There are several established collections of cognition tests used to test aspects of intelligence in human and animals (e.g., crows, monkeys), such as the Primate Cognition Test Battery. These experiments help determine, for example, an agent's ability to understand object permanence and causality. Although these experiments are very common in cognitive science, they have not been deployed on and applied to machine learning models and algorithms at scale. This is mostly because, so far, these cognitive science experiments were undertaken manually, with an experimenter having to physically move objects in front of the test agent while recording the interaction as a data point. The goal of the Simulated Cognitive Experiment Test Suite is to allow these experiments to be deployed for machine learning research, and at scale. Then these experiments are recreated in 3D in a randomized and programmatic fashion to allow for the creation of rich, large datasets.