The Reward Positivity (RewP) is a neurophysiological marker of reward sensitivity that has been found to be impacted in depression. However, there have been some mixed findings regarding the relationship between the RewP and depression, suggesting there are other factors which impact this relationship. The current study investigated how the demographic factors of sex, age, and socio-economic status might moderate the RewP-depression relationship, and examined if these effects generalize across three different inventories for symptoms of depression. 194 people were recruited by random digit dialing (55.2% male, mean age = 51.34 years, mean monthly income = $6625.95). They completed the SCID, HAM-D, and IDAS measures of depression, and an EEG session in which they did a random guessing task to elicit the RewP. We found that there was a trend-level interaction of a moderate effect size between symptoms of depression, age, and sex in predicting RewP amplitude. Further exploration of this interaction revealed that for females, there was an interactive effect between age and symptoms of depression, such that for younger females, increased symptoms of depression were associated with a blunted RewP, and lower symptoms of depression were associated with an enhanced RewP. These effects were specific to the SCID, but did not generalize to the HAM-D or IDAS. Moreover, there was no interactive effects between age and depression symptoms for males, nor did SES interact with depression and other demographic factors in predicting the RewP. This study provides evidence that demographic factors can impact the strength and nature of the relationships between the RewP and depression, and that future researchers might wish to over-sample younger females when investigating other moderating factors of the RewP in order to increase power.

現今解釋錯誤偵測系統及前扣帶皮質（ACC）的關係之理論主要為增強學習理論。增強學習理論認為個體會在行為後對於行為結果產生預期，並將該預期與實際結果進行比較，若實際結果較預期結果差則會活化ACC進而觀察到較大的FN（Feedback Negativity）振幅。近年來有學者提出奠基於增強學習理論的錯誤可能性理論，錯誤可能性理論則認為當個體在學習到行為與結果之間的關聯後，當接收到可能犯錯的訊息時便會活化ACC而引起較大的FN。本研究主要的目的為探討增強學習理論及錯誤可能性理論的適用性，其次為探討風險之因素是否能反映於FN上。由於兩理論對於風險情境中是否會觀察到FN有不同的預測，錯誤可能性理論預測會在高風險的情況下觀察到較大的FN；而增強學習理論則預測由於風險畫面並非回饋畫面，故風險不會影響FN。實驗一藉由探討風險與FN間之關係企圖提供兩理論初步之區分並提供風險研究的實驗證據，實驗一結果顯示FN確實會反映風險之因素。也點出增強學習理論用以解釋錯誤偵測系統之不完備之處。而實驗二則利用操弄回饋結果好壞及高低錯誤可能性以檢驗錯誤可能性對於錯誤偵測系統之必要性，實驗二結果顯示錯誤可能性為事件評估之因素之一。除此之外，實驗二亦提供FN反映懊悔之支持性證據。