An electrophysiological Monetary Incentive Delay (e-MID) task: a way to decompose the different components of neural response to positive and negative monetary reinforcement

Abstract

Background: The ability to anticipate and then secure future rewards and avoid future punishments by
responding effectively to environmental demands is at the core of successful decision making. Disruptions
to these processes have been shown to be implicated in a number of psychiatric conditions. In the
current paper we use the electrophysiological monetary incentive delay task (e-MID) to decompose the
neural response to (i) reinforcement anticipation, (ii) reinforcement-contingent target processing and
(iii) reinforcement-related feedback.
Methods: Thirty-eight adolescents and young adults performed an ERP-based analogue of the monetary
incentive delay task. ERP components previously associated with motivationally salient cue (cue-P3
and contingent negative variation, CNV), target (P3) and feedback (success vs. failure; feedback-related
negativity; FRN and the late positive potential; LPP) stimuli were examined.
Results: Response times were shorter and less variable in the monetary gain and loss conditions. Distinctive
ERP components were observed for each phase of reinforcement processing. First, cue-P3 was enhanced
to monetary gain cues. Predicted alterations in cue-P3 following monetary loss cues and the CNV following
cues of either monetary loss or gain were not observed. Target P3 was enhanced in both incentive conditions.
The FRN was greater following monetary loss feedback. LPP amplitude was enhanced following
feedback denoting monetary gain and the avoidance of monetary loss.
Conclusion: Although behaviourally the effects of monetary loss and gain were similar, the e-MID task
differentiated neural processing in terms of anticipation and feedback-related brain potentials. The e-
MID task and the results of the current study provide a valuable complement to fMRI-based approaches
to studying normal and abnormal brain correlates of reinforcement processing.