RT Journal Article
SR Electronic
T1 Does the Processing of Sensory and Reward-Prediction Errors Involve Common Neural Resources? Evidence from a Frontocentral Negative Potential Modulated by Movement Execution Errors
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 4845
OP 4856
DO 10.1523/JNEUROSCI.4390-13.2014
VO 34
IS 14
A1 Flavie Torrecillos
A1 Philippe Albouy
A1 Thomas Brochier
A1 Nicole Malfait
YR 2014
UL http://www.jneurosci.org/content/34/14/4845.abstract
AB In humans, electrophysiological correlates of error processing have been extensively investigated in relation to decision-making theories. In particular, error-related ERPs have been most often studied using response selection tasks. In these tasks, involving very simple motor responses (e.g., button press), errors concern inappropriate action-selection only. However, EEG activity in relation to inaccurate movement-execution in more complex motor tasks has been much less examined. In the present study, we recorded EEG while volunteers performed reaching movements in a force-field created by a robotic device. Hand-path deviations were induced by interspersing catch trials in which the force condition was unpredictably altered. Our goal was twofold. First, we wanted to determine whether a frontocentral ERP was elicited by sensory-prediction errors, whose amplitude reflected the size of kinematic errors. Then, we explored whether common neural processes could be involved in the generation of this ERP and the feedback-related negativity (FRN), often assumed to reflect reward-prediction errors. We identified a frontocentral negativity whose amplitude was modulated by the size of the hand-path deviations induced by the unpredictable mechanical perturbations. This kinematic error-related ERP presented great similarities in terms of time course, topography, and potential source-location with the FRN recorded in the same experiment. These findings suggest that the processing of sensory-prediction errors and the processing of reward-prediction errors could involve a shared neural network.