Model-based fMRI and its application to reward learning and decision making
Model-based fMRI and its application to reward learning and decision making
复制标题
DOI：
10.1196/annals.1390.022
复制
发表时间：
2007-01-01
期刊：
影响因子：
--
通讯作者：
Kim, Hackjin
中科院分区：
文献类型：
--
作者：
O'Doherty, John P.;Hampton, Alan;Kim, Hackjin
在基于模型的功能磁共振成像 (fMRI) 中，从特定认知过程的计算模型得出的信号与执行相关任务的受试者的 fMRI 数据相关，以确定显示与该模型一致的反应概况的大脑区域。与更传统的神经成像方法相比，该技术的一个关键优势是基于模型的功能磁共振成像可以深入了解特定大脑区域如何实施特定的认知过程，而不是仅仅识别特定过程的位置。本文将简要总结基于模型的功能磁共振成像方法，参考奖励学习和决策领域，其中计算模型被用来探索奖励关联学习的神经机制，修改动作选择以获得奖励，以及编码反映决策问题抽象结构的期望值信号。最后，将讨论这种方法的一些局限性。
In model-based functional magnetic resonance imaging (fMRI), signals derived from a computational model for a specific cognitive process are correlated against fMRI data from subjects performing a relevant task to determine brain regions showing a response profile consistent with that model. A key advantage of this technique over more conventional neuroimaging approaches is that model-based fMRI can provide insights into how a particular cognitive process is implemented in a specific brain area as opposed to merely identifying where a particular process is located. This review will briefly summarize the approach of model-based fMRI, with reference to the field of reward learning and decision making, where computational models have been used to probe the neural mechanisms underlying learning of reward associations, modifying action choice to obtain reward, as well as in encoding expected value signals that reflect the abstract structure of a decision problem. Finally, some of the limitations of this approach will be discussed.