pyWitness 1.0: A python eyewitness identification analysis toolkit.
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| Název: | pyWitness 1.0: A python eyewitness identification analysis toolkit. |
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| Autoři: | Mickes L; School of Psychological Science, University of Bristol, Bristol, UK. laura.mickes@bristol.ac.uk., Seale-Carlisle TM; School of Psychology, King's College, University of Aberdeen, Aberdeen, UK., Chen X; School of Psychological Science, University of Bristol, Bristol, UK., Boogert S; Department of Physics and Astronomy, University of Manchester, Manchester, UK. |
| Zdroj: | Behavior research methods [Behav Res Methods] 2024 Mar; Vol. 56 (3), pp. 1533-1550. Date of Electronic Publication: 2023 Jul 19. |
| Způsob vydávání: | Journal Article |
| Jazyk: | English |
| Informace o časopise: | Publisher: Springer Country of Publication: United States NLM ID: 101244316 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1554-3528 (Electronic) Linking ISSN: 1554351X NLM ISO Abbreviation: Behav Res Methods Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2010- : New York : Springer Original Publication: Austin, Tex. : Psychonomic Society, c2005- |
| Výrazy ze slovníku MeSH: | Recognition, Psychology* , Mental Processes*, Humans ; ROC Curve ; Algorithms ; Data Analysis |
| Abstrakt: | 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. (© 2023. The Author(s).) |
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| Contributed Indexing: | Keywords: Confidence accuracy characteristic; Detection-plus-localization; Eyewitness; Memory; Receiver operating characteristic; Recognition memory; Signal detection theory; Visual search task |
| Entry Date(s): | Date Created: 20230804 Date Completed: 20240405 Latest Revision: 20240906 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC10991016 |
| DOI: | 10.3758/s13428-023-02108-2 |
| PMID: | 37540469 |
| Databáze: | MEDLINE |
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