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前扣带皮层中学习奖励和错误结果预测之间的竞争。

Competition between learned reward and error outcome predictions in anterior cingulate cortex.

机构信息

Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.

出版信息

Neuroimage. 2010 Feb 15;49(4):3210-8. doi: 10.1016/j.neuroimage.2009.11.065. Epub 2009 Dec 1.

Abstract

The anterior cingulate cortex (ACC) is implicated in performance monitoring and cognitive control. Non-human primate studies of ACC show prominent reward signals, but these are elusive in human studies, which instead show mainly conflict and error effects. Here we demonstrate distinct appetitive and aversive activity in human ACC. The error likelihood hypothesis suggests that ACC activity increases in proportion to the likelihood of an error, and ACC is also sensitive to the consequence magnitude of the predicted error. Previous work further showed that error likelihood effects reach a ceiling as the potential consequences of an error increase, possibly due to reductions in the average reward. We explored this issue by independently manipulating reward magnitude of task responses and error likelihood while controlling for potential error consequences in an Incentive Change Signal Task. The fMRI results ruled out a modulatory effect of expected reward on error likelihood effects in favor of a competition effect between expected reward and error likelihood. Dynamic causal modeling showed that error likelihood and expected reward signals are intrinsic to the ACC rather than received from elsewhere. These findings agree with interpretations of ACC activity as signaling both perceptions of risk and predicted reward.

摘要

扣带前回(ACC)与表现监控和认知控制有关。非人类灵长类动物的 ACC 研究显示出明显的奖励信号,但在人类研究中却难以捉摸,反而主要显示出冲突和错误效应。在这里,我们证明了人类 ACC 中存在明显的奖赏和厌恶活动。错误可能性假说表明,ACC 活动会随着错误的可能性成比例增加,并且 ACC 还对预测错误的结果大小敏感。先前的工作进一步表明,随着错误的潜在后果增加,错误可能性效应达到上限,这可能是由于平均奖励减少所致。我们通过在激励变化信号任务中独立操纵任务反应的奖励大小和错误可能性,同时控制潜在的错误后果,来探讨这个问题。fMRI 结果排除了预期奖励对错误可能性效应的调节作用,而有利于预期奖励和错误可能性之间的竞争效应。动态因果建模表明,错误可能性和预期奖励信号是 ACC 的固有信号,而不是来自其他地方的信号。这些发现与将 ACC 活动解释为既能感知风险又能预测奖励的解释一致。

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