  In recent years, trends towards studying simulated games have gained momentum
in the fields of artificial intelligence, cognitive science, psychology, and
neuroscience. The intersections of these fields have also grown recently, as
researchers increasing study such games using both artificial agents and human
or animal subjects. However, implementing games can be a time-consuming
endeavor and may require a researcher to grapple with complex codebases that
are not easily customized. Furthermore, interdisciplinary researchers studying
some combination of artificial intelligence, human psychology, and animal
neurophysiology face additional challenges, because existing platforms are
designed for only one of these domains. Here we introduce Modular
Object-Oriented Games, a Python task framework that is lightweight, flexible,
customizable, and designed for use by machine learning, psychology, and
neurophysiology researchers.
