John E. Marsh, Mark J. Hurlstone, Alexandre Marois, Linden J. Ball, Stuart B. Moore, François Vachon, Sabine J. Schlittmeier, Jan Philipp Röer, Axel Buchner, Frederik Aust, & Raoul Bell
This repository contains research products associated with the publication.
The data_raw directory contains the data in CSV format; RData-files
can be found in data_processed/.
The analyses herein were performed with the R package
targets
and can be rerun by executing the file _make.sh. Note that rerunning
the analyses may take several days (see below). The analyses are run
separately for each study and the results are stored in the directories
mochasta1/_targets/ and mochasta2/_targets/. To run the analysis for
Experiment 1 set the project variable in _make.sh to mochasta1
(and analogously for Experiment 2).
The Quarto files in the directories mochasta1 and mochasta2 create
reports of the results of the analyses reported in the paper.
The Bayesian meta-analysis reported in the paper was performed by hand using the online Bayes factor calculator by Zoltan Dienes; hence, no code is provided for this analysis.
@Article{marshhurlstone:2024,
title = {Changing-state irrelevant speech disrupts visual–verbal but not visual–spatial serial recall.},
author = {Marsh, John E. and Hurlstone, Mark J. and Marois, Alexandre and Ball, Linden J. and Moore, Stuart B. and Vachon, Fran\c{c}ois and Schlittmeier, Sabine J. and R\"{o}er, Jan Philipp and Buchner, Axel and Aust, Frederik and Bell, Raoul},
year = {2024},
journal = {Journal of Experimental Psychology: Learning, Memory, and Cognition},
volume = {50},
number = {11},
pages = {1772–1790},
doi = {10.1037/xlm0001360}
}
The version of R and all packages required to reproduce the anlysis are
listed in the DESCRIPTION file. Additonally, it is required to install
the mochasta package from this GitHub repository.
# Install the mochasta package from GitHub
remotes::install_github("crsh/mochasta")
# Install from a local copy of the repository
devtools::install("path/to/mochasta")Running all analyses may take severa on a Desktop computer even when
Bayesian models are fit in parallel on 7 cores (current setting). To
control the number of cores used for parallel processing, change the
targets controller option in targets.r in the folders for each
experiment.
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