School of Psychological Science, University of Bristol, Bristol, UK.
School of Psychology, King's College, University of Aberdeen, Aberdeen, UK.
Behav Res Methods. 2024 Mar;56(3):1533-1550. doi: 10.3758/s13428-023-02108-2. Epub 2023 Jul 19.
pyWitness is a python toolkit for recognition memory experiments, with a focus on eyewitness identification (ID) data analysis and model fitting. The current practice is for researchers to use different statistical packages to analyze a single dataset. pyWitness streamlines the process. In addition to conducting key data analyses (e.g., receiver operating characteristic analysis, confidence accuracy characteristic analysis), statistical comparisons, signal-detection-based model fits, simulated data generation, and power analyses are also possible. We describe the package implementation and provide detailed instructions and tutorials with datasets so that users can follow. There is also an online manual that is regularly updated. We developed pyWitness to be user-friendly, reduce human interaction with pre-processing and processing of data and model fits, and produce publication-ready plots. All pyWitness features align with open science practices, such that the algorithms, fits, and methods are reproducible and documented. While pyWitness is a python toolkit, it can also be used from R for users more accustomed to this environment.
pyWitness 是一个用于识别记忆实验的 Python 工具包，专注于目击者识别 (ID) 数据分析和模型拟合。目前的做法是让研究人员使用不同的统计软件包来分析单个数据集。pyWitness 简化了这一过程。除了进行关键数据分析（例如，接收者操作特征分析、信心准确性特征分析）、统计比较、基于信号检测的模型拟合、模拟数据生成和功效分析外，还可以进行统计比较、基于信号检测的模型拟合、模拟数据生成和功效分析。我们描述了该软件包的实现，并提供了带有数据集的详细说明和教程，以便用户遵循。还有一个在线手册，定期更新。我们开发了 pyWitness，使其易于使用，减少了对数据和模型拟合的预处理和处理过程中的人为交互，并生成了可发布的图形。pyWitness 的所有功能都符合开放科学实践，例如算法、拟合和方法是可重复的和有记录的。虽然 pyWitness 是一个 Python 工具包，但对于更习惯这种环境的用户，也可以从 R 中使用它。
