发表机构
University of Florida; Brown University; University of Missouri（佛罗里达大学; 布朗大学; 密苏里大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
metasignal是实现17种元认知测量及相关指标的Python包，支持二元辨别任务，提供多种分析工具，旨在推动元认知与决策研究的应用。
AI 中文摘要
Metasignal是一个用于信号检测理论（SDT）和元认知测量的开源Python包，它实现了Rahnev（2025）评估的17种元认知测量指标，以及参考变量d'（知觉敏感性）、反应准则c（反应偏差）和平均置信度。这17种指标包括3个meta-d'族估计值：meta-d'、M-ratio和M-difference；4种非参数Type-2指标：Type-2受试者工作特征曲线下面积（AUC2）、Gamma、Phi和delta置信度，以及它们的8种SDT标准化比率和差值形式；还有2种基于模型的指标：meta-noise和meta-uncertainty。只需一个函数即可从试次级别的刺激、反应和置信度数组中计算出完整的指标集。metasignal目前支持二元（二择一）辨别任务，其中每个试次的刺激和反应都用恰好两个类别编码。该包还提供命令行界面、组汇总、自助法置信区间、置换检验、可选的分层贝叶斯模型以及信息论指标。metasignal将这些指标统一在单一平台中，以促进元认知研究及其在决策研究中的更广泛应用。
英文摘要
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.
