Carrying the parietal how pathway forward, visual information going along the dorsal pathway through the parietal cortex heads directly into the frontal cortex, where it can drive motor neurons in primary motor cortex, which can directly drive the muscles to produce overt motor actions. This completes the critical sensory-motor loop that lies at the core of all behavior. Motor control also critically involves many subcortical brain areas, including the basal ganglia and cerebellum. The rough division of labor between these areas is:
- Neocortex (parietal to frontal) -- does high-level metrical processing of sensory information, integrating multiple modalities and translating between different reference frames as necessary, to arrive at a range of possible responses to the current sensory environment.
- Basal Ganglia -- receives both sensory inputs and the potential responses being "considered" in frontal cortex, and can then trigger a disinhibitory Go signal that enables the best of the possible actions to get over threshold and actually drive behavior. This process of action selection is shaped by reinforcement learning -- the basal ganglia are bathed in dopamine, which drives learning in response to rewards and punishments, and also influences the speed of the selection process itself. Thus, the basal ganglia selects the action that is most likely to result in reward, and least likely to result in punishment. The amygdala plays a key role in driving these dopamine signals in response to sensory cues associated with reward and punishment.
- Cerebellum -- is richly interconnected with the parietal and motor cortex, and it is capable of using a simple yet powerful form of error-driven learning to acquire high-resolution metrical maps between sensory inputs and motor outputs. Thus, it is critical for generating smooth, coordinated motor movements that properly integrate sensory and motor feedback information to move in an efficient and controlled manner. It also likely serves to teach the parietal and motor cortex what it has learned.
In Motor Control and Reinforcement Learning, we will see how dopamine signals shape basal ganglia learning and performance in a basic action selection task. Then, we'll explore a fascinating model of cerebellar motor learning in a virtual robot that performs coordinated eye and head movements to fixate objects -- this model shows how the error signals needed for cerebellar learning can arise naturally.
Interestingly, all of these "low level" motor control systems end up being co-opted by "higher level" executive function systems (e.g., the prefrontal cortex), so although some don't think of motor control as a particularly cognitive domain, it actually provides a solid foundation for understanding some of the highest levels of cognitive function!