The idea behind the jaysire package is to allow users to write flexible behavioural experiments that run through a web browser, relying on the jsPsych javascript library to run the experiment itself. In this article, I’ll walk you through the process of creating a very minimal experiment using the package. The first step, as usual, is loading the package:


Because the jaysire package is a wrapper around the jsPsych library, the place to start is with jsPsych itself. The key idea in jsPsych is that your job is to provide a description of the experiment, referred to as a timeline. The timeline is an abstract description of what stimuli should be shown to participants, how responses are to be collected, what randomisation procedure to follow, and so on. Once you have specified the timeline, the jsPsych library takes care of all the low level actions like determining which trial will run next, where the data are stored and so on.

In its simplest form, a timeline is straightforward: it’s just a list of the trials that make up the experiment, listed in the order that they should be run. We’ll talk about fancier features like randomisation, loops, conditional branching etc later on, but for now let’s imagine that all we want to do is run a fixed sequence of trials in a fixed order.

That sounds pretty straightforward, but raises the natural question of “what is a ‘trial’, exactly?” Behavioural experiments can vary in all sorts of ways, and so the number of things that can be considered a “trial” in an experiment is limited only by your imagination. The way that jsPsych handles this is providing a system of plugins, where each plugin defines a different kind of experimental trial, and allows you to write your own plugins if you need to. Within the jaysire package, there is a function called trial_generic() that allows you to create a trial using any plugin you like, but in most cases it is more convenient to use a more specific function because the arguments are better documented.

Creating instructions

In most experiments, the first thing we want to do is present some instructions to the participant. jsPsych contains an “instructions” plugin that will present multiple pages of instructions to the participant, and there is a corresponding function in jaysire called trial_instructions() that we can use:

In this code, the pages argument specifies four very short pages of text that will be displayed to the participants. By setting show_clickable_nav to TRUE, we are telling jsPsych to display a pair of buttons that participants can click to move forward or backward within the instructions. By default, these are labelled “Next” and “Previous”, but you can change this if you like. The third thing I’ve specified here is the post_trial_gap, which is the length of time (in milliseconds) that the experiment will pause between the end of this trial (the instructions) and the start of the next one. During this “gap” period a blank screen is shown.

Creating simple trials

Our next job is to write some experimental trials! We’ll keep it simple in this first example, and create two variables trial1 and trial2. In both cases we’ll present people with a piece of text, and then ask them to respond by clicking a button with the mouse. We can do this with the trial_html_button_response() function. This function will use the “html-button-response” plugin within jsPsych. The plugin does exactly what you might expect given the name: it displays some HTML as the stimulus (in this case, just some regular text), and collects responses using buttons! Here’s the code to create trial1:

In this code the stimulus argument specifies the text that will be displayed on screen to the participant, and the choices argument specifies the labels that will be shown on the response buttons. Again, the post_trial_gap argument is used to tell jsPsych how long to pause before starting the next trial. We can create trial2 in much the same way:

In fact, the code for trial2 is so similar to the code for trial1 that it feels inefficient. There should be a way to create both trials at the same time, and indeed there is, which I’ll talk about that later.

Creating a timeline

Now that we have our trial objects, instructions, trial1 and trial2, our next taks is to bind together into a timeline. The build_timeline() function allows us to do this:

all_trials <- build_timeline(instructions, trial1, trial2)

At this point we have a complete timeline for our simple experiment! Yay!

Building the experiment

At the moment we have a complete timeline, but it is stored in an abstract form as the all_trials variable. What we really want to do is “build” an experiment from this timeline: we want to write the files that will run the experiment, and save those files somewhere. That is the job of the build_experiment() function. First, let’s specify the location of the experiment. Normally, we would build the experiment into a sensible location (e.g., somewhere inside an RStudio project), but for the purposes of this demonstration – which has to be reproducible on any computer, not just mine – I’ll use the temporary_folder() function for this purposes:

Now all we have to do is write the experiment into this folder build_experiment() function, specifying the timeline and path arguments to tell R what to write and where to write it:

The other thing I’ve specified here is the on_finish argument, which tells jsPsych what do to when the experiment ends. For our simple experiment, the only thing we want to do is save the data. To keep it simple, we’ll assume the experiment is going to be run on the same computer where the experiment is stored, and so I’ve used the save_locally() function here.

What have we created?

When we run the build_experiment() function, two subfolders within the exp_path folder are created, one called “data” (which is initially empty) and another called “experiment”, which contains all the source files required to run the experiment. Here are the files we’ve just created:

list.files(exp_path, recursive = TRUE)
#> character(0)

Running the experiment

To run the experiment on the local machine (and save the data to the “data” folder) all we have to do is call the run_locally() function, specifying the location of the experiment to run: