University of Cambridge, UK.
J Cogn Neurosci. 2011 Apr;23(4):936-46. doi: 10.1162/jocn.2010.21456. Epub 2010 Feb 10.
We assessed electrophysiological activity over the medial frontal cortex (MFC) during outcome-based behavioral adjustment using a probabilistic reversal learning task. During recording, participants were presented two abstract visual patterns on each trial and had to select the stimulus rewarded on 80% of trials and to avoid the stimulus rewarded on 20% of trials. These contingencies were reversed frequently during the experiment. Previous EEG work has revealed feedback-locked electrophysiological responses over the MFC (feedback-related negativity; FRN), which correlate with the negative prediction error [Holroyd, C. B., & Coles, M. G. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002] and which predict outcome-based adjustment of decision values [Cohen, M. X., & Ranganath, C. Reinforcement learning signals predict future decisions. Journal of Neuroscience, 27, 371-378, 2007]. Unlike previous paradigms, our paradigm enabled us to disentangle, on the one hand, mechanisms related to the reward prediction error, derived from reinforcement learning (RL) modeling, and on the other hand, mechanisms related to explicit rule-based adjustment of actual behavior. Our results demonstrate greater FRN amplitudes with greater RL model-derived prediction errors. Conversely expected negative outcomes that preceded rule-based behavioral reversal were not accompanied by an FRN. This pattern contrasted remarkably with that of the P3 amplitude, which was significantly greater for expected negative outcomes that preceded rule-based behavioral reversal than for unexpected negative outcomes that did not precede behavioral reversal. These data suggest that the FRN reflects prediction error and associated RL-based adjustment of decision values, whereas the P3 reflects adjustment of behavior on the basis of explicit rules.
我们使用基于结果的行为调整概率反转学习任务评估内侧前额皮质（MFC）的电生理活动。在记录过程中，参与者在每次试验中都会看到两个抽象的视觉模式，并且必须选择在 80%的试验中受到奖励的刺激，避免在 20%的试验中受到奖励的刺激。在实验过程中，这些条件经常反转。以前的 EEG 研究揭示了 MFC 上的反馈锁定电生理反应（反馈相关负波；FRN），它与负预测误差相关[Holroyd，CB 和 Coles，MG 人类错误处理的神经基础：强化学习，多巴胺和错误相关负波。心理评论，109，679-709，2002]，并且可以预测基于结果的决策值调整[Cohen，MX 和 Ranganath，C 强化学习信号预测未来决策。神经科学杂志，27，371-378，2007]。与以前的范式不同，我们的范式使我们能够一方面分解与强化学习（RL）建模相关的奖励预测误差相关的机制，另一方面分解与实际行为的明确基于规则的调整相关的机制。我们的结果表明，RL 模型得出的预测误差越大，FRN 幅度越大。相反，基于规则的行为反转之前预期的负面结果并没有伴随着 FRN。这种模式与 P3 振幅形成鲜明对比，基于规则的行为反转之前预期的负面结果比不先于行为反转的意外负面结果产生的 P3 振幅显著更大。这些数据表明，FRN 反映了预测误差和基于 RL 的决策值调整，而 P3 反映了基于明确规则的行为调整。
