Solway Alec, Lohrenz Terry, Montague P Read
Virginia Tech Carilion Research Institute, Roanoke, VA, United States.
Department of Physics, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States.
Front Neurosci. 2019 Sep 6;13:915. doi: 10.3389/fnins.2019.00915. eCollection 2019.
Reward-based decision making is thought to be driven by at least two different types of decision systems: a simple stimulus-response cache-based system which embodies the common-sense notion of "habit," for which model-free reinforcement learning serves as a computational substrate, and a more deliberate, prospective, model-based planning system. Previous work has shown that loss aversion, a well-studied measure of how much more on average individuals weigh losses relative to gains during decision making, is reduced when participants take all possible decisions and outcomes into account including future ones, relative to when they myopically focus on the current decision. Model-based control offers a putative mechanism for implementing such foresight. Using a well-powered data set ( = 117) in which participants completed two different tasks designed to measure each of the two quantities of interest, and four models of choice data for these tasks, we found consistent evidence of a relationship between loss aversion and model-based control but in the direction opposite to that expected based on previous work: loss aversion had a positive relationship with model-based control. We did not find evidence for a relationship between either decision system and risk aversion, a related aspect of subjective utility.
一种是简单的基于刺激-反应缓存的系统，它体现了“习惯”的常识概念，无模型强化学习作为其计算基础；另一种是更审慎、前瞻性的基于模型的规划系统。先前的研究表明，损失厌恶是一种经过充分研究的指标，用于衡量个体在决策过程中平均而言对损失的重视程度比收益高多少。当参与者考虑所有可能的决策和结果（包括未来的决策和结果）时，相对于他们短视地关注当前决策时，损失厌恶程度会降低。基于模型的控制提供了一种实施这种远见的假定机制。我们使用一个样本量充足的数据集（N = 117），其中参与者完成了两项不同的任务，旨在测量两个感兴趣的量中的每一个，以及针对这些任务的四种选择数据模型，我们发现了损失厌恶与基于模型的控制之间存在关系的一致证据，但方向与先前研究预期的相反：损失厌恶与基于模型的控制呈正相关。我们没有发现任何一个决策系统与风险厌恶（主观效用的一个相关方面）之间存在关系的证据。
