Classes in PsyNet#

Introduction to object-orientation#

PsyNet is an object-oriented framework. Object-oriented programming is a popular pattern in Python and many other programming languages. In object-oriented programming, one defines a collection of classes, where a class defines an abstract category of objects, for example ‘users’, ‘transactions’, or ‘events’. The programmer then creates and manipulates instances of these classes, called objects. In Python, one can create classes as follows:

class Person:
    def __init__(self, forename, surname):
        self.forename = forename
        self.surname = surname

    def greet(self):
        raise NotImplementedError

class EnglishPerson(Person):
    def greet(self):

class FrenchPerson(Person)
    def greet(self):

Here we created a base class called Person, and two subclasses called EnglishPerson and FrenchPerson. Subclasses inherit the structure of their parent class, but also can have additional custom logic. Here the EnglishPerson and FrenchPerson subclasses share the parent concept of forenames and surnames, but they have customized greeting methods corresponding to their respective languages.

We can then create instances of these classes as follows:

jeff = EnglishPerson(forename="Jeff", surname="Stevens")
madeleine = FrenchPerson(forename="Madeleine", surname="de la Coeur")

print(jeff.surname)  # yields "Stevens"

jeff.greet()  # yields "Hi!"
madeleine.greet()  # yields "Salut!"

Working with PsyNet requires fluency in object-oriented programming in Python. You should aim to be familiar with the following concepts:

  • Defining classes

  • Defining subclasses

  • Defining methods

  • Using the @property decorator

  • Using super()

  • Creating instances

  • Class attributes versus instance attributes

If some of these concepts are new to you, we recommend doing a few relevant online tutorials before proceeding.

PsyNet classes in

If you open a given PsyNet experiment (e.g. demos/mcmcp/ you will typically see a variety of PsyNet classes. These will be imported from particular PsyNet modules, for example:

from import InfoPage

Page classes like InfoPage are particularly important for defining the experiment’s timeline; you’ll see logic for instructions using this class, for example.

Many PsyNet experiments also include some custom subclasses that inherit from particular PsyNet classes. For example, you might see something like this:

from psynet.trial.mcmcp import MCMCPTrial

class CustomTrial(MCMCPTrial):
    def show_trial(self, ...):

This allows the experimenter to define a particular kind of trial for their experiment, that inherits certain functionality from core PsyNet (e.g. the logic of a Markov Chain Monte Carlo with People [MCMCP] experiment) but also adds custom logic (e.g. displaying a particular kind of stimulus to the participant).

In the next section we’ll introduce the core PsyNet classes in proper detail so that you understand how they all fit together and how they are used in practice.

Overview of key PsyNet classes#


The Experiment class is the most central class in the PsyNet experiment. It is defined in, the main Python file in your experiment directory. You define your Experiment class by subclassing PsyNet’s built-in Experiment class. Your custom Experiment class must include a definition of the experiment’s timeline:

import psynet.experiment

class Exp(psynet.experiment.Experiment):
    timeline = join(

The timeline attribute should receive a series of Elt objects (see below), with these Elts joined together using the join() function.

There are various other customizations that can be applied to the experiment via this experiment class, see the Experiment documentation for details.


The Participant class is used to represent participants. Each Participant object has various attributes that are populated during the experiment, carrying useful information for identifying the participant and recording their experience during the experiment. For example, gives a unique integer ID for the Participant; Participant.creation_time tells you when the Participant started the experiment; Participant.failed tells you if the Participant has been failed, and so on. For a full list of attributes see the Participant class documentation.

Most PsyNet experimenters do not interact much with built-in Participant attributes. Instead, they define custom Participant variables which are used to track state during the experiment. Participant variables are defined via Participant.var, and can take any name, for example Participant.var.custom_variable. For example, one might write print(participant.var.custom_variable) to print the current value of custom_variable, or write participant.var.custom_variable = 3 to set custom_variable to 3. For setting Participant variables in lambda functions (see below), Python syntax doesn’t allow you to write expressions like participant.var.custom_variable = 3 directly; instead we write participant.var.set("custom_variable", 3).


Elt objects define the logic of the experiment. They determine what materials are shown to the participant, how the participant responds to those materials, how the server processes those responses, and so on.

There are several main types of Elt objects:

We will now introduce each of these concepts in a little more detail. See their dedicated documentation for full information.


psynet.timeline.Page objects determine the web pages that are presented to the participant. The base psynet.timeline.Page class allows you to define a Page using a custom Jinja template. Jinja is a templating engine that is popular for creating websites with a Python back-end. For example, here’s what the template for psynet.timeline.SuccessfulEndPage currently looks like:

{% extends "timeline-page.html" %}

{% block main_body %}
    That's the end of the experiment!
    {% if config.show_reward %}
        {% include "final-page-rewards.html" %}
    {% endif %}
    Thank you for taking part.

    <p class="vspace"></p>
        Please click "Finish" to complete the HIT.
    <p class="vspace"></p>

    <button type="button" id="next-button" class="btn btn-primary btn-lg" onClick="dallinger.submitAssignment();">Finish</button>
{% endblock %}

Most PsyNet users don’t work with these Jinja templates directly. Instead, they use PsyNet helper classes that create these templates programmatically.

The simplest case is the InfoPage. The Info Page simply displays some information to the participant, and does not request any response. An Info Page can be created like this:

from import InfoPage

InfoPage("Welcome to the experiment!", time_estimate=5)

The time_estimate parameter tells PsyNet how many seconds the participant is expected to spend on the page. This is a common feature of PsyNet Pages. This time estimate is used to manage the progress bar and to compensate participants pro rata for their time on the experiment.

More often than not, experimenters eventually end up using the ModularPage class for their experiment implementations. The Modular Page is a powerful way of defining pages that combines two basic elements: the Prompt and the Control. The Prompt defines what is presented to the participant, whereas the Control defines their interface for responding. The PsyNet library contains many built-in implementations of Prompts and Controls, but it’s perfectly possible to create your own Prompts or Controls for a given experiment, and then reuse them in future experiment implementations.

Here’s an example of a Modular Page which combines an AudioPrompt with a PushButtonControl:

from psynet.modular_page import ModularPage, AudioPrompt, PushButtonControl

    AudioPrompt("", "Do you like this audio file?"),
    PushButtonControl(["Yes", "No"]),

The other important kind of page is the EndPage. An EndPage is used to mark the end of an experiment. There are two commonly used types of End Pages, triggering different end-of-experiment behavior: the SuccessfulEndPage and the UnsuccessfulEndPage. The latter is typically used when the participant fails some kind of performance check and is made to finish the experiment early.

Page Maker#

psynet.timeline.PageMaker objects generate Pages on-demand. The resulting pages can be dynamic, incorporating content that depends on the current state of the participant or the experiment.

from psynet.timeline import PageMaker

PageMaker(lambda participant: InfoPage(
    f"Welcome to the experiment, {}.",

The Page Maker takes a function as its primary argument. Typically we use a lambda function, which allows us to define the Page Maker content in-line. However, it’s also possible to pass a named function which is defined or imported earlier in the code.

The Page Maker function can optionally take a variety of arguments, of which participant is one. To find the full list of available arguments, see the documentation.

Warning: The Page Maker function will be called more than once for a given page, including whenever the page is refreshed. It is important therefore that the code is idempotent, i.e. calling it multiple times should have the same effect as calling it just once. It is a bad idea to incorporate random functions in this code.

Code Block#

psynet.timeline.CodeBlock objects define code that runs in between Pages. They are similar to Page Makers, but do not return pages. Like Page Makers, they take a function as the primary argument, which can optionally take a variety of arguments such as participant. Unlike Page Makers, they only ever run once, so they’re a safe place to put random functions.

from psynet.timeline import CodeBlock

CodeBlock(lambda participant: participant.var.seed = random.randint(0, 5))

Control Flow#

Control flow functions determine how these elements are sequenced within the timeline. They are currently not implemented as classes, but rather as pure functions; we might change this in the future though to achieve a cleaner syntax.

While Loop#

A While Loop repeats a particular series of Elts while a particular condition is satisfied. The condition is specified as a function that is called with various optional arguments, most commonly participant.

    lambda participant: participant.answer == "Yes",
            Prompt("Would you like to stay in this loop?"),
            control=PushButtonControl(["Yes", "No"], arrange_vertically=False),

For Loop#

A For Loop instructs PsyNet to loop over the values of a list, and using these values to dynamically generate Elts in the manner of a Page Maker. The following example uses a For Loop to create a series of Info Pages counting from 1 to 3:

from psynet.timeline import for_loop
from import InfoPage

    iterate_over=lambda: [1, 2, 3],
    logic=lambda number: InfoPage(f"{number} / 3"),

For Loops can also include random functions to generate their seed lists. This provides a straightforward way to randomize the order of material presented to Participants. For example:

import random
from psynet.timeline import for_loop
from import InfoPage

    iterate_over=lambda: random.sample(range(10), 3),
    logic=lambda number: InfoPage(f"Stimulus {number}"),


A Conditional construct is used to branch Timeline logic according to whether or not a given Condition is satisfied. The Condition is programmed as a function, analogous to the function for the While Loop, which should return either True or False. If the function returns True, then the logic follows the first branch; if it returns False, the logic follows the second branch (if such a branch was specified). For example:

from psynet.timelime import conditional
from import InfoPage

    lambda participant: participant.answer == "Yes",
    InfoPage("It's nice to hear that you like chocolate!", time_estimate=5),
        "I'm sorry to hear that you don't like chocolate...",


A Switch construct is a more powerful version of the Conditional construct that supports arbitrary numbers of branches. As before, the experimenter writes a function that is evaluated once the Participant reaches the Switch, but this time the function can return an arbitrary Python object (technically, this object must be ‘hashable’, which includes things like strings, integers, and floats). The experimenter then also provides a dictionary of branches, where each branch is a piece of Timeline logic, and the branches are keyed by possible outputs of the function. PsyNet sends the Participant to the branch that’s keyed by the output of the function. For example:

from psynet.timeline import switch

    lambda participant: participant.answer,
        "Red": InfoPage("You selected 'red'.", time_estimate=1),
        "Green": InfoPage("You selected 'green'.", time_estimate=1),
        "Blue": InfoPage("You selected 'blue'.", time_estimate=1),


A Module is a construct for organizing Timeline logic into standalone blocks. For example, if we create a pre-screening test that involves asking the Participant some spelling questions, we might make this pre-screening test a Module and then distribute it in a helper package.

Modules are useful for tracking the Participants’ journey through the experiment. For example, the Dashboard contains a useful visualization that shows how many Participants have started and finished each Module.

Modules are also useful for encapsulating Participant state. This means that variables don’t unintentionally leak from one part of the Experiment to the other, something which otherwise can produce subtle bugs. To take advantage of this feature, the experimenter avoids setting participant variables in this way (which sets variables that are ‘global’ to the entire timeline):

participant.var.custom_variable = 3

and instead sets participant variables this way:

participant.locals.custom_variable = 3

or equivalently:

participant.module_state.var.custom_variable = 3

Modules can be used as the base class for object-oriented hierarchies of Timeline constructs. For example, the TrialMaker class is a special kind of Module class that implements logic for administering Trials to the participant (see below). One day we might similarly create a PreScreen class for implementing pre-screening tests.

Modules are also useful for managing Assets, as described below.


An Asset is some kind of file (or collection of files) that is referenced during an experiment. These might for example be video files that we play to the participant, or perhaps audio recordings that we collect from the participant.

The API for Assets is powerful but complex. PsyNet provides many patterns for creating Assets and for accessing them within an experiment. These are documented in detail in the Assets chapter. For now, we will just illustrate the simplest of these patterns, which is to define an Asset at the Module level.

You can create an asset within a Module by passing it to the Module constructor’s assets argument. This argument expects a dictionary. For example:

import psynet.experiment
from psynet.asset import CachedAsset

class Exp(psynet.experiment.Experiment):
    timeline = join(
                "logo": CachedAsset("logo.svg"),

You can then access this asset within your module as follows:

from psynet.timeline import PageMaker

def my_pages():
    return PageMaker(
        lambda assets: ModularPage(
            ImagePrompt(assets["logo"], "Look at this image."),

Note how the asset must be accessed within a PageMaker, and is pulled from the optional assets argument that we included in the lambda function. This assets argument is populated with a dictionary of assets from the current module.


The Trial class represents a single Trial within the Experiment. A Trial typically involves administering some kind of stimulus to the Participant and recording their response.

The PsyNet experimenter typically creates their own Trial subclass as part of the Experiment implementation. This might look something like this:

from psynet.trial.main import Trial

class RateTrial(Trial):
    time_estimate = 3

    def show_trial(self, experiment, participant):
        word = self.definition["word"]

        return ModularPage(
            Markup(f"How happy is the following word: <strong>{word}</strong>"),
                ["Not at all", "A little", "Very much"],

This minimal example of a custom trial class has two important elements: time_estimate and show_trial.

The time_estimate attribute tells PsyNet how long an average Trial is expected to last, in seconds. This is used to construct progress bars and to reward participants for their progress through the experiment.

The show_trial method then defines how the Trial is displayed to the Participant. The show_trial method method should refer to the Trial’s definition attribute, which will be a dictionary containing defining information about the Trial, typically providing all the information required to uniquely determine the stimulus that will be presented to the Participant. Ordinarily the show_trial method should return a single page, however, it’s also possible to construct more complex multi-page Trials by returning a series of Elts wrapped in a call to join().

The simplest way to use a custom Trial class in an experiment is by using the cue() method. This inserts a Trial in the timeline with a given definition, with this definition provided as an argument to cue. The following example combines Trial.cue with a for_loop to deliver three trials with randomly sampled words:

    label="Randomly sample three words from the word list",
    iterate_over=lambda: random.sample(WORDS, 3),
    logic=lambda word: RateTrial.cue(
            "word": word,

Trials used in this way can also incorporate Assets. However, this approach is only recommended for External Assets (i.e. Assets that are hosted externally on a web server) or for Fast Function Assets (i.e. Assets that are generated on-demand).

audio_ratings = Module(
        label="Deliver 5 trials with randomly sampled parameters",
        iterate_over=lambda: [
                "frequency_gradient": random.uniform(-100, 100),
                "start_frequency": random.uniform(-100, 100),
            for _ in range(5)
        logic=lambda definition: RateTrial.cue(
                "audio": FastFunctionAsset(


If your experiment design requires the Participant session to depend on what happened in previous Participant sessions (e.g. if you want to ensure that every stimulus receives exactly the same number of ratings), or if it requires pregenerating Assets (which normally is sensible if your Assets are slow to generate), then you will likely want to take advantage of Nodes.

A Node is a PsyNet database construct that is used for organizing Trials. In particular, it can be conceptualized as a parent for Trials, storing important parameters that are used to define its child Trials, as well as storing Assets that the Trials can make use of.

Nodes are useful for enacting interactions between Participant sessions because they exist independently of individual Participants. In a non-adaptive experiment, a Node would typically represent a stimulus that is to be shown to multiple Participants. PsyNet can then balance stimulus selection by making sure that each Node ends up receiving the same number of Trials. In an adaptive experiment (e.g. Gibbs Sampling with People), a Node can instead represent the current state of the experiment (or, more specifically, the state of a particular chain within an experiment).

Nodes are useful for asset management because they are typically created before the Participant comes along. This means they can have a headstart with asset generation, meaning that the Participant isn’t kept waiting in the meantime. Moreover, since the same Node can spawn many Trials, the same Assets can be reused many times, instead of having to be regenerated for each new Trial.

The simplest way to use Nodes in an experiment is to create a collection of Nodes in and use these for your Trials. Here’s an example from a PsyNet demo:

def synth_stimulus(path, frequencies):
    synth_prosody(vector=frequencies, output_path=path)

            "frequency_gradient": frequency_gradient,
            "start_frequency": start_frequency,
            "frequencies": [start_frequency + i * frequency_gradient for i in range(5)],
            "stimulus": CachedFunctionAsset(
    for frequency_gradient in [-100, -50, 0, 50, 100]
    for start_frequency in [-100, 0, 100]

class RateTrial(Trial):
    time_estimate = 5

    def show_trial(self, experiment, participant):
        return ModularPage(
                text="How happy is the following word?",
                ["Not at all", "A little", "Very much"],

audio_ratings = Module(
        label="Deliver 5 random samples from the stimulus set",
        iterate_over=lambda nodes: random.sample(nodes, 5),
        logic=lambda node: RateTrial.cue(node),

Here the Nodes are used to define a stimulus set that explores a factorial combination of two variables, frequency_gradient and start_frequency. Each Node has an Asset, specifically a Cached Function Asset, defined as a function that gets its arguments from the Node’s definition. When the experiment is deployed, PsyNet will automatically generate the full set of Assets if it doesn’t find them in its cache.

Note how the Nodes are passed to the Module call. This ensures that the Nodes are recognized by the Experiment, and it associates the Nodes with the "audio_ratings" module. Now code within that module (e.g. Page Makers, For Loops) can access those Nodes within lambda functions, as in the example above. These nodes can be used to create Trials by using the Trial.cue method, as in the example above. The Trial then inherits the Node’s definition (in this case frequency_gradient, start_frequency, and frequencies); the Node’s assets then can be accessed through trial.node.assets.

It is also possible to create Nodes during the Experiment using similar techniques, but at the time of writing we haven’t got a demo for this yet. Watch this space.

Trial maker#

The previous sections described how trial-based experiments can be implemented using the Trial.cue method. With this approach, the experimenter has to define the logic of choosing Trials themselves using constructs such as For Loops. However, such logic can get complex and repetitive. PsyNet therefore provides some built-in constructs that cover a variety of use cases, including:

  • Static experiments, where Trials are generated from a pre-specified collection of Nodes, and Node selection is balanced to ensure that Trials accumulate evenly across Nodes;

  • Serial reproduction, where a participant imitates a stimulus, another participant imitates that imitation, and so on for many generations;

  • Markov Chain Monte Carlo with People, a procedure which coordinates many two-alternative forced-choice trials into a process which stochastically samples from a (possibly high-dimensional) stimulus space;

  • Gibbs Sampling with People, a variant of Markov Chain Monte Carlo with People based on a continuous slider-based task.

These constructs are implemented as Trial Makers (psynet.trial.main.TrialMaker). A Trial Maker is a special kind of Module that provides logic for administering Trials within an experiment. Experiments using a Trial Maker typically implement custom Trial classes, as before. Complex experiments (e.g. chain-based) experiments will typically also implement a custom Node class. Then, instead of using some combination of For Loops with Trial.cue, the experimenter instead inserts a Trial Maker instance into the Timeline. This Trial Maker might look something like this:


This Trial Maker has several features as determined by the options that have been passed to it:

  • PsyNet will expect each participant to take about 6 trials;

  • Each participant will take no more than 2 trials in each block;

  • Each participant is in theory allowed to take multiple Trials from the same Node;

  • Node selection will be balanced, meaning that Trials should accumulate evenly across Nodes;

  • PsyNet will check the participant’s performance at the end of the Trial Maker, and potentially terminate their session if they perform too badly;

  • The Trial Maker will prompt PsyNet to keep recruiting until 50 participants have been recruited;

  • The Trial Maker will administer three Trials at the end that are repeats of three randomly selected Trials from earlier in the Trial Maker; the results from these Trials can be used to evaluate the participant’s test-rest reliability.

Explore documentation for specific Trial Maker classes as well as PsyNet demos for more information.

Connection to SQLAlchemy classes#

Several PsyNet classes are database-backed, which means that their objects are represented as rows in a database. This enables object states to be communicated across servers and persisted throughout the duration of the experiment. Examples of database-backed classes include:

  • Participants

  • Trials

  • Nodes

  • Assets

  • Error Logs

  • Asynchronous Processes

This database integration is implemented via SQLAlchemy. SQLAlchemy is a powerful Python package that creates a mapping between Python objects and database elements.

In most PsyNet usage you do not need to worry much about the mechanics of this database integration. As long as you work with pre-existing object attributes and variable stores (e.g. participant.var.my_variable), then your changes should propagate and persist just as you expect. However in advanced usage you will eventually want to understand more about how this integration works. We will soon include a tutorial on SQLAlchemy usage into this documentation website.

Connection to Dallinger classes#

As you may know, PsyNet is built on an earlier platform called Dallinger which deals with many of the lower-level aspects of server management and experiment deployment. Dallinger has its own collection of database-backed classes which are designed with a particular focus on cultural simulation experiments, with names such as Info, Vector, Transmission, Node, and so on.

Several PsyNet classes are built on some of these pre-existing Dallinger classes. For example, Trials are built on Infos, PsyNet Nodes are built on Dallinger Nodes, and PsyNet Nodes are organized into Networks, just like in Dallinger. The motivation for this inheritance is that it allows PsyNet to share some features with Dallinger, in particular as regards network visualizations in the dashboard. However, it does mean that in a few places in the code and the database you might see the word Info used when you were expecting to see the word Trial. We are working to eliminate these instances to make the abstraction more intuitive.