Neural mechanisms of cue-approach training.
Neural mechanisms of cue-approach training.
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DOI：
10.1016/j.neuroimage.2016.09.059
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发表时间：
2017-05-01
期刊：
影响因子：
5.7
通讯作者：
Schonberg T
中科院分区：
文献类型：
--
作者：
Bakkour A;Lewis-Peacock JA;Poldrack RA;Schonberg T
偏见选择可能被证明是实现行为改变的有用方法。先前的工作表明，不依赖于外部强化的简单训练任务（线索方法任务）可以通过将选择偏向训练期间的目标项目来强烈影响选择行为。在当前的研究中，我们复制了之前的行为发现，并探索了提示方法训练后偏好转变的神经机制。鉴于最近在基于任务的功能磁共振成像数据上开发和应用机器学习技术取得了成功，这对认知的神经基础有了深入的了解，我们试图利用这些技术的力量来更好地理解线索方法训练期间的神经变化，这些变化随后导致了选择行为的转变。与我们的预期相反，我们发现在非强化训练期间应用于功能磁共振成像数据的机器学习技术未能成功阐明行为效应背后的神经机制。然而，训练期间的单变量分析表明，与主要在外侧前额皮质区域选择 NoGo 项目相比，BOLD 与 Go 项目选择之间的关系随着训练的进展而增加。这一新的成像发现表明，偏好是通过任务控制网络的差异参与而改变的，这些任务控制网络在线索方法训练期间与价值网络相互作用。
Biasing choices may prove a useful way to implement behavior change. Previous work has shown that a simple training task (the cue-approach task), which does not rely on external reinforcement, can robustly influence choice behavior by biasing choice toward items that were targeted during training. In the current study, we replicate previous behavioral findings and explore the neural mechanisms underlying the shift in preferences following cue-approach training. Given recent successes in the development and application of machine learning techniques to task-based fMRI data, which have advanced understanding of the neural substrates of cognition, we sought to leverage the power of these techniques to better understand neural changes during cue-approach training that subsequently led to a shift in choice behavior. Contrary to our expectations, we found that machine learning techniques applied to fMRI data during non-reinforced training were unsuccessful in elucidating the neural mechanism underlying the behavioral effect. However, univariate analyses during training revealed that the relationship between BOLD and choices for Go items increases as training progresses compared to choices of NoGo items primarily in lateral prefrontal cortical areas. This new imaging finding suggests that preferences are shifted via differential engagement of task control networks that interact with value networks during cue-approach training.