Measures of Metacognitive Efficiency Across Cognitive Models of Decision Confidence

Meta-d '/d ' has become the quasi-gold standard to quantify metacognitive efficiency because meta-d '/d ' was developed to control for discrimination performance, discrimination criteria, and confidence criteria even without the assumption of a specific generative model underlying confidence judgments. Using simulations, we demonstrate that meta-d '/d ' is not free from assumptions about confidence models: Only when we simulated data using a generative model of confidence according to which the evidence underlying confidence judgments is sampled independently from the evidence utilized in the choice process from a truncated Gaussian distribution, meta-d '/d ' was unaffected by discrimination performance, discrimination task criteria, and confidence criteria. According to five alternative generative models of confidence, there exist at least some combination of parameters where meta-d '/d ' is affected by discrimination performance, discrimination criteria, and confidence criteria. A simulation using empirically fitted parameter sets showed that the magnitude of the correlation between meta-d '/d ' and discrimination performance, discrimination task criteria, and confidence criteria depends heavily on the generative model and the specific parameter set and varies between negligibly small and very large. These simulations imply that a difference in meta-d '/d ' between conditions does not necessarily reflect a difference in metacognitive efficiency but might as well be caused by a difference in discrimination performance, discrimination task criterion, or confidence criteria.