一站式工具，精准量化人类决策中的元认知能力
metasignal: A Python Package for Comprehensive Metacognitive Analysis and Decision-Making
- 集成17种元认知测量方法，统一计算信号检测理论指标
- 支持二选一任务，从原始数据直接输出完整元认知分析结果
- 适合心理学、神经科学与行为决策研究者快速开展元认知分析
Metasignal 是一个开源 Python 工具包，用于信号检测理论（SDT）和元认知测量。它实现了 Rahnev (2025) 评估的 17 种元认知指标，包括感知敏感度 d'、反应标准 c 与平均信心等参考变量。这 17 项指标涵盖三类：元 d' 家族估计（meta-d'、M-ratio、M-difference）；四种非参数型 2 型指标（Type-2 AUC2、Gamma、Phi、delta confidence）及其八种标准化比值与差值形式；以及两种模型基础指标（meta-noise、meta-uncertainty）。单一函数即可从试验级刺激、反应与信心数组中计算全部指标。当前支持二择一辨别任务，每轮刺激与反应仅含两类。工具还提供命令行接口、组汇总统计、自助法置信区间、置换检验、可选分层贝叶斯模型及信息论度量。Metasignal 将这些方法整合于单一平台，推动元认知研究在决策科学中的广泛应用。
原文摘要 · Abstract (English)
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.
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