James Grange j.a.grange@keele.ac.uk
mixtur: An R package for designing, analysing, and modelling continuous report visual short-term memory studies
Grange
Authors
Abstract
Visual short-term memory (vSTM) is often measured via continuous-report tasks whereby participants are presented with stimuli that vary along a continuous dimension (e.g., colour) with the goal of memorising the stimulus features. At test, participants are probed to recall the feature value of one of the memoranda in a continuous manner (e.g., by clicking on a colour wheel). The angular deviation between the participant response and the true feature value provides an estimate of recall precision. Two prominent models of performance on such tasks are the two- and three-component mixture models (Bays et al., 2009; Zhang & Luck, 2008). Both models decompose participant responses into probabilistic mixtures of: (1) responses to the true target value based on a noisy memory representation; (2) random guessing when memory fails. In addition, the three-component model proposes (3) responses to a non-target feature value (i.e., binding errors). Here we report the development of mixtur, an open-source package written for the statistical programming language R that facilitates the fitting of the 2- and 3-component mixture models to continuous report data. We also conduct simulations to develop recommendations for researchers on trial numbers, set-sizes and memoranda similarity, as well as parameter recovery and model recovery. In the Discussion, we discuss how mixtur can be used to fit the slots and the slots-plus-averaging models, as well as how mixtur can be extended to fit explanatory models of visual short-term memory. It is our hope that mixtur will lower the barrier of entry for utilising mixture modelling.
Citation
Grange. (2022). mixtur: An R package for designing, analysing, and modelling continuous report visual short-term memory studies. Behavior Research Methods, https://doi.org/10.3758/s13428-021-01688-1
Acceptance Date | Aug 9, 2021 |
---|---|
Publication Date | Jan 31, 2022 |
Journal | Behavior Research Methods |
Print ISSN | 1554-351X |
Publisher | Springer Verlag |
DOI | https://doi.org/10.3758/s13428-021-01688-1 |
Keywords | Visual short-term memory; Mixture modelling; R; Simulation |
Publisher URL | https://link.springer.com/article/10.3758/s13428-021-01688-1 |
Files
Grange-Moore2022_Article_MixturAnRPackageForDesigningAn.pdf
(56.8 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
Grange and Moore (mixtur).pdf
(2.3 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
You might also like
UKRN Open Research Training Resources and Priorities Working Paper
(2023)
Working Paper
Rumination and inhibition in task switching: No evidence for an association
(2023)
Journal Article
Improving psychological science: further thoughts, reflections and ways forward
(2022)
Journal Article
A spurious correlation between difference scores in evidence-accumulation model parameters
(2022)
Journal Article
Downloadable Citations
About Keele Repository
Administrator e-mail: research.openaccess@keele.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search