Cerebral changes during performance of overlearned arbitrary visuomotor associations

The posterior parietal cortex (PPC) is known to be involved in the control of automatic movements that are spatially guided, such as grasping an apple. We considered whether the PPC might also contribute to the performance of visuomotor associations in which stimuli and responses are linked arbitrarily, such as producing a certain sound for a typographical character when reading aloud or pressing pedals according to the color of a traffic light when driving a motor vehicle. The PPC does not appear to be necessary for learning new arbitrary visuomotor associations, but with extensive training, the PPC can encode nonspatial sensory features of task-relevant cues. Accordingly, we have tested whether the contributions of the PPC might become apparent once arbitrary sensorimotor mappings are overlearned. We have used functional magnetic resonance imaging to measure cerebral activity while subjects were learning novel arbitrary visuomotor associations, overlearning known mappings, or attempting to learn frequently changing novel mappings. To capture the dynamic features of cerebral activity related to the learning process, we have compared time-varying modulations of activity between conditions rather than average (steady-state) responses. Frontal, striatal, and intraparietal regions showed decreasing or stable activity when subjects learned or attempted to learn novel associations, respectively. Importantly, the same frontal, striatal, and intraparietal regions showed time-dependent increases in activity over time as the mappings become overlearned, i.e., despite time-invariant behavioral responses. The automaticity of these mappings predicted the degree of intraparietal changes, indicating that the contribution of the PPC might be related to a particular stage of the overlearning process. We suggest that, as the visuomotor mappings become robust to interference, the PPC may convey relevant sensory information toward the motor cortex. More generally, our findings illustrate how rich cerebral dynamics can underlie stable behavior.