Behavior Research Methods

, Volume 47, Issue 1, pp 1–12 | Cite as

jsPsych: A JavaScript library for creating behavioral experiments in a Web browser

  • Joshua R. de LeeuwEmail author


Online experiments are growing in popularity, and the increasing sophistication of Web technology has made it possible to run complex behavioral experiments online using only a Web browser. Unlike with offline laboratory experiments, however, few tools exist to aid in the development of browser-based experiments. This makes the process of creating an experiment slow and challenging, particularly for researchers who lack a Web development background. This article introduces jsPsych, a JavaScript library for the development of Web-based experiments. jsPsych formalizes a way of describing experiments that is much simpler than writing the entire experiment from scratch. jsPsych then executes these descriptions automatically, handling the flow from one task to another. The jsPsych library is open-source and designed to be expanded by the research community. The project is available online at


Online experiments JavaScript Amazon Mechanical Turk 


Author note

This material is based on work that was supported by a National Science Foundation Graduate Research Fellowship under Grant No. DGE-1342962. The author thanks Rob Goldstone, Nicholas de Leeuw, Rick Hullinger, and Peter Todd for feedback and suggestions on an earlier draft of this article, as well as the numerous people who have used jsPsych throughout the development process and have provided valuable feedback.


  1. Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6, 3–5. doi: 10.1177/1745691610393980 CrossRefGoogle Scholar
  2. Crump, M. J. C., McDonnell, J. V., & Gureckis, T. M. (2013). Evaluating Amazon’s Mechanical Turk as a tool for experimental behavioral research. PloS ONE, 8, e51382. doi: 10.1371/journal.pone.0057410 CrossRefGoogle Scholar
  3. Eriksen, B., & Eriksen, C. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics, 16, 143–149. doi: 10.3758/BF03203267 CrossRefGoogle Scholar
  4. Fox, E. (1995). Negative priming from ignored distractors in visual selection: A review. Psychonomic Bulletin & Review, 2, 129–139. doi: 10.3758/BF03210958 CrossRefGoogle Scholar
  5. Goldstone, R., Rogosky, B., Pevtzow, R., & Blair, M. (2005). Perceptual and semantic reorganization during category learning. In H. Cohen & C. Lefebvre (Eds.), Handbook of categorization in cognitive science (pp. 651–678). Amsterdam: Elsevier.CrossRefGoogle Scholar
  6. Goodman, J. K., Cryder, C. E., & Cheema, A. (2013). Data collection in a flat world: The strengths and weaknesses of Mechanical Turk samples. Journal of Behavioral Decision Making, 26, 213–224. doi: 10.1002/bdm.1753 CrossRefGoogle Scholar
  7. Kopp, B., Mattler, U., & Rist, F. (1994). Selective attention and response competition in schizophrenic patients. Psychiatry Research, 53, 129–139.CrossRefPubMedGoogle Scholar
  8. Mason, W., & Suri, S. (2012). Conducting behavioral research on Amazon’s Mechanical Turk. Behavior Research Methods, 44, 1–23. doi: 10.3758/s13428-011-0124-6 CrossRefPubMedGoogle Scholar
  9. McDonnell, J. V., Martin, J. B., Markant, D. B., Coenen, A., Rich, A. S., & Gureckis, T. M. (2012). psiTurk (Version 1.02) [Software]. New York, NY: New York University. Available from
  10. Palmer, S. E. (1977). Hierarchical structure in perceptual representation. Cognitive Psychology, 9, 441–474. doi: 10.1016/0010-0285(77)90016-0 CrossRefGoogle Scholar
  11. Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on Amazon Mechanical Turk. Judment and Decision Making, 5, 411–419.Google Scholar
  12. Reips, U.-D., & Neuhaus, C. (2002). WEXTOR: A Web-based tool for generating and visualizing experimental designs and procedures. Behavior Research Methods, Instruments, & Computers, 34, 234–240. doi: 10.3758/BF03195449 CrossRefGoogle Scholar
  13. Ross, J., Irani, L., Silberman, M. S., Zaldivar, A., & Tomlinson, B. (2010). Who are the turkers? Worker demographics in Amazon Mechanical Turk. In CHI ’10: CHI Conference on Human Factors in Computing Systems (pp. 2863–2872). New York: ACM.Google Scholar
  14. Zwaan, R. A., & Pecher, D. (2012). Revisiting mental simulation in language comprehension: six replication attempts. PloS ONE, 7, e51382. doi: 10.1371/journal.pone.0051382 CrossRefPubMedCentralPubMedGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2014

Authors and Affiliations

  1. 1.Department of Psychological & Brain Science, Cognitive Science ProgramIndiana UniversityBloomingtonUSA

Personalised recommendations