Human reasoning deviates from classical logic. Psychological findings demonstrate that human reasoning is nonmonotonic, i.e., new information can lead to the retraction of previous inferences, it is defeasible reasoning. It is relevant whenever no contrary information is known (defeasible reasoning), when a most likely explanation is sought (abductive reasoning), when we need to revise our initial beliefs (belief revision), or to model human `commonsense reasoning' a topic highly relevant in AI research. While analysis of population data has identified nonmonotonic features, it is an open question, if systems that capture nonmonotonic reasoning better captures individual human reasoning. In this article, we take three prominent nonmonontonic approaches, the Weak Completion Semantics, Reiters Default Logic, and OCF, a ranking on possible worlds, implement variants of them and evaluate them within the CCOBRA-framework for their predictive capability in the Suppression Task. We demonstrate that both systems achieve a high performance being able to predict on average 82% of the inference drawn by an individual reasoner. Furthermore, we can demonstrate that OCF and an improved version of Reiter make identical predictions and that abduction is relevant on the level of an individual reasoner. We discuss implications of logical systems for human reasoning.