Moving Behavioral Experimentation Online: A Tutorial and Some Recommendations for Drift Diffusion Modeling

Abstract

Behavioral science demands skillful experimentation and high-quality data that are typically gathered in person. However, the COVID-19 pandemic forced many behavioral research laboratories to close. Thankfully, new tools for conducting online experiments allow researchers to elicit psychological responses and gather behavioral data with unprecedented precision. It is now possible to quickly conduct large-scale high-quality behavioral experiments online, even for studies designed to generate data necessary for complex computational models. However, these techniques require new skills that might be unfamiliar to behavioral researchers who are more familiar with laboratory-based experimentation. We present a detailed tutorial introducing an end-to-end build of an online experimental pipeline and corresponding data analysis. We provide an example study investigating people’s media preferences using drift-diffusion modeling (DDM), paying particular attention to potential issues that come with online behavioral experimentation. This tutorial includes sample data and code for conducting and analyzing DDM data gathered in an online experiment, thereby mitigating the extent to which researchers must reinvent the wheel.