Multivariate Analysis of Electrophysiological Signals Reveals the Time Course of Precision Grasps Programs: Evidence for Nonhierarchical Evolution of Grasp Control

Current understanding of the neural processes underlying human grasping suggests that grasp computations involve gradients of higher to lower level representations and, relatedly, visual to motor processes. However, it is unclear whether these processes evolve in a strictly canonical manner from higher to intermediate and to lower levels given that this knowledge importantly relies on functional imaging, which lacks temporal resolution. To examine grasping in fine temporal detail here we used multivariate EEG analysis. We asked participants to grasp objects while controlling the time at which crucial elements of grasp programs were specified. We first specified the orientation with which participants should grasp objects, and only after a delay we instructed participants about which effector to use to grasp, either the right or the left hand. We also asked participants to grasp with both hands because bimanual and left-hand grasping share intermediate-level grasp representations. We observed that grasp programs evolved in a canonical manner from visual representations, which were independent of effectors to motor representations that distinguished between effectors. However, we found that intermediate representations of effectors that partially distinguished between effectors arose after representations that distinguished among all effector types. Our results show that grasp computations do not proceed in a strictly hierarchically canonical fashion, highlighting the importance of the fine temporal resolution of EEG for a comprehensive understanding of human grasp control.

Automated summary

The study used multivariate EEG analysis to investigate human grasping and found that grasp computations do not strictly proceed in a canonical hierarchical manner, evolving from visual to motor representations that distinguish between effectors. The results emphasize the importance of fine temporal resolution for a comprehensive understanding of human grasp control.