Code and data for the main results of AVWM Study

This dataset contains the data and code to analyze
the behavioral results of "Uluc I, Turpin T, Kotlarz P, Lankinen K, Mamashli F,
Ahveninen J. Comparing auditory and visual aspects of multisensory working
memory using bimodally matched feature patterns."

Unpack the zipped folders at a desirable location.
The scripts are designed to be run from their own directories, including:
~share_data_code_Uluc_2024/code_for_sharing/experiment1/
~share_data_code_Uluc_2024/code_for_sharing/experiment2/
~share_data_code_Uluc_2024/code_for_sharing/experiment3/

Each folder contains their respective analyze_data*.m script.
Please ensure the current working directory matches each script's location.

The anonymized Presentation log files are intended to be in the same path
at

~share_data_code_Uluc_2024/data_for_sharing_noSID/experiment*/
ex

The scripts depend on freely available functions, which can be downloaded
from Matlab Central File Exchange or github:

1) Tobias Otto (2024). Import of log files created by Presentation
(https://www.mathworks.com/matlabcentral/fileexchange/28793-import-of-log-files-created-by-presentation),
MATLAB Central File Exchange.

2) Karin (2024). dprime_simple.m
(https://www.mathworks.com/matlabcentral/fileexchange/47711-dprime_simple-m),
MATLAB Central File Exchange. Retrieved December 14, 2024.

3) E Zakreski (2024). bonferroni_holm (https://www.mathworks.com/matlabcentral/fileexchange/69817-bonferroni_holm),
MATLAB Central File Exchange. Retrieved December 14, 2024.

4) Bechtold, Bastian, 2016. Violin Plots for Matlab, Github Project
https://github.com/bastibe/Violinplot-Matlab, DOI: 10.5281/zenodo.4559847

These functions need to be downloaded from their respective sites and placed to:

~code_for_sharing/install_dependencies_here/

The scripts also require Matlab Statistics and Machine Learning toolbox,
for running the repeated measures ANOVAs using fitrm.m and ranova.m

If this toolbox not available, an alternative way for providing the ANOVAs
using the following functions written by Antonio Trujillo-Ortiz, which are available
at Matlab Central File Exchange, is provided also.
The scripts will test the availability of Statistics and Machine Learning toolbox
automatically and run the analysis using an alternative way.
However, in this case, extracting the output of ANOVA (p-values, F-values, partial eta squared)
will require some customization: the Trujillo-Ortiz functions' output is
printed to the Command Window, as an ANOVA result table with rounded values
only. The output of exact p-values is needed for post hoc correction using the
Bonferroni-Holm correction. An approximation can be made by copying all values
from the output tables to a vector (in each experiment) and using that as an
input for the bonferroni_holm (Zakreski).

The Antonio Trujillo-Ortiz (2024) toolboxes include:
1) RMAOV2 (https://www.mathworks.com/matlabcentral/fileexchange/5578-rmaov2),
MATLAB Central File Exchange.

2) RMAOV33 (https://www.mathworks.com/matlabcentral/fileexchange/9638-rmaov33),
MATLAB Central File Exchange.