PsychoPy2: Experiments in behavior made easy

By Jonathan Westley Peirce, Jeremy R. Gray, Sol Simpson, Michael R. MacAskill, Richard Höchenberger, Hiroyuki Sogo, Erik K. Kastman, Jonas Kristoffer Lindeløv. Behavior Research Methods, 2019

4,363 citations recorded by the source corpus.

PsychoPy is an application for the creation of experiments in behavioral science (psychology, neuroscience, linguistics, etc.) with precise spatial control and timing of stimuli. 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. Here we describe the features that have been added over the last 10 years of its development. 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. 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. Tens of thousands of users now launch PsychoPy every month, and more than 90 people have contributed to the code. We discuss the current state of the project, as well as plans for the future.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d8de6b18497823512b556041dd86a70f12a8b1f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

  1. Michael R. MacAskill
  2. Jonathan Westley Peirce
  3. Tal Yarkoni
  4. Yoritaka Akimoto
  5. Keito Miyake
  6. Sebastiaan Mathôt
  7. Craig Hedge
  8. Petroc Sumner

In Computer Vision and Pattern Recognition

  1. Tal Yarkoni

Citations in the corpus