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. 2021 Jun 11;11:12364. doi: 10.1038/s41598-021-90990-1

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Bayesian multivariate regression. (a) Graphical illustration of the model. Both response times and memory accuracy were modelled to be drawn from a multivariate Gaussian distribution. Parameters of the two categorical predictors (Stimuli, Condition) and their interaction term were drawn from a noninformative Student t prior. Their slopes were allowed to vary across the group j (participants, n = 24), capturing the participant-level variability. (b) Posterior predictive densities of accuracy (top) and reaction times (bottom) depicting a good model fit. Dark lines represent the observed data distribution (y) of the two response variables, while the green lines represent the replicated (simulated) draws (y_rep) from the model's posterior predictive distribution.