediction and state estimation [10 12].
PT output provides online information about the state of cortical activation to downstream areas involved in motor control.
