  We introduce and implement a cognitively plausible model for learning from
generic language, statements that express generalizations about members of a
category and are an important aspect of concept development in language
acquisition (Carlson & Pelletier, 1995; Gelman, 2009). We extend a
computational framework designed to model grounded language acquisition by
introducing the concept network. This new layer of abstraction enables the
system to encode knowledge learned from generic statements and represent the
associations between concepts learned by the system. Through three tasks that
utilize the concept network, we demonstrate that our extensions to ADAM can
acquire generic information and provide an example of how ADAM can be used to
model language acquisition.
