Simulating future value in intertemporal choice.
Simulating future value in intertemporal choice.
复制标题
模拟跨期选择中的未来价值。
DOI：
10.1038/srep43119
复制
发表时间：
2017
影响因子：
4.6
通讯作者：
Montague,PRead
中科院分区：
文献类型：
--
作者：
Solway,Alec;Lohrenz,Terry;Montague,PRead
对人类和其他动物如何权衡价值和时间的实验室研究有着悠久而传奇的历史，也是大量文献的主题。然而，尽管有很长的研究历史，但对于跨期选择偏好是如何产生的，并没有达成一致的机械论解释。最近，一些理论家提出了基于模型的强化学习作为候选框架。该框架描述了一套算法，通过该算法，以状态转移函数和奖励函数的形式的环境模型可以在线转换为决策。状态转移函数允许基于模型的系统根据预测的未来状态做出决策，而奖励函数为每个状态赋值，共同捕获成功进行跨期选择所需的组件。实证研究也指出了预期增加和贴现减少之间可能存在的关系。在当前的论文中，我们在一个大型的新数据集中(n = 168)寻找时间折扣和基于模型的控制之间关系的直接证据。然而，在几种不同的建模公式下测试这种关系时，没有迹象表明这两个量是相关的。
The laboratory study of how humans and other animals trade-off value and time has a long and storied history, and is the subject of a vast literature. However, despite a long history of study, there is no agreed upon mechanistic explanation of how intertemporal choice preferences arise. Several theorists have recently proposed model-based reinforcement learning as a candidate framework. This framework describes a suite of algorithms by which a model of the environment, in the form of a state transition function and reward function, can be converted on-line into a decision. The state transition function allows the model-based system to make decisions based on projected future states, while the reward function assigns value to each state, together capturing the necessary components for successful intertemporal choice. Empirical work has also pointed to a possible relationship between increased prospection and reduced discounting. In the current paper, we look for direct evidence of a relationship between temporal discounting and model-based control in a large new data set (n = 168). However, testing the relationship under several different modeling formulations revealed no indication that the two quantities are related.