  The last decade has seen huge progress in the development of advanced machine
learning models; however, those models are powerless unless human users can
interpret them. Here we show how the mind's construction of concepts and
meaning can be used to create more interpretable machine learning models. By
proposing a novel method of classifying concepts, in terms of 'form' and
'function', we elucidate the nature of meaning and offer proposals to improve
model understandability. As machine learning begins to permeate daily life,
interpretable models may serve as a bridge between domain-expert authors and
non-expert users.
