Human decision behavior entails a graded awareness of its certainty, known as a feeling of confidence. Until now, considerable interest has been paid to behavioral and computational dissociations of decision and confidence, which has raised an urgent need for measurement frameworks that can quantify the efficiency of confidence rating relative to decision accuracy (metacognitive efficiency). As a unique addition to such frameworks, we have developed a new signal detection theory paradigm utilizing the generalized Gaussian distribution (GGSDT). This framework evaluates the observer's metacognitive efficiency and internal standard deviation ratio through shape and scale parameters, respectively. The shape parameter quantifies the kurtosis of internal distributions and can practically be understood in reference to the proportion of the Gaussian ideal observer's confidence being disrupted with random guessing (metacognitive lapse rate). This interpretation holds largely irrespective of the contaminating effects of decision accuracy or operating characteristic asymmetry. Thus, the GGSDT enables hitherto unexplored research protocols (e.g., direct comparison of yes/no vs. forced-choice metacognitive efficiency), expected to find applications in various fields of behavioral science. This article provides a detailed walkthrough of the GGSDT analysis with an accompanying R package (ggsdt). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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广义高斯信号检测理论：用于置信数据分析的统一信号检测框架。


人类的决策行为需要对其确定性进行分级认识，称为自信感。到目前为止，人们对决策和信心的行为和计算分离产生了相当大的兴趣，这迫切需要能够量化相对于决策准确性（元认知效率）的信心评级效率的测量框架。作为此类框架的独特补充，我们利用广义高斯分布（GGSDT）开发了一种新的信号检测理论范式。该框架分别通过形状和尺度参数评估观察者的元认知效率和内标准偏差比。形状参数量化了内部分布的峰度，并且实际上可以参考高斯理想观察者的置信度被随机猜测（元认知衰退率）破坏的比例来理解。无论决策准确性或操作特性不对称性的污染影响如何，这种解释在很大程度上都是成立的。因此，GGSDT 实现了迄今为止尚未探索的研究方案（例如，是/否与强制选择元认知效率的直接比较），有望在行为科学的各个领域找到应用。本文提供了 GGSDT 分析的详细演练以及随附的 R 包 (ggsdt)。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。