We demonstrate that distributed vector representations are capable
of hierarchical reasoning by summing sets of vectors representing
hyponyms (subordinate concepts) to yield a vector
that resembles the associated hypernym (superordinate concept).
These distributed vector representations constitute a potentially
neurally plausible model while demonstrating a high
level of performance in many different cognitive tasks. Experiments
were run using DVRS, a word embedding system
designed for the Sigma cognitive architecture, and Word2Vec,
a state-of-the-art word embedding system. These results contribute
to a growing body of work demonstrating the various
tasks on which distributed vector representations perform competently.