Quantifying metacognition with the hmetad package in R
Abstract
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.
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BibTeX
@misc{oneill2026quantifying,
title = {{Quantifying metacognition with the hmetad package in R}},
author = {Kevin O'Neill and Stephen Fleming},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/e3qhz_v1},
url = {https://osf.io/preprints/psyarxiv/e3qhz_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Quantifying metacognition with the hmetad package in R
AU - Kevin O'Neill
AU - Stephen Fleming
PY - 2026
DA - 2026/10/27
PB - PsyArXiv
DO - 10.31234/osf.io/e3qhz_v1
UR - https://osf.io/preprints/psyarxiv/e3qhz_v1/
AB - 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.
ER -