Chiu Yu-Chin, Jiang Jiefeng, Egner Tobias
Center for Cognitive Neuroscience and
Center for Cognitive Neuroscience and.
J Neurosci. 2017 Jan 25;37(4):1028-1038. doi: 10.1523/JNEUROSCI.0778-16.2016.
A longstanding dichotomy in cognitive psychology and neuroscience pits controlled, top-down driven behavior against associative, bottom-up driven behavior, where cognitive control processes allow us to override well-learned stimulus-response (S-R) associations. By contrast, some previous studies have raised the intriguing possibility of an integration between associative and controlled processing in the form of stimulus-control state (S-C) associations, the learned linkage of specific stimuli to particular control states, such as high attentional selectivity. The neural machinery mediating S-C learning remains poorly understood, however. Here, we combined human functional magnetic resonance imaging (fMRI) with a previously developed Stroop protocol that allowed us to dissociate reductions in Stroop interference based on S-R learning from those based on S-C learning. We modeled subjects' acquisition of S-C and S-R associations using an associative learning model and then used trial-by-trial S-C and S-R prediction error (PE) estimates in model-based behavioral and fMRI analyses. We found that PE estimates derived from S-C and S-R associations accounted for the reductions in behavioral Stroop interference effects in the S-C and S-R learning conditions, respectively. Moreover, model-based fMRI analyses identified the caudate nucleus as the key structure involved in selectively updating stimulus-control state associations. Complementary analyses also revealed a greater reliance on parietal cortex when using the learned S-R versus S-C associations to minimize Stroop interference. These results support the emerging view that generalizable control states can become associated with specific bottom-up cues, and they place the caudate nucleus of the dorsal striatum at the center of the neural stimulus-control learning machinery.
Previous behavioral studies have demonstrated that control states, for instance, heightened attentional selectivity, can become directly associated with, and subsequently retrieved by, particular stimuli, thus breaking down the traditional dichotomy between top-down and bottom-up driven behavior. However, the neural mechanisms underlying this type of stimulus-control learning remain poorly understood. We therefore combined noninvasive human neuroimaging with a task that allowed us to dissociate the acquisition of stimulus-control associations from that of stimulus-response associations. The results revealed the caudate nucleus as the key brain structure involved in selectively driving stimulus-control learning. These data represent the first identification of the neural mechanisms of stimulus-specific control associations, and they significantly extend current conceptions of the type of learning processes mediated by the caudate.
认知心理学和神经科学中一个长期存在的二分法将受控的、自上而下驱动的行为与联想的、自下而上驱动的行为对立起来,其中认知控制过程使我们能够克服习得性良好的刺激-反应(S-R)关联。相比之下,一些先前的研究提出了一种有趣的可能性,即联想与受控加工以刺激-控制状态(S-C)关联的形式进行整合,也就是特定刺激与特定控制状态(如高注意力选择性)之间的习得性联系。然而,介导S-C学习的神经机制仍知之甚少。在这里,我们将人类功能磁共振成像(fMRI)与先前开发的斯特鲁普实验方案相结合,该方案使我们能够区分基于S-R学习导致的斯特鲁普干扰减少和基于S-C学习导致的减少。我们使用联想学习模型对受试者S-C和S-R关联的习得进行建模,然后在基于模型的行为和fMRI分析中使用逐次试验的S-C和S-R预测误差(PE)估计。我们发现,分别来自S-C和S-R关联的PE估计解释了S-C和S-R学习条件下行为斯特鲁普干扰效应的减少。此外,基于模型的fMRI分析确定尾状核是参与选择性更新刺激-控制状态关联的关键结构。补充分析还显示,在使用习得的S-R关联与S-C关联来最小化斯特鲁普干扰时,对顶叶皮层的依赖更大。这些结果支持了一种新出现的观点,即通用的控制状态可以与特定的自下而上线索相关联,并且它们将背侧纹状体的尾状核置于神经刺激-控制学习机制的中心。
先前的行为研究表明,控制状态,例如增强的注意力选择性,可以与特定刺激直接关联并随后由其唤起,从而打破了自上而下和自下而上驱动行为之间的传统二分法。然而,这种类型的刺激-控制学习背后的神经机制仍知之甚少。因此,我们将无创人类神经成像与一项任务相结合,该任务使我们能够区分刺激-控制关联的习得与刺激-反应关联的习得。结果表明尾状核是参与选择性驱动刺激-控制学习的关键脑结构。这些数据首次确定了刺激特异性控制关联的神经机制,并且它们显著扩展了当前对由尾状核介导的学习过程类型的认识。