metasignal: A Python Package for Comprehensive Metacognitive Analysis and Decision-Making

2026-07-31

AI Summary

This study addresses the lack of a unified, open-source analytical framework in metacognition research, which has hindered methodological standardization and reproducibility. To bridge this gap, the authors introduce an open-source Python toolkit that, for the first time, integrates 17 distinct metacognitive metrics—spanning parametric, non-parametric, and model-based approaches—within a signal detection theory framework. The toolkit enables automatic computation of perceptual sensitivity, response bias, confidence, and multiple metacognitive indices directly from single-trial data, while also offering bootstrap confidence intervals, permutation tests, hierarchical Bayesian modeling, and information-theoretic analyses. Designed with both command-line accessibility and built-in visualization capabilities, the package substantially lowers the barrier to entry, thereby promoting standardized and widespread application of metacognitive measurement across cognitive science and decision-making research.

Abstract

Metasignal is an open-source Python package for signal detection theory (SDT) and metacognitive measurement. It implements the 17 metacognitive measures evaluated by Rahnev (2025), together with the reference variables d' (perceptual sensitivity), response criterion c (response bias), and mean confidence. The 17 measures comprise three meta-d' family estimates, meta-d', M-ratio, and M-difference; four nonparametric Type-2 measures, the Type-2 area under the receiver-operating-characteristic curve (AUC2), Gamma, Phi, and delta confidence, together with their eight SDT-normalized ratio and difference forms; and two model-based measures, meta-noise and meta-uncertainty. A single function computes the complete set from trial-level stimulus, response, and confidence arrays. `metasignal` currently supports binary (two-alternative) discrimination tasks, in which each trial's stimulus and response are coded with exactly two categories. The package also provides a command-line interface, group summaries, bootstrap confidence intervals, permutation tests, optional hierarchical Bayesian models, and information-theoretic measures. `metasignal` unifies these measures in a single platform to encourage broader metacognition research and adoption in decision-making studies.

Problem

• metacognition
• signal detection theory
• meta-d'
• Type-2 AUC
• confidence

Innovation

• metacognition
• signal detection theory
• meta-d'
• Type-2 ROC
• Python package