This experiment description is internally stored in terms of standard Python objects: a Python list of Routines, each of which is a list of Components, which are themselves essentially a Python dictionary of parameters and, finally, a list of items on the Flow.
The experimental timing is controlled by specifying the times of onset and offset of the stimuli and of response-gathering events within the Routines themselves. The Flow has no “knowledge” of time per se it simply runs each Routine immediately after the previous one has ended. It contains the Routines themselves, as well as Loops (which repeat the Routines they encompass). The last part of the experiment description is the Flow: a flow diagram that controls how the Routines relate to each other.
#Getting started with psychopy series
The Components in the Routines can be thought of as a series of tracks in a video- or music-editing suite they can be controlled independently in time-that is, onsets and offsets-but also in terms of their properties. In PsychoPy Builder, an experiment is described by a set of Routines, which contain a set of one or more Components, such as stimuli and response options. There was also an open-source Macintosh application called PsyScopeX ( ), buCupdate since 2015. PsychoPy and OpenSesame remain, to our knowledge, the most versatile open-source experiment-building packages currently available, and we compare them in the following section. Another Python-based application, OpenSesame (Mathôt, Schreij, & Theeuwes, 2012), was, however, developed around the same time as the PsychoPy Builder interface. Most critically, however, the other libraries do not offer a graphical interface to create studies, which limits their suitability for undergraduate teaching. In comparison to these, PsychoPy offers a broader list of stimulus options, experimental designs, response options (such as rating scales), and hardware support, as well as a larger community of active developers. Since 2008, numerous additional libraries have been created in Python, such as Expyriment (Krause & Lindemann, 2014), PyGaze (Dalmaijer, Mathôt, & Van der Stigchel, 2014), mPsy ( ), and SMILE ( ).
We discuss the current state of the project, as well as plans for the future.Īt the time that the core PsychoPy library was written, the other comparable packages were Vision Egg (Straw, 2008) and PyEPL (Geller, Schlefer, Sederberg, Jacobs, & Kahana, 2007), both of which subsequently ceased development. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We also present some of the other new features, including further stimulus options, asynchronous time-stamped hardware polling, and better support for open science and reproducibility.
#Getting started with psychopy code
The most notable addition has been that Builder interface, allowing users to create studies with minimal or no programming, while also allowing the insertion of Python code for maximal flexibility. Here we describe the features that have been added over the last 10 years of its development. It now provides a choice of interface users can write scripts in Python if they choose, while those who prefer to construct experiments graphically can use the new Builder interface. PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli.
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