School of Communication, The Ohio State University, 3105 Derby Hall, 154 North Oval Mall, Columbus, OH, 43210, USA.
Department of Communication, University of California, Santa Barbara, CA, USA.
Cogn Affect Behav Neurosci. 2018 Oct;18(5):902-924. doi: 10.3758/s13415-018-0612-6.
Cognitive control is a framework for understanding the neuropsychological processes that underlie the successful completion of everyday tasks. Only recently has research in this area investigated motivational contributions to control allocation. An important gap in our understanding is the way in which intrinsic rewards associated with a task motivate the sustained allocation of control. To address this issue, we draw on flow theory, which predicts that a balance between task difficulty and individual ability results in the highest levels of intrinsic reward. In three behavioral and one functional magnetic resonance imaging studies, we used a naturalistic and open-source video game stimulus to show that changes in the balance between task difficulty and an individual's ability to perform the task resulted in different levels of intrinsic reward, which is associated with different brain states. Specifically, psychophysiological interaction analyses show that high levels of intrinsic reward associated with a balance between task difficulty and individual ability are associated with increased functional connectivity between key structures within cognitive control and reward networks. By comparison, a mismatch between task difficulty and individual ability is associated with lower levels of intrinsic reward and corresponds to increased activity within the default mode network. These results suggest that intrinsic reward motivates cognitive control allocation.
认知控制是一个理解神经心理学过程的框架，这些过程是成功完成日常任务的基础。直到最近，该领域的研究才开始探讨动机对控制分配的贡献。我们理解中的一个重要空白是与任务相关的内在奖励激励控制持续分配的方式。为了解决这个问题，我们借鉴了流动理论，该理论预测任务难度和个人能力之间的平衡会产生最高水平的内在奖励。在三项行为和一项功能磁共振成像研究中，我们使用了一种自然和开源的视频游戏刺激，以表明任务难度和个人执行任务能力之间平衡的变化会导致不同水平的内在奖励，而内在奖励与不同的大脑状态有关。具体来说，心理生理交互分析表明，与任务难度和个人能力之间的平衡相关的高水平内在奖励与认知控制和奖励网络内关键结构之间的功能连接增加有关。相比之下，任务难度和个人能力之间的不匹配与较低水平的内在奖励有关，并且与默认模式网络内的活动增加相对应。这些结果表明，内在奖励激励认知控制分配。
