  People can refer to quantities in a visual scene by using either exact
cardinals (e.g. one, two, three) or natural language quantifiers (e.g. few,
most, all). In humans, these two processes underlie fairly different cognitive
and neural mechanisms. Inspired by this evidence, the present study proposes
two models for learning the objective meaning of cardinals and quantifiers from
visual scenes containing multiple objects. We show that a model capitalizing on
a 'fuzzy' measure of similarity is effective for learning quantifiers, whereas
the learning of exact cardinals is better accomplished when information about
number is provided.
