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归一化除法模型的选择理论基础。

Choice-theoretic foundations of the divisive normalization model.

作者信息

Steverson Kai, Brandenburger Adam, Glimcher Paul

机构信息

Department of Neuroscience, New York University, New York 10003, USA.

Stern School of Business, Tandon School of Engineering, NYU Shanghai, New York University, New York, NY 10012, USA.

出版信息

J Econ Behav Organ. 2019 Aug;164:148-165. doi: 10.1016/j.jebo.2019.05.026. Epub 2019 Jun 6.

Abstract

Recent advances in neuroscience suggest that a utility-like calculation is involved in how the brain makes choices, and that this calculation may use a computation known as divisive normalization. While this tells us the brain makes choices, it is not immediately evident the brain uses this computation or exactly behavior is consistent with it. In this paper, we address both of these questions by proving a three-way equivalence theorem between the normalization model, an information-processing model, and an axiomatic characterization. The information-processing model views behavior as optimally balancing the expected value of the chosen object against the entropic cost of reducing stochasticity in choice. This provides an optimality rationale for the brain may have evolved to use normalization-type models. The axiomatic characterization gives a set of testable behavioral statements equivalent to the normalization model. This answers behavior arises from normalization. Our equivalence result unifies these three models into a single theory that answers the "how", "why", and "what" of choice behavior.

摘要

神经科学的最新进展表明,大脑在做出选择时涉及一种类似效用的计算,并且这种计算可能使用一种称为归一化除法的运算。虽然这告诉我们大脑会做出选择,但大脑是否使用这种运算,或者行为究竟如何与之相符,却并非一目了然。在本文中,我们通过证明归一化模型、信息处理模型和公理表征之间的三方等价定理来解决这两个问题。信息处理模型将行为视为在所选对象的预期值与降低选择随机性的熵成本之间进行最优平衡。这为大脑可能进化到使用归一化类型模型提供了一个最优性原理。公理表征给出了一组与归一化模型等价的可测试行为陈述。这回答了行为如何源于归一化的问题。我们的等价结果将这三个模型统一为一个单一理论,该理论回答了选择行为的“如何”“为何”以及“是什么”。

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