Decisions are based on the subjective values of choice options. However, subjective value is a theoretical construct and not directly observable. Strikingly, distinct theoretical models competing to explain how subjective values are assigned to choice options often make very similar behavioral predictions, which poses a major difficulty for establishing a mechanistic, biologically plausible explanation of decision-making based on behavior alone. Here, we demonstrate that model comparison at the neural level provides insights into model implementation during subjective value computation even though the distinct models parametrically identify common brain regions as computing subjective value. We show that frontal cortical regions implement a model based on the statistical distributions of available rewards, whereas intraparietal cortex and striatum compute subjective value signals according to a model based on distortions in the representations of probabilities. Thus, better mechanistic understanding of how cognitive processes are implemented arises from model comparisons at the neural level, over and above the traditional approach of comparing models at the behavioral level alone.

决策基于选择选项的主观价值。然而，主观价值是一种理论结构，不能直接观察到。引人注目的是，不同的理论模型竞相解释如何将主观价值分配给选择选项，通常会做出非常相似的行为预测，这对仅基于行为建立一个机械的、生物学上合理的决策解释构成了重大困难。在这里，我们证明了神经层面的模型比较提供了对主观价值计算过程中模型实施的洞察，即使不同的模型参数化地将共同的大脑区域识别为计算主观价值。我们展示了额叶皮层区域基于可用奖励的统计分布实现了一个模型，而顶叶皮层和纹状体根据基于概率表示的失真的模型计算主观价值信号。因此，对认知过程如何实现的更好的机械理解来自于神经层面的模型比较，超越了仅在行为层面比较模型的传统方法。