Value Signals in the Prefrontal Cortex Predict Individual Preferences across Reward Categories
Value Signals in the Prefrontal Cortex Predict Individual Preferences across Reward Categories
复制标题
DOI：
10.1523/jneurosci.5082-13.2014
复制
发表时间：
2014-05-28
影响因子：
5.3
通讯作者：
Goebel, Rainer
中科院分区：
文献类型：
--
作者：
Gross, Jorg;Woelbert, Eva;Goebel, Rainer
人类可以从根本上不同的选项中做出选择，比如看电影或出去吃饭。根据功利主义哲学家提出并在经济学中广泛使用的效用概念，这可以通过将不同选项的价值映射到一个共同的尺度上来实现，而不依赖于具体的选项特征（Fehr和Rangel，2011; Levy和Glimcher，2012）。如果是这样的话，大脑中与价值相关的活动模式应该可以预测不同奖励类别的个人偏好。我们分析了前额叶皮层的功能磁共振成像数据，同时受试者想象他们从属于两种不同奖励类别的项目中获得的快乐：参与活动（如出去喝酒，做白日梦或做运动）和零食。在与一个类别相关的大脑模式上训练的支持向量机可以可靠地预测另一个类别的个人偏好，反之亦然。此外，我们预测参与者的偏好。这些发现表明，前额叶皮层的价值信号遵循一个共同的价值尺度表示，甚至可以在个体之间进行比较，原则上可以用来预测选择。
Humans can choose between fundamentally different options, such as watching a movie or going out for dinner. According to the utility concept, put forward by utilitarian philosophers and widely used in economics, this may be accomplished by mapping the value of different options onto a common scale, independent of specific option characteristics (Fehr and Rangel, 2011; Levy and Glimcher, 2012). If this is the case, value-related activity patterns in the brain should allow predictions of individual preferences across fundamentally different reward categories. We analyze fMRI data of the prefrontal cortex while subjects imagine the pleasure they would derive from items belonging to two distinct reward categories: engaging activities (like going out for drinks, daydreaming, or doing sports) and snack foods. Support vector machines trained on brain patterns related to one category reliably predict individual preferences of the other category and vice versa. Further, we predict preferences across participants. These findings demonstrate that prefrontal cortex value signals follow a common scale representation of value that is even comparable across individuals and could, in principle, be used to predict choice.