Gorilla in our midst: An online behavioral experiment builder

Abstract

Behavioral researchers are increasingly conducting their studies online, to gain access to large and diverse samples that would be difficult to get in a laboratory environment. However, there are technical access barriers to building experiments online, and web browsers can present problems for consistent timing-an important issue with reaction-time-sensitive measures. For example, to ensure accuracy and test-retest reliability in presentation and response recording, experimenters need a working knowledge of programming languages such as JavaScript. We review some of the previous and current tools for online behavioral research, as well as how well they address the issues of usability and timing. We then present the Gorilla Experiment Builder (gorilla.sc), a fully tooled experiment authoring and deployment platform, designed to resolve many timing issues and make reliable online experimentation open and accessible to a wider range of technical abilities. To demonstrate the platform's aptitude for accessible, reliable, and scalable research, we administered a task with a range of participant groups (primary school children and adults), settings (without supervision, at home, and under supervision, in both schools and public engagement events), equipment (participant's own computer, computer supplied by the researcher), and connection types (personal internet connection, mobile phone 3G/4G). We used a simplified flanker task taken from the attentional network task (Rueda, Posner, & Rothbart, 2004). We replicated the "conflict network" effect in all these populations, demonstrating the platform's capability to run reaction-time-sensitive experiments. Unresolved limitations of running experiments online are then discussed, along with potential solutions and some future features of the platform.

Keywords: Attentional control; Browser timing; Online methods; Online research; Remote testing; Timing accuracy.

Fig. 1

Example of the two main GUI elements of Gorilla. (A) The Task Builder, with a screen selected showing how a trial is laid out. (B) The Experiment Builder, showing a check for the participant, followed by a randomizer node that allocates the participant to one of two conditions, before sending them to a Finish node

Fig. 2

Trial types for Experiment 1: Different conditions used in the flanker task

Fig. 3

Time course of a typical trial in Experiment 1. These screens represent what the participant was seeing within the web browser

Fig. 4

Distribution of accuracy differences between congruent and incongruent trials, for each group in Experiment 1. Group A was children in school in Corsica, France; Group B consisted of children in schools in London, UK; and Group C consisted of children attending a university public engagement event in London

Fig. 5

Distribution of RT differences between congruent and incongruent trials for each group in Experiment 1

Fig. 6

Trial types for Experiment 2: Different conditions used in the flanker task

Fig. 7

Time course of a typical trial in Experiment 2. These screens represent what the participant was seeing within the web browser