Individual differences and modulation of conflict monitoring intensity as a determinant of avoidance learning: Evidence for an integrative ACC theory?

作为先前研究结果的延伸，我们试图在前扣带回(ACC)活动的最新理论的背景下，研究冲突监控和被动回避学习。几年来，这两位研究人员作为一对双职夫妇成功地合作，自2012年以来，他们在波恩大学正式合作。到目前为止，作为强化学习的一种形式，冲突监控和被动回避学习的神经过程被比较独立地研究。然而，只要还没有像Botvinick假设的那样，在任务执行过程中，强化的冲突监控是否会导致强化的被动回避学习，我们就不能得出结论，强化的冲突监控在决策过程和被动回避学习过程中是一个反面的教学信号。然而，这些调查结果将进一步推动关于行政协调会职能相关性的最新和广泛的理论辩论。提高冲突监控强度和随后的被动回避学习的一种可能性是对动机因素的准实验操纵。在这方面，在研究1中，我们的目标是通过在执行歧视任务期间使用最大认知努力与平均认知努力的逐次试验治疗来改变个体的动机。我们预期，由个体努力动机引起的冲突监控强度的增加应该会强化被动回避学习。如果强化的冲突监测和随后强化的被动回避学习对于日常环境很重要，那么强化的冲突监测对回避学习的影响应该推广到嵌入到日常环境中的歧视任务。在研究2中，参与者执行基于描述更自然背景的小插曲的歧视任务。初步研究表明，通过基于面孔的辨别任务和基于背景的辨别任务，可以诱导冲突监测和被动回避学习。此外，对同级的存在进行了操纵，以调节冲突监控，从而实现被动回避学习。为了验证我们的假设，通过大样本(每项研究超过120名参与者)的多通道脑电和行为参数来评估众所周知的事件相关电位。为了研究冲突监控中的个体差异与被动回避学习的直接关系，本研究采用成长模型作为一种创新的统计分析工具。

As an extension of prior findings illustrating the modulation of the conflict monitoring intensity by means of required effort and aversive reinforcement we aim at investigating conflict monitoring and passive avoidance learning in the context of recent theories on anterior cingulate cortex (ACC) activity. For several years, both researchers have successfully collaborated as a dual-career couple as they officially do since 2012 at the University of Bonn. Till date, the neural processes of conflict monitoring and passive avoidance learning as one form of reinforcement learning have been investigated rather independently. However, as long as it has not yet been investigated whether, as postulated by Botvinick, an intensified conflict monitoring results in an intensified passive avoidance learning during task performance, we cannot conclude that an intensified conflict monitoring serves as an aversive teaching signal in decision-making processes and during passive avoidance learning. Those findings would, however, further the most recent and extensive theoretical debate on the functional relevance of the ACC. One possibility to enhance the conflict monitoring intensity and subsequent passive avoidance learning is the quasi-experimental manipulation of motivational factors. In this respect, in Study 1 we aim at varying an individuals motivation by using a maximal vs. average cognitive effort trial-by-trial treatment during performance of a discrimination task. We expect that an increased conflict monitoring intensity induced by means of an individuals effort motivation should intensify passive avoidance learning. If an intensified conflict monitoring and subsequently an intensified passive avoidance learning are of importance for every-day contexts the effect of intensified conflict monitoring on avoidance learning should generalize to discrimination tasks that are embedded into every-day contexts. In Study 2, participants perform discrimination tasks that are based on a vignette describing a more natural context. The possibility to induce conflict monitoring and passive avoidance learning by means of a discrimination task based on faces and a discrimination task based on a vignette has been shown in preliminary studies. Moreover, the presence of peers is manipulated in order to modulate conflict monitoring and thereby passive avoidance learning. To test our hypotheses, well-known event-related potentials are assessed by means of a multi-channel electroencephalogram along with behavioral parameters in large samples (more than 120 participants per study). To investigate the direct relations of individual differences in conflict monitoring and passive avoidance learning growth-modeling is applied as an innovative statistical analysis tool.