GGSDT: A unified signal detection framework for confidence data analysis
GGSDT: A unified signal detection framework for confidence data analysis
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GGSDT：用于置信度数据分析的统一信号检测框架
DOI：
10.1101/2022.10.28.514329
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Nishida Shin’ya
中科院分区：
文献类型：
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作者：
Miyoshi Kiyofumi;Nishida Shin’ya
人类的决策行为需要对其确定性的分级意识，称为信心。到目前为止，相当大的兴趣已经支付给决策和信心的行为和计算的分离，这就提出了一个迫切需要的测量框架，可以量化的信心评级相对于决策准确性（元认知效率）的效率。作为一个独特的除了这样的框架，我们已经开发了一个新的信号检测理论范式，利用广义高斯分布（GGSDT）。该框架通过尺度和形状参数分别评估观测者的内标准差比和元认知效率。形状参数量化了内部分布的峰度，实际上可以理解为高斯理想观察者的置信度被随机猜测（元高斯递减率）破坏的比例。这种解释在很大程度上与决策准确性或操作特性不对称性的污染效应无关。因此，GGSDT能够实现迄今为止未开发的研究方案（例如，是/否与强迫选择元认知效率的直接比较），预计将在行为科学的各个领域中找到应用。本文提供了GGSDT分析的详细演练，并附带了一个R包（ggsdt）。
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 internal standard deviation ratio and metacognitive efficiency through the scale and shape 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 versus forced-choice metacognitive efficiency), expected to find applications in various fields of behavioral science. This paper provides a detailed walkthrough of the GGSDT analysis with an accompanying R package (ggsdt).