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 learn ing the objective meaning of cardinals and quantifiers from visual scenes containing mul tiple objects. We show that a model capitaliz ing on a 'fuzzy' measure of similarity is effec tive for learning quantifiers, whereas the learn ing of exact cardinals is better accomplished when information about number is provided.
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