Title: Implementing a Concept Network Model,
Abstract: The same concept can mean different things or be instantiated in different
forms depending on context, suggesting a degree of flexibility within the
conceptual system. We propose that a compositional network model can be used to
capture and predict this flexibility. We modeled individual concepts (e.g.,
BANANA, BOTTLE) as graph-theoretical networks, in which properties (e.g.,
YELLOW, SWEET) were represented as nodes and their associations as edges. In
this framework, networks capture the within-concept statistics that reflect how
properties correlate with each other across instances of a concept. We ran a
classification analysis using graph eigendecomposition to validate these
models, and find that these models can successfully discriminate between object
concepts. We then computed formal measures from these concept networks and
explored their relationship to conceptual structure. We find that diversity
coefficients and core-periphery structure can be interpreted as network-based
measures of conceptual flexibility and stability, respectively. These results
support the feasibility of a concept network framework and highlight its
ability to formally capture important characteristics of the conceptual system.