Aujla Harinder
Department of Psychology, University of Winnipeg, R3B 2E9, Winnipeg, Canada.
Behav Res Methods. 2025 Aug 13;57(9):258. doi: 10.3758/s13428-025-02768-2.
Signal detection theory (SDT) was developed to provide independent measures of sensitivity and bias for an observer asked to discriminate a signal stimulus against background noise. The sensitivity measure, , achieves this goal when the underlying decision space consists of two Gaussian distributions of equal variance. However, fails to provide a stable measure of sensitivity when the distributions are of unequal variance. In addition, unequal base rates of stimulus presentations or asymmetries in the payoff matrix for decision outcomes further shift criterion placement and estimations of sensitivity. Alternative sensitivity metrics that attempt to consider these scenarios either require information across multiple confidence levels or make implicit assumptions about the underlying decision space a priori. I propose an optimization approach that accurately estimates information about the underlying decision space without requiring information over multiple confidence levels. The proposed approach requires and a single false-alarm and hit rate pair. The reliance on limits the proposed method to providing a normative model of performance where the researcher is operating under a theoretical framework of decision. Simulations illustrate that in cases where is known, or can be reasonably estimated, the optimization approach is successful in recovering the critical characteristics of the decision space.
信号检测理论（SDT）的发展是为了在要求观察者区分信号刺激与背景噪声时，提供对灵敏度和偏差的独立测量。当潜在的决策空间由两个方差相等的高斯分布组成时，灵敏度度量 实现了这一目标。然而，当分布的方差不相等时， 无法提供稳定的灵敏度测量。此外，刺激呈现的基础概率不相等或决策结果的收益矩阵中的不对称性会进一步改变标准位置和灵敏度估计。试图考虑这些情况的替代灵敏度指标要么需要跨多个置信水平的信息，要么对潜在的决策空间进行先验的隐含假设。我提出了一种优化方法，该方法无需跨多个置信水平的信息就能准确估计有关潜在决策空间的信息。所提出的方法需要 以及单个误报率和命中率对。对 的依赖将所提出的方法限制为提供一种性能规范模型，其中研究人员在决策的理论框架下进行操作。模拟表明，在已知 或可以合理估计的情况下，优化方法成功地恢复了决策空间的关键特征。
