How to learn#
See also the work-in-progress Tracks section, which will provide learning programmes for various uses of PsyNet.
Before you start learning PsyNet there are a few general-purpose programming tools you should familiarize yourself with, such as Python and Git; see Prerequisites for details.
Once you’re happy with the prerequisites, the next thing you should do is skim a few
example PsyNet experiments
to get a feel for the way that real-life experiments can be implemented using PsyNet.
A good starting point is the repository’s
experiment.py file, but it’s worth exploring other .py files in the
You should now spend some time reading about essential PsyNet principles. This online documentation website contains lots of material here. Have a read through the Tutorials section and see what catches your eye. Some tutorials will not be relevant to you, depending on what kinds of experiments you’re planning on running; feel free to skip them.
Now you can start playing with PsyNet yourself. A good starting point is to start playing with some demos. Start off by downloading the PsyNet repository to your local machine; I normally do this by going to a terminal and typing the following:
cd # Navigates to your home directory git clone email@example.com:PsyNetDev/psynet
Open the resulting folder (
~/PsyNet) in your IDE (we normally recommend PyCharm).
You can then navigate to the
demos folder to see all the demos contained in PsyNet.
In your PyCharm terminal, you can navigate to a particular demo you want to run like this:
# The precise path will depend on where you downloaded PsyNet. cd ~/PsyNet/demos/timeline
Then you can run the demo using the standard PsyNet debug command:
# If you are using the Docker approach bash docker/psynet debug local # If you instead have a local installation of PsyNet psynet debug local
You can try changing parts of the demo now to test your understanding of PsyNet, for example modifying the page display, changing the stimuli, and so on. Note that some changes will manifest correctly as soon as you save your code and refresh the page; others will only manifest when you start a new participant session via the dashboard; others still will require you to start a new debug session by cancelling the debug command (Ctrl-C) and running it again.
Once you are ready to develop your own experiment, you will want to move outside the PsyNet repository and create your own repository. A good way to start is by copying an existing demo, or an existing experiment implementation, to a new location on your computer. If you’re going to use Git for version control, you can then initialize a Git repository in this location and link it to a remote repository on GitHub or similar. Then you can start changing code more wholesale.
Often you will only need to understand a particular subset of PsyNet’s features for implementing a particular experiment. Some of these features will be described in this website’s Tutorials section. Many of them will be illustrated in one or more of the PsyNet demos. It’s worth having a look through these demos to identify which of them provide relevant examples, and try to repurpose them for your own experiment.
When writing specific bits of PsyNet code you will often need to consult lower-level documentation for individual classes and functions. One way to do this is to search the class/function name in this website’s search box, or to look it up in the API documentation section. Alternatively, most IDEs will let you right click on PsyNet classes/functions and follow the link to the PsyNet source code, where you can see the documentation directly in the code. The latter approach is often more powerful as it allows you to see the underlying code itself, which is often the best way of understanding its functioning.
Learning PsyNet takes a while, but it pays off handsomely. Give it a few months, and you’ll be implementing fresh experiments in no time at all!