Implementing a concept network model.

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 feature-based 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 within-concept statistics that reflect how properties relate to one another across instances of a concept. We extracted formal measures of these networks that capture different aspects of network structure, and explored whether a concept's network structure relates to its flexibility of use. To do so, we compared network measures to a text-based measure of semantic diversity, as well as to empirical data from a figurative-language task and an alternative-uses task. We found that network-based measures were predictive of the text-based and empirical measures of flexible concept use, highlighting the ability of this approach to formally capture relevant characteristics of conceptual structure. Conceptual flexibility is a fundamental attribute of the cognitive and semantic systems, and in this proof of concept we reveal that variations in concept representation and use can be formally understood in terms of the informational content and topology of concept networks.