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Abstract:
Abstract: In traditional theories of semantic memory,
performance of semantic tasks relies upon a mediating process of
categorization. However, categorization-based theories do not
capture the complex and flexible ways in which people use their
conceptual knowledge to perform natural semantic tasks imposed on
them by the environment. For example, both children and adults
understand that a given property may be important for categorizing
some kinds of objects, but not others; that different kinds of
properties generalize across different groups of objects; and that
insides can be more important for determining category membership
than outsides. Consequently, some researchers propose to describe
conceptual knowledge in terms of naive theories about causal
mechanisms. In the current work, we present simulations using a
simple connectionist network that learns the mappings between
objects and their properties in different contexts. We show that
the evolution of representations throughout learning in our model
constrains the ease with which particular object properties can be
learned, and how they will generalize. The configuration of weights
at any point during development may provide the kinds of `enabling
constraints' on acquisition that some researchers attribute to
naive theories. Many of the phenomena that arise in the
theory-theory tradition may be understood within this framework.
Knowledge about how object properties vary across contexts is
stored in connection weights that are learned from experience. This
knowledge plays the role that naive theories play in the
theory-theory framework.
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