Similarity spaces are standardly constructed by collecting pairwise similarity judgments and subjecting those to a dimension-reduction technique such as multidimensional scaling or principal component analysis. While this approach can be effective, it has some known downsides, most notably, it tends to be costly and has limited generalizability. Recently, a number of authors have attempted to mitigate these issues through machine learning techniques. For instance, neural networks have been trained on human similarity judgments to infer the spatial representation of unseen stimuli. (...) However, these newer methods are still costly and fail to generalize widely beyond their initial training sets. This paper proposes leveraging prebuilt semantic vector spaces as a cheap alternative to collecting similarity judgments. Our results suggest that some of those spaces can be used to approximate human similarity judgments at low cost and high speed. (shrink)

We argue that concepts from the social sciences can be as natural as those from physics and chemistry, thereby answering in the positive the question of whether social metaphysics is or can be substantive. The argument takes as a starting point Douven & Gärdenfors’ (Mind & Language, 35, 313–334 2020) optimality account of natural concepts, according to which natural concepts are represented by the cells of an optimally partitioned similarity space. While the account applies straightforwardly to perceptual concepts, it does (...) not apply as straightforwardly to more abstract concepts, such as many scientific concepts. We propose an extension of Douven and Gärdenfors’ optimality account that pertains to scientific concepts as well. The proposal reuses ideas from Lewis’ Best Systems Account of laws of nature, but rather than invoking natural concepts (as Lewis does) defines natural scientific concepts as those that figure in the or a best systematization of the totality of facts. On this proposal, there is nothing especially different between concepts from the natural sciences and those from the social sciences. (shrink)