Open data: Is Auditory Awareness Negativity Confounded by Performance?

The main file is performance_correction.html in AAN3_analysis_scripts.zipOpens in a new tab. It contains the results of the main analyses. See AAN3_readme_figshare.txt: 1. Title of Dataset:Open data: Is auditory awareness negativity confounded by performance? 2. Author Information A. Principal Investigator Contact Information Name: Stefan Wiens Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.su.se/profiles/swiens-1.184142Opens in a new tab Email: sws@psychology.su.seOpens in a new tab B. Associate or Co-investigator Contact Information Name: Rasmus Eklund Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.su.se/profiles/raek2031-1.223133Opens in a new tab Email: rasmus.eklund@psychology.su.seOpens in a new tab C. Associate or Co-investigator Contact Information Name: Billy Gerdfeldter Institution: Department of Psychology, Stockholm University, Sweden Internet: https://www.su.se/profiles/bige1544-1.403208Opens in a new tab Email: billy.gerdfeldter@psychology.su.seOpens in a new tab 3. Date of data collection: Subjects (N = 28) were tested between 2018-12-03 and 2019-01-18. 4. Geographic location of data collection: Department of Psychology, Stockholm, Sweden 5. Information about funding sources that supported the collection of the data: Swedish Research Council / Vetenskapsrådet (Grant 2015-01181) Marianne and Marcus Wallenberg (Grant 2019-0102) SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: CC BY 4.0 2. Links to publications that cite or use the data: Eklund R., Gerdfeldter B., & Wiens S. (2020). Is auditory awareness negativity confounded by performance? Consciousness and Cognition. https://doi.org/10.1016/j.concog.2020.102954Opens in a new tab The study was preregistered: https://doi.org/10.17605/OSF.IO/W4U7VOpens in a new tab 3. Links to other publicly accessible locations of the data: N/A 4. Links/relationships to ancillary data sets: N/A 5. Was data derived from another source? No 6. Recommended citation for this dataset: Eklund R., Gerdfeldter B., & Wiens S. (2020). Open data: Is auditory awareness negativity confounded by performance? Stockholm: Stockholm University. https://doi.org/10.17045/sthlmuni.9724280Opens in a new tab DATA & FILE OVERVIEW File List: The files contain the raw data, scripts, and results of main and supplementary analyses of the electroencephalography (EEG) study. Links to the hardware and software are provided under methodological information. AAN3_experiment_scripts.zipOpens in a new tab: contains the Python files to run the experiment AAN3_rawdata_EEG.zipOpens in a new tab: contains raw EEG data files for each subject in .bdf format (generated by Biosemi) AAN3_rawdata_log.zipOpens in a new tab: contains log files of the EEG session (generated by Python) AAN3_EEG_scripts.zipOpens in a new tab: Python-MNE scripts to process and to analyze the EEG data AAN3_EEG_source_localization_scripts.zipOpens in a new tab: Python-MNE files needed for source localization. The template MRI is provided in this zip. The files are obtained from the MNE tutorial (https://mne.tools/stable/auto_tutorials/source-modeling/plot_eeg_no_mri.html?highlight=templateOpens in a new tab). Note that the stc folder is empty. The source time course files are not provided because of their large size. They can quickly be generated from the analysis script. They are needed for the source localization. AAN3_analysis_scripts.zipOpens in a new tab: R scripts to analyze the data. The main file is performance_correction.html. It contains the results of the main analyses. AAN3_results.zipOpens in a new tab: contains summary data files, figures, and tables that are created by Python-MNE and R. METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: The auditory stimuli were two 100-ms tones (f = 900 Hz and 1400 Hz, 5 ms fade-in and fade-out). The experiment was programmed in Python: https://www.python.orgOpens in a new tab and used extra functions from here: https://github.com/stamnosslin/mnOpens in a new tab The EEG data were recorded with an Active Two BioSemi system (BioSemi, Amsterdam, Netherlands; www.biosemi.comOpens in a new tab) and saved in .bdf format. For more information, see linked publication. 2. Methods for processing the data: We computed event-related potentials and source localization. See linked publication 3. Instrument- or software-specific information needed to interpret the data: MNE-Python (Gramfort A., et al., 2013): https://mne.tools/stable/index.html#Opens in a new tab Rstudio used with R (R Core Team, 2016): https://rstudio.com/products/rstudioOpens in a new tab Wiens, S. (2017). Aladins Bayes Factor in R (Version 3). https://www.doi.org/10.17045/sthlmuni.4981154.v3Opens in a new tab 4. Standards and calibration information, if appropriate: For information, see linked publication. 5. Environmental/experimental conditions: For information, see linked publication. 6. Describe any quality-assurance procedures performed on the data: For information, see linked publication. 7. People involved with sample collection, processing, analysis and/or submission: - Data collection: Rasmus Eklund with assistance from Billy Gerdfeldter. - Data processing, analysis, and submission: Rasmus Eklund and Stefan Wiens DATA-SPECIFIC INFORMATION: All relevant information can be found in the MNE-Python and R scripts (in EEG_scripts and analysis_scripts folders) that process the raw data. For example, we added notes to explain what different variables mean. The folder structure needs to be as follows: AAN3 (main folder) --->data --->--->bdf (AAN3_rawdata_EEG) --->--->log (AAN3_rawdata_log) --->--->raw (empty) --->MNE (AAN3_EEG_scripts) --->R (AAN3_analysis_scripts) --->results (AAN3_results) --->source (AAN3_EEG_source_localization_files) To run the MNE-Python scripts: Anaconda was used with MNE-Python 0.20 (see installation at https://mne.tools/stable/index.html#Opens in a new tab ). For Downsample_AAN3, ICA_raw_AAN3, Preprocess_AAN3, Make_inverse_operator_AAN3.pyOpens in a new tab, BehaviorTables_AAN3, and PlotSource, the complete scripts should be run (from anaconda prompt). For Analysis_AAN3, one section at the time should be run (from Spyder).