OpenSesame: an open-source, graphical experiment builder for the social sciences

Abstract

In the present article, we introduce OpenSesame, a graphical experiment builder for the social sciences. OpenSesame is free, open-source, and cross-platform. It features a comprehensive and intuitive graphical user interface and supports Python scripting for complex tasks. Additional functionality, such as support for eyetrackers, input devices, and video playback, is available through plug-ins. OpenSesame can be used in combination with existing software for creating experiments.

Fig. 1

The OpenSesame graphical user interface on start-up. a The item toolbar contains icons that can be dragged into the overview area. b The overview area represents the experiment as a tree-structure. c The tab area contains tabs for editing items and getting context-sensitive help. By clicking on an item in the overview area, a tab with the appropriate controls opens. By clicking on one of the blue “help” buttons, a context-sensitive help tab opens. d The structure of an example experiment shown in the overview area

Fig. 2

Using variables and conditional (“if”) statements. a Independent variables can be defined in a loop item. b By entering [SOA] in the duration field of a sketchpad item, the variable SOA is used to control the presentation duration of the sketchpad. This assumes that SOA has been defined elsewhere in the experiment. c By using [avg_rt] and [acc] as part of the text in a feedback item, appropriate feedback can be given to the user. The variables avg_rt and acc are set automatically by the various response items (e.g., keyboard_response). d Conditional statements can be used to control which items from a sequence will be called. By entering [correct] = 1 in the “Run if…” field of “green_fixation,” the item will be called only if the variable correct has been set to 1. The variable correct is set automatically by the various response items

Fig. 3

The file pool. All files that are used in the experiment, such as images, sounds, and videos, are stored in the file pool. The file pool is saved along with the experiment, allowing for maximum portability

Fig. 4

Results of the benchmark experiment. The automated response times are shown as a Tukey box plot conform Robbins (2004). The central line reflects the median value. The rectangle shows the interquartile range (the 25th percentile to the 75th percentile). The whiskers reflect the minimum and maximum values that fall within 1.5 times the interquartile range. Dots correspond to individual observations that fall outside of 1.5 times the interquartile range