pyWitness 1.0: A python eyewitness identification analysis toolkit

• Published in: Behavior research methods Vol. 56; no. 3; pp. 1533 - 1550
• Main Authors: Mickes, Laura, Seale-Carlisle, Travis M., Chen, Xueqing, Boogert, Stewart
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2024; Springer Nature B.V
• Subjects: Algorithms; Behavioral Science and Psychology; Cognitive Psychology; Data Analysis; Data processing; Humans; Mathematical models; Mental Processes; Psychology; Recognition, Psychology; ROC Curve; Signal detection theory; Detection-plus-localization; Recognition memory; Receiver operating characteristic; Memory; Visual search task; Eyewitness; Confidence accuracy characteristic
• ISSN: 1554-3528; 1554-351X; 1554-3528
• DOI: 10.3758/s13428-023-02108-2

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.