Beyond the neural underpinnings of action emulation in expert athletes: An EEG study.

Athletes specializing in sports demanding rapid predictions and hand-eye coordination are highly trained in predicting the consequences of motor commands. This can be framed as highly efficient action emulation, but the neural underpinnings of this remain elusive. We examined the neural processes linked to the training effect of athletes (4000 h of training) by employing a continuous pursuit tracking task and EEG data. We manipulated feedback availability by intermittently occluding the cursor. As a performance measure, we used the distance between cursor and target (position error), the angle between the cursor and target movement direction (direction error) and the magnitude of cursor acceleration (acceleration error) to quantify movement strategy. In EEG data, we investigated beta, alpha, and theta frequency band oscillations. Athletes' position error is lower than non-athletes' when there is no feedback about the cursor location, but direction error is not. We found no quantitative power differences in the investigated frequency bands, but evidence that athletes and non-athletes accomplish action emulation through different functional neuroanatomical structures, especially when alpha and beta band activity is concerned. We surmise that non-athletes seemed to rely on top-down inhibitory control to predict guesses on cursor trajectories in the absence of cursor position feedback. In contrast, athletes might benefit from enhanced inhibitory gating mechanisms in the ventral stream and the integration of sensory and motor processes in the insular cortex, which could provide them with processing advantages in computing forward models. We further reflect that this advantage might be supported by alpha band activity in athletes' motor cortex, suggesting less inhibitory gating and a higher likelihood of executing integrated sensorimotor programs. We posit that current framings of neuroanatomical structures and neurophysiological processes in the action emulation framework must be revised to better capture superior motor performance.