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An electrophysiological monetary incentive delay (e-MID) task: a way to decompose the different components of neural
Samantha J Broyd1, Helen J Richards, Suzannah K Helps
1Institute for Disorders of Impulse & Attention, Developmental Brain-Behaviour Laboratory, School of Psychology, University of Southampton, UK.
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.
