Over the past few decades, cognitive science has identified several forms of reasoning that make essential use of conceptual knowledge. Despite significant theoretical and empirical progress, there is still no unified framework for understanding how concepts are used in reasoning. This paper argues that the theory of conceptual spaces is capable of filling this gap. Our strategy is to demonstrate how various inference mechanisms which clearly rely on conceptual information—including similarity, typicality, and diagnosticity-based reasoning—can be modeled using principles derived from (...) conceptual spaces. Our first topic analyzes the role of expectations in inductive reasoning and their relation to the structure of our concepts. We examine the relationship between using generic expressions in natural language and common-sense reasoning as a second topic. We propose that the strength of a generic can be described by distances between properties and prototypes in conceptual spaces. Our third topic is category-based induction. We demonstrate that the theory of conceptual spaces can serve as a comprehensive model for this type of reasoning. The final topic is analogy. We review some proposals in this area, present a taxonomy of analogical relations, and show how to model them in terms of distances in conceptual spaces. We also briefly discuss the implications of the model for reasoning with concepts in artificial systems. (shrink)

This paper deals with Gärdenfors’ theory of conceptual spaces. Let \({\mathcal {S}}\) be a conceptual space consisting of 2-type fuzzy sets equipped with several kinds of metrics. Let a finite set of prototypes \(\tilde{P}_1,\ldots,\tilde{P}_n\in \mathcal {S}\) be given. Our main result is the construction of a classification algorithm. That is, given an element \({\tilde{A}}\in \mathcal {S},\) our algorithm classifies it into the conceptual field determined by one of the given prototypes \(\tilde{P}_i.\) The construction of our algorithm uses some physical analogies (...) and the Newton potential plays a significant role here. Importantly, the resulting conceptual fields are not convex in the Euclidean sense, which we believe is a reasonable departure from the assumptions of Gardenfors’ original definition of the conceptual space. A partitioning algorithm of the space \(\mathcal {S}\) is also considered in the paper. In the application section, we test our classification algorithm on real data and obtain very satisfactory results. Moreover, the example we consider is another argument against requiring convexity of conceptual fields. (shrink)

Recently, there has been much research into conceptual engineering in connection with feminist inquiry and activism, most notably involving gender issues, but also sexism and misogyny. Our paper contributes to this research by explicating, in a principled manner, a series of other concepts important for feminist research and activism, to wit, feminist political identity terms. More specifically, we show how the popular Conceptual Spaces Framework (CSF) can be used to identify and regiment concepts that are central to feminist research, focusing (...) especially on feminism in France. According to the CSF, concepts can be represented geometrically, as regions in similarity spaces. A particular strength of the CSF framework is its empirically-focused methodology, which allows researchers to infer the boundaries of concepts from empirical data, thus eliminating the need to strongly rely on intuitions about meanings. This is shown to be especially valuable for the explication of concepts relating to feminist political identity, given that the intuitions of feminist scholars and activists about what would appear to be core concepts in the area tend to be poorly aligned or even conflicting. We report the results from an empirical categorization study conducted among French feminists and show how they support the view that the CSF can contribute to both the conceptual engineering project and our understanding of the structure of social reality. (shrink)