  Although neural models have performed impressively well on various tasks such
as image recognition and question answering, their reasoning ability has been
measured in only few studies. In this work, we focus on spatial reasoning and
explore the spatial understanding of neural models. First, we describe the
following two spatial reasoning IQ tests: rotation and shape composition. Using
well-defined rules, we constructed datasets that consist of various complexity
levels. We designed a variety of experiments in terms of generalization, and
evaluated six different baseline models on the newly generated datasets. We
provide an analysis of the results and factors that affect the generalization
abilities of models. Also, we analyze how neural models solve spatial reasoning
tests with visual aids. Our findings would provide valuable insights into
understanding a machine and the difference between a machine and human.
