Teasing apart the anticipatory and consummatory processing of monetary incentives: An event‐related potential study of reward dynamics

  • Keisha D. Novak
    Department of Psychological Science Ball State University Muncie Indiana USA
  • Dan Foti
    Department of Psychological Sciences Purdue University West Lafayette Indiana USA

書誌事項

公開日
2015-07-29
権利情報
  • http://onlinelibrary.wiley.com/termsAndConditions#vor
DOI
  • 10.1111/psyp.12504
公開者
Wiley

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説明

<jats:title>Abstract</jats:title><jats:p>The monetary incentive delay (MID) task has been widely used in fMRI studies to investigate the neural networks involved in anticipatory and consummatory reward processing. Previous efforts to adapt the MID task for use with ERPs, however, have had limited success. Here, we sought to further decompose reward dynamics using a comprehensive set of anticipatory (cue‐N2, cue‐P3, contingent negative variation [CNV]) and consummatory ERPs (feedback negativity [FN], feedback P3 [fb‐P3]). ERP data was recorded during adapted versions of the MID task across two experiments. Unlike previous studies, monetary incentive cues modulated the cue‐N2, cue‐P3, and CNV; however, cue‐related ERPs and the CNV were uncorrelated with one another, indicating distinct anticipatory subprocesses. With regard to consummatory processing, FN amplitude primarily tracked outcome valence (reward vs. nonreward), whereas fb‐P3 amplitude primarily tracked outcome salience (uncertain vs. certain). Independent modulation of the cue‐P3 and fb‐P3 was observed, indicating that these two P3 responses may uniquely capture the allocation of attention during anticipatory and consummatory reward processing, respectively. Overall, across two samples, consistent evidence of both anticipatory and consummatory ERP activity was observed on an adapted version of the MID paradigm, demonstrating for the first time how these ERP components may be integrated with one another to more fully characterize the time course of reward processing. This ERP‐MID paradigm is well suited to parsing reward dynamics, and can be applied to both healthy and clinical populations.</jats:p>

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