Abstract

This paper presents a nine step argument for “Connectionist Structuralism” (CS), a physical nominalist position that takes seriously the non-physical phenomenology of abstract objects. CS provides an ontology of sensible properties as a subtype of abstract objects, e.g. “is red” or “is a rectangle”. CS proposes that each sensible property a person draws on corresponds to a subset of their brain structure with functionality isomorphic to a suitable connectionist network. While a common assumption in parts of the artificial intelligence literature, such discussions have not developed formal accounts nor engaged with metaphysical issues that potentially undermine it. Using evidence from cognitive neuroscience, machine learning, and evolutionary biology, as well as a fully worked toy example, we describe how CS can support our core cognitive uses of sensible properties and account for our core phenomenal experiences of them. Six phenomenal features are accounted for—feeling intangible, non-located, transparent, and unchanging, along with raw sensation and ante rem phenomenologies—with potential future accounts for further phenomena such as synaesthesia, semantic clarity, and sensory overload. A response is provided to three common objections to nominalist positions that similarly reject intangible, universal benchmarks for sensible properties: referential opacity; identity of indiscernibles; and infinite regression. CS leads to a four-layer hierarchy of similarity for whether your “red” is the same as mine, arguing they are likely non-identical but can be made close enough for practical purposes. Finally, we describe future work to elaborate CS as a metaphysical project and test it through empirical research.