Congratulations, you have found the repository in which you can now easily participate in the Flanker Task experiment and contribute data to our project! This project is all about getting in contact with, participating in and understanding results of the slow event-related Flanker Task experiment as it was introduced and first run by Eriksen.
- README.md -> this is what you are currently reading and which shall give you an overview about what to expect
- menu.py -> the file in which all applications come together
- FlankerExperiment.py -> run the experiment on your own contribute data points to our data collection
- DataWrangling.py -> collection of funtions to construct the plots you can also look at in DataSummary.ipynb
- PersonalSummary.py -> another collection of functions to construct plots with which you can look at your own results
- DataSummary.ipynb -> have a look at data tidying and some summary plots based on the data we have!
- data/data.csv -> our whole data collection
- requirements.txt -> helps you to check whether you have and, if necessary, download or update the packages required to run all code chunks in this repository's files
Apart from basic packages like python's numpy and pandas package for data wrangling, we used plotnine's ggplot, as well as matplotlib to construct the plots that you can check out in the file DataSummary.ipynb. If you are already familiar with the data science programming language R, getting along with the code might be child's play for you! Bar plots, violin plots or jittering data points can all be very easy with ggplot, since it does a lot of things implicitly, which means you do not have to state how the program shall proceed. Forthermore, matplotlib was used for one specific stacked bar plot that we were reluctant to leave out, which is why matplotlib is included here as well.
Conducting the Flanker Task experiment is done with help of the package expyriment in the file FlankerExperiment.py. It comes in handy when including stimuli that are presented on screen and enables to obtain extremely precise reaction times with a fault range of roughly 20ms.
For Ubuntu Linux, you can open a terminal and run sudo apt-get install git.
For MacOS, first install Homebrew if you don't have it already from here. Then run brew install git.
For Windows, download the .exe from here and run the installer. In the install wizard, make sure that git can be used from the command prompt. Other than that, you'll probably go for the openSSL as well as Windows default console as terminal emulator options.
Download the correct installer for your system from here. This will depend on your operating system and whether your have a 32-bit or 64-bit system (on Linux, this can be determined by running getconf LONG_BIT).
To start the installer, navigate to the place where you downloaded the file to in the terminal and run bash Miniconda3-latest-Linux-x86_64.sh. If the file you downloaded is named differently, type that file name after bash instead. Most options can just be answered with yes, however you should insist on adding conda to your PATH if the installer asks you.
Start the graphical installer, it should guide through the installation process. If you don't have another Python installation already installed, you can safely add Python to your %PATH%, such that you can use it from your normal cmd-shell. In any case, you should have a program named "Anaconda prompt" installed afterward, which you can find with the start menu. If you didn't add it to your %PATH% as instructed above, you need to run all conda-commands in the Anaconda prompt, otherwise you can use the normal cmd. Make sure to not use the PowerShell, as some conda-commands won't work properly there.
If you decide to use an environment later on, make sure you have admin rights for the terminal you use. To do that, hit Win type cmd, right-click the first option Command Prompt, and select Open as Administrator.
Before you continue the installation process, you need to update conda in order to install the environment. Do so by using the command conda update conda
Check that you now have a working Conda installation by running conda --version.
Check that you now have a working Python installation by running python --version.
Now make sure that you use Python version 3.7 or higher on your machine and install the packages listed in the file requirements.txt. It is, therefore, best to run the installation of all packages via a command like pip install -r requirements.txt. This command reads out all the packages as well as the corresponding version you need in order to avoid encountering hardships and installs them right away. Apart from that, the only thing you need to know for working with this repository is what you can do with it, which is the next point.
This repo gives you access to many different files, be they basic python scripts(.py), python notebooks(.ipynb), data-files(.csv) and more. The data we obtained from an official experient run, as well as the data everybody contributes can be found in the csv-file of the folder /data. You might want to have a brief look at it or peak at the official files on the Internet (see this).
In order to run any python script in this repo, go to the directory in which the experiment script is saved and use the basic command python SCRIPTNAME.py. In order to use the interface we constructed and browse more easily, execute the menu.py file as described above.
This respository, as already mentioned multiple times, gives you the chance to participate in the experiment and contribute data to out project. What you might ask yourself now are questions like: what is the slow event-related Flanker Task? and what knowledge gain does this experiment bring?.
The Flanker Task is a reaction task in which it is neccessary to press a button depending on the stimulus that is presented on the screen. These stimuli consist of arrays of "arrows" pointing to either the right or the left and can look like "<<<<<" (for which you would have to press the left-arrow key) or "<<><<" (for which you would have to press the right-arrow key). These stimuli can be categorized in the categories congruent and incongruent and you already combined very well that the key you have to press depends on whether the middle arrow points to the left or right.
In order to get a more detailed description of the Flanker Task experiment and see some plots about what results we can obtain, see the file DataSummary.ipynb. There, we include information about experiment conduction and present a showcase of data tidying and some basic summary statistics. But there is more to discover! Run the experiment on your own or just look at the python script.
If, after running the experiment on your local machine, you want to continue browsing around, re-open the menu.py-file in a fresh terminal, since the panels to choose options to not react otherwise.
In case you did the eyperiment multiple times on your machine and collected several .xpd-files containing rich and valuable data that you want to have visualized, change the filename in line xx of the file PersonalSummary.py to the filename you want to have visualized. Per "default", the program only makes use of the first run's data, so if you do not aim to delete every data file you constructed before, save some time and simply change the filename.
Kelly, A.M., Uddin, L.Q., Biswal, B.B., Castellanos, F.X., Milham, M.P. (2008). Competition between functional brain networks mediates behavioral variability. Neuroimage, 39(1):527-37
Mennes, M., Kelly, C., Zuo, X.N., Di Martino, A., Biswal, B.B., Castellanos, F.X., Milham, M.P. (2010). Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity. Neuroimage, 50(4):1690-701. doi:
10.1016/j.neuroimage.2010.01.002. Epub 2010 Jan 15. Erratum in: Neuroimage. 2011 Mar 1;55(1):434
Mennes, M., Zuo, X.N., Kelly, C., Di Martino, A., Zang, Y.F., Biswal, B., Castellanos, F.X., Milham, M.P. (2011). Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics. Neuroimage, 54(4):2950-9. doi:
10.1016/j.neuroimage.2010.10.046
We are a group of three Cognintive Science students at the university of Osnabrueck and what you can discover in this repository is, in fact, the final project we did for the course Scientific Programming in Python. There might be a lot of things to be improved, but we would like to ask you to be not too hard on us, though we would love to get honest advice on how to improve our coding and documentation skills, of course! Thanks for sticking with us to the end of this README-file and we hope you can have an interesting time browsing around! :)
Greetings from our GitHub-profiles
When it comes to the data we collected from the official experiment run, we have faith that if you use this repository, you leave all this valuable data untouched!