Department of Neuroscience, Department of Economics, and Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63110, USA.
Neuron. 2022 May 18;110(10):1615-1630. doi: 10.1016/j.neuron.2022.03.002. Epub 2022 Mar 24.
Logistic regressions were developed in economics to model individual choice behavior. In recent years, they have become an important tool in decision neuroscience. Here, I describe and discuss different logistic models, emphasizing the underlying assumptions and possible interpretations. Logistic models may be used to quantify a variety of behavioral traits, including the relative subjective value of different goods, the choice accuracy, risk attitudes, and choice biases. More complex logistic models can be used for choices between good bundles, in cases of nonlinear value functions, and for choices between multiple options. Finally, logistic models can quantify the explanatory power of neuronal activity on choices, thus providing a valid alternative to receiver operating characteristic (ROC) analyses.
逻辑回归最初是在经济学中用于建模个体选择行为的。近年来，它们已成为决策神经科学中的重要工具。在这里，我将描述和讨论不同的逻辑模型，强调其基本假设和可能的解释。逻辑模型可用于量化各种行为特征，包括不同商品的相对主观价值、选择准确性、风险态度和选择偏差。更复杂的逻辑模型可用于商品束之间的选择、非线性价值函数的情况下，以及多种选择之间的选择。最后，逻辑模型可以量化神经元活动对选择的解释能力，从而为接收者操作特征 (ROC) 分析提供有效替代。
