kirschberg32/flanker_task

7. choose a license (probably open community)
8. badges, how to contribute, include tests?

An approach to slow event-related Flanker Task

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.

Table of Files:

• README.md -> this is what you are currently reading and which shall give you an overview about what to expect
• PsychoPy.py -> we did not put anything in there, yet, guyyyyys!!! D:
• FlankerExperiment.py -> run the experiment on your own contribute data points to our data collection
• 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

Here is what we used

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 a nice user interface, we then used the package psychopy in Coder-Mode in order to make everything more accessible.

Here is what you need

In order to make use of this code, you have to be able to run Python code (Python version 3.6 or newer) 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. A thing we as contributors founnd very helpful when using psychopy on an Ubuntu Linux distribution was to use Ubuntu 18.04, since we found newer version(s) not quite compatible in usage. Apart from that, there are only few things about this repository and the files it contains that you need to know.

Here is what to do

This repo gives you access to many different files, be they basic python code files(.py), python notebooks(.ipynb), data-files(.csv) and more. The data we obtained from an official 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. This works for runnng the experiment as well as running the psychopy interface.

Here is what you can do

This respository, as already mentioned multiple times, gives you the chance to participate in theexperiment 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

Data references / Credits

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

A last word about us

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

• The Yeti
• laHellmann
• meJansen