Quantifying metacognition with the hmetad package in R

A longstanding goal in metacognition research is the quantification of confidence-related parameters independently from task performance. While there is now a proliferation of generative models of confidence in numerous psychological tasks, the meta-d' model remains a popular approach to estimating metacognitive sensitivity and metacognitive bias within a signal detection theoretic framework. However, as questions in metacognition research increase in complexity, it remains an ongoing challenge to develop extensions of the meta-d' model built to deal with longitudinal, repeated-measures, or other complex experimental designs. Here we introduce the open-source R package hmetad, which allows users to fit the meta-d' model for a wide range of experimental designs using a familiar formula syntax. We also review how the meta-d' model can be validated in a Bayesian workflow which investigates posterior estimates of key parameters. Overall, the hmetad package offers a comprehensive interface to fitting, evaluating, and visualizing signal detection theoretic models of metacognition.