We assess whether a cardinal model can be used to relate neural observables to stochastic choice behavior. We develop a general empirical framework for relating any neural observable to choice prediction and propose a means of benchmarking their predictive power. In a previous study, measurements of neural activity were made while subjects considered consumer goods. Here, we find that neural activity predicts choice behavior with the degree of stochasticity in choice related to the cardinality of the measurement. However, we also find that current methods have a significant degree of measurement error which severely limits their inferential and predictive performance.

中文翻译：

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神经随机效用：将基本神经可观察量与随机选择行为联系起来。

我们评估是否可以使用基数模型将神经可观察量与随机选择行为联系起来。我们开发了一个通用的经验框架，用于将任何可观察到的神经与选择预测相关联，并提出一种对其预测能力进行基准测试的方法。在之前的一项研究中，当受试者考虑消费品时，对神经活动进行了测量。在这里，我们发现神经活动通过与测量基数相关的选择的随机性程度来预测选择行为。然而，我们也发现当前的方法有很大程度的测量误差，这严重限制了它们的推理和预测性能。