  Humans can generate reasonable answers to novel queries (Schulz, 2012): if I
asked you what kind of food you want to eat for lunch, you would respond with a
food, not a time. The thought that one would respond "After 4pm" to "What would
you like to eat" is either a joke or a mistake, and seriously entertaining it
as a lunch option would likely never happen in the first place. While
understanding how people come up with new ideas, thoughts, explanations, and
hypotheses that obey the basic constraints of a novel search space is of
central importance to cognitive science, there is no agreed-on formal model for
this kind of reasoning. We propose that a core component of any such reasoning
system is a type theory: a formal imposition of structure on the kinds of
computations an agent can perform, and how they're performed. We motivate this
proposal with three empirical observations: adaptive constraints on learning
and inference (i.e. generating reasonable hypotheses), how people draw
distinctions between improbability and impossibility, and people's ability to
reason about things at varying levels of abstraction.

Artificial Intelligence