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<p>This repository contains data and materials for the experiment reported in our paper:</p> <p>van Moorselaar, D., Foster, J.J., Sutterer, D.W., Theeuwes, J., Olivers, C.N.L. & Awh, E (2017). Spatially selective alpha oscillations reveal moment-by-moment trade-offs between working memory and attention. Journal of Cognitive Neuroscience.</p> <p>Data citation: See citation list in the top-right corner of the main project page.</p> <p>Usage: This OSF project contains the data and analysis scripts for the experiment in our Journal of Cognitive Neuroscience paper. If you would like to use the data in published work, please cite both the paper and OSF data set.</p> <p>We analyzed the data using MATLAB and python, so the data are .mat files and the analysis scripts are .m files and .py files. To run the scripts, you will need access to MATLAB and python. Specifically, all preprocessing was done in Matlab. The inverted spatial encoding model was performed in python.</p> <h2><strong>Instructions for Use</strong></h2> <p>Here you can find all the data and materials for the experiment reported in our JoCN paper</p> <h3><strong>Contents</strong></h3> <p><strong>Exp_file.osexp</strong> is the script used to run the experiment (this script requires OpenenSesame; see <a href="http://osdoc.cogsci.nl/;" rel="nofollow">http://osdoc.cogsci.nl/;</a> Mathôt, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314-324. doi:10.3758/s13428-011-0168-7). </p> <h4><strong>Folders</strong></h4> <p><strong>AnalysisScripts</strong> – this folder contains all analysis scripts needed to reproduce the analyses in the paper. The folder also contains a <strong>readME.txt</strong> file with instructions.</p> <p><strong>Behavior</strong> – this folder contains the raw behavior files. For each subject, there is an individual file. There also is one single file that contains the compiled bahavior files, which will be easier to work with.</p> <p><strong>EEG</strong> – this folder contains the segmented EEG data.</p> <p>You can find more detailed descriptions of the contents of each of these folders in the sections below.</p> <h3><strong>'AnalysisScripts' Folder</strong></h3> <p>This folder contains the analysis scripts including plotting functions. The script requires a predefined folder structure as explained in the <strong>readMe</strong> file. Below are brief descriptions of what each script does.</p> <p><strong><a href="http://SpatialEM.py" rel="nofollow">SpatialEM.py</a></strong>: script contains all analysis steps to create spatial tuning functions from raw (preprocessed) eeg, including plotting functions.</p> <p><strong><a href="http://session.py" rel="nofollow">session.py</a></strong>: script points SpatialEM to raw data files and makes sure that intermediate files and plots are saved in correct location. </p> <h3><strong>'Behavior' Folder</strong></h3> <p>This folder only contains a subfolder <strong>replaced</strong> which contains the data of the participants that were replaced, because they had too many contaminated eeg epochs. The individual data files (.csv) as produced by OpenSesame and the compiled behavior data (.xlsx) without practice trials are stored on Figshare and can be accessed via the figshare add on. Here are the critical variables in each file:</p> <p><strong>RT_search</strong>: reaction time on the covert attention task in the dual task blocks</p> <p><strong>block_type</strong>: indicating whether the trial was within a single or a dual task block</p> <p><strong>cue</strong>: indicating whether the target was cued valid or invalid</p> <p><strong>degrees_response</strong>: position in degrees on angular rim as indicated by participant</p> <p><strong>level_staircase</strong>: SOA between target onset and mask (only adjusted in practice block)</p> <p><strong>location_bin_mem</strong>: spatial position bin of memory location (in degrees)</p> <p><strong>location_bin_search</strong>: spatial position bin of cue location (in integers)</p> <p><strong>memory_orient</strong>: position in degrees of grey disk in memory display</p> <p><strong>practice</strong>: specifies whether trials was a practice trial or not</p> <p><strong>search_resp</strong>: specifies whether covert attention response in dual-task condition was correct (0 = incorrect, 1 = correct)</p> <p><strong>subject_nr</strong>: participant number</p> <h3><strong>'EEG' Folder</strong></h3> <p>This folder contains the EEG data for each subject. There is also a subfolder <strong>replaced</strong> which contains the raw .bdf files of replaced participants. The important variables for each subject are:</p> <p><strong>erp.trial.data</strong>: a Trials x Electrodes x Samples matrix of segmented EEG data.</p> <p><strong>erp.chanLabels</strong>: the names of the electrodes recorded from (after removing bad electrodes).</p> <p><strong>erp.arf.artIndCleaned</strong>: index of trials with artifacts (1 = artifact, 0 = clean trial).</p> <p><strong>erp.preTime</strong>: the start of the segment in milliseconds relative to onset of the sample stimulus.</p> <p><strong>erp.postTime</strong>: the end of the segment in milliseconds relative to onset of the sample stimulus.</p> <p>The <strong>sampling rate</strong> was 512 Hz.</p> <p>A brief note on our artifact rejection procedures…</p> <p>Our artifact rejection procedure involved two steps:</p> <p>Step 1: We applied an automated artifact detection algorithm.</p> <p>Step 2: We manually inspected the data to ensure that automatic routine was catching artifacts and was not throwing away clean trials. Usually, we inspect all trials and correct any errors the automatic routine made. </p> <p>For any further questions or comments, please email me at dirkvanmoorselaar@gmail.com</p>
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