Abstract
Different neural systems underlie the evaluation of different types of errors. Recent electroencephalographic evidence suggests that outcome errors—errors indicating the failure to achieve a movement goal—are evaluated within medial-frontal cortex (Krigolson and Holroyd 2006, 2007a, b). Conversely, evidence from a variety of manual aiming studies has demonstrated that target errors—discrepancies between the actual and desired motor command brought about by an unexpected change in the movement environment—are mediated within posterior parietal cortex (e.g., Desmurget et al. 1999, 2001; Diedrichsen et al. 2005). Here, event-related brain potentials (ERP) were recorded to assess medial-frontal and parietal ERP components associated with the evaluation of outcome and target errors during performance of a manual aiming task. In line with previous results (Krigolson and Holroyd 2007a), we found that target perturbations elicited an ERP component with a parietal scalp distribution, the P300. However, the timing of kinematic changes associated with accommodation of the target perturbations relative to the timing of the P300 suggests that the P300 component was not related to the online control of movement. Instead, we believe that the P300 evoked by target perturbations reflects the updating of an internal model of the movement environment. Our results also revealed that an error-related negativity, an ERP component typically associated with the evaluation of speeded response errors and error feedback, was elicited when participants missed the movement target. Importantly, this result suggests that a reinforcement learning system within medial-frontal cortex may play a role in improving subsequent motor output.
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Notes
Onset procedures such as the one we employed here are not typically used on acceleration data in this manner. However, we felt that this was a viable technique for identifying when participants began to correct for the target perturbation. Furthermore, this technique allowed for a direct comparison with the ERP data.
Note that due to the inherent difficulties in single trial ERP analysis we were unable to get an onset value of the P300 for each trial, or for that matter even for individual subjects, and so could not compare this quantity with single-trial acceleration values.
The constant and variable error values reported here for the blocked condition reflect the final endpoint of the participants’ stylus. As the cursor was locked to the horizontal axis in this condition, the vertical error associated with the final cursor location was simply the distance from the midline to the target. Additionally, as there was no variation in the vertical cursor position the variable error of the cursor was always zero.
It is worth noting that a much slower ERN may have been elicited following the corrective movements as participants may have been slow to detect the error in the presence of conflicting feedback. Indeed, the reinforcement learning theory of the ERN would predict this—an ERN should be elicited at the earliest indicator that events are worse than expected (Holroyd and Coles 2002). If participants were not able to evaluate the response itself, then feedback (whenever it is determined) would elicit an ERN. However, in this case participants may have detected the error at different times during the movement trajectory. Given the methodology of the present experiment it is not possible to ascertain whether such temporal instability in fact occurred.
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Acknowledgments
We would like to thank the Michael Smith Foundation for Health Research and the National Sciences and Engineering Research Council of Canada for supporting this research. The first author would also like to thank Kelcey Erlandson for her help with data collection.
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Krigolson, O.E., Holroyd, C.B., Van Gyn, G. et al. Electroencephalographic correlates of target and outcome errors. Exp Brain Res 190, 401–411 (2008). https://doi.org/10.1007/s00221-008-1482-x
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DOI: https://doi.org/10.1007/s00221-008-1482-x