The gaussian signal detection framework has served as the foundation for several theories of visual confidence. Because of how prevalent the framework is, the peculiar repercussions of this distributional supposition have gone unnoticed. To test the accuracy of metacognitive judgment, researchers presented systematic comparisons of the gaussian signal detection framework with its logistic equivalent. 

These frameworks were discovered to offer distinct viewpoints on the effectiveness of confidence rating about objective judgment due to differences in their distribution kurtosis (the logistic model naturally provides a higher meta-d/d ratio than the gaussian model). These theories might likewise offer conflicting answers regarding the metacognitive inefficiency along the internal evidence continuum (whether meta-d is bigger or lower for increasing degrees of confidence). Given that the gaussian and logistic metacognitive models got almost comparable support in the quantitative model comparisons, previous ideas based on these lines of study may need to be re-examined. 

Despite the differences, they discovered that comparisons of metacognitive measures across conditions or participants are often resilient against distributional assumptions, which gives standard research practice a great deal of security. They thought that by raising awareness of the importance of hidden modeling assumptions, the essay could help the field in general advance.