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Abstract：
In many decision tasks, we have a set of alternative choices and are faced with the problem of how to use our latent beliefs and preferences about each alternative to make a single choice. Cognitive and decision models typically presume that beliefs and preferences are distilled to a scalar latent strength for each alternative, but it is also critical to model how people use these latent strengths to choose a single alternative. Most models follow one of two traditions to establish this link. Modern psychophysics and memory researchers make use of signal detection theory, assuming that latent strengths are perturbed by noise, and the highest resulting signal is selected. By contrast, many modern decision theoretic modeling and machine learning approaches use the softmax function (which is based on Luce\'s choice axiom; Luce, 1959) to give some weight to non-maximal-strength alternatives. Despite the prominence of these two theories of choice, current approaches rarely address the connection between them, and the choice of one or the other appears more motivated by the tradition in the relevant literature than by theoretical or empirical reasons to prefer one theory to the other. The goal of the current work is to revisit this topic by elucidating which of these two models provides a better characterization of latent processes in m -alternative decision tasks, with a particular focus on memory tasks. In a set of visual memory experiments, we show that, within the same experimental design, the softmax parameter β varies across m -alternatives, whereas the parameter d \' of the signal-detection model is stable. Together, our findings indicate that replacing softmax with signal-detection link models would yield more generalizable predictions across changes in task structure. More ambitiously, the invariance of signal detection model parameters across different tasks suggests that the parametric assumptions of these models may be more than just a mathematical convenience, but reflect something real about human decision-making.
摘要：
在许多决策任务中，我们有一系列的替代选择，我们面临的问题是如何利用我们潜在的信念和偏好对每一个选择做出单一的选择。认知和决策模型通常假定信念和偏好被提炼成每种选择的标量潜在强度，但对人们如何利用这些潜在的优势来选择单一的替代方案进行建模也是至关重要的。大多数模型遵循两个传统之一来建立这种联系。现代心理物理学和记忆研究者利用信号检测理论,假设潜在的强度受到噪音的干扰，并选择最高的结果信号。相比之下,许多现代决策理论建模和机器学习方法都使用softmax函数(该函数基于Luce的选择公理；Luce，1959)给予非最大强度替代品一定的权重。尽管这两种选择理论很突出，目前的方法很少解决它们之间的联系，和选择一个或另一个似乎更多的动机在传统的相关文献比理论或经验的原因更喜欢一个理论。当前工作的目标是通过阐明这两个模型中的哪一个可以更好地表征m-alternative决策任务中的潜在过程来重新审视这一主题。特别关注内存任务。在一组视觉记忆实验中，我们证明，在相同的实验设计中，softmax参数β在m个备选方案中变化，而信号检测模型的参数d'是稳定的。一起,我们的发现表明，用信号检测链路模型代替softmax将在任务结构的变化中产生更普遍的预测。更有野心,信号检测模型参数在不同任务之间的不变性表明，这些模型的参数假设可能不仅仅是数学上的便利，但反映了人类决策的一些真实的东西。
