Can race really be erased? A pre-registered replication study

Amazon's Mechanical Turk is an online labor market where requesters post jobs and workers choose which jobs to do for pay. The central purpose of this article is to demonstrate how to use this Web site for conducting behavioral research and to lower the barrier to entry for researchers who could benefit from this platform. We describe general techniques that apply to a variety of types of research and experiments across disciplines. We begin by discussing some of the advantages of doing experiments on Mechanical Turk, such as easy access to a large, stable, and diverse subject pool, the low cost of doing experiments, and faster iteration between developing theory and executing experiments. While other methods of conducting behavioral research may be comparable to or even better than Mechanical Turk on one or more of the axes outlined above, we will show that when taken as a whole Mechanical Turk can be a useful tool for many researchers. We will discuss how the behavior of workers compares with that of experts and laboratory subjects. Then we will illustrate the mechanics of putting a task on Mechanical Turk, including recruiting subjects, executing the task, and reviewing the work that was submitted. We also provide solutions to common problems that a researcher might face when executing their research on this platform, including techniques for conducting synchronous experiments, methods for ensuring high-quality work, how to keep data private, and how to maintain code security.

Previous studies have established that people encode the race of each individual they encounter, and do so via computational processes that appear to be both automatic and mandatory. If true, this conclusion would be important, because categorizing others by their race is a precondition for treating them differently according to race. Here we report experiments, using unobtrusive measures, showing that categorizing individuals by race is not inevitable, and supporting an alternative hypothesis: that encoding by race is instead a reversible byproduct of cognitive machinery that evolved to detect coalitional alliances. The results show that subjects encode coalitional affiliations as a normal part of person representation. More importantly, when cues of coalitional affiliation no longer track or correspond to race, subjects markedly reduce the extent to which they categorize others by race, and indeed may cease doing so entirely. Despite a lifetime's experience of race as a predictor of social alliance, less than 4 min of exposure to an alternate social world was enough to deflate the tendency to categorize by race. These results suggest that racism may be a volatile and eradicable construct that persists only so long as it is actively maintained through being linked to parallel systems of social alliance.