Compositional Distributional Cognition
This work addresses theoretical challenges in cognitive modeling for researchers in AI and cognitive science, though it appears incremental as it combines existing frameworks.
The paper tackles the problem of unbounded representation spaces and incomparable sentence structures in the Integrated Connectionist/Symbolic Architecture (ICS) by integrating it with categorical compositional semantics (CatCo) to form a model called categorical compositional cognition (CatCog). This resolves intrinsic issues in ICS and leverages CatCo tools for representing ambiguity, logical reasoning, and structural meanings.
We accommodate the Integrated Connectionist/Symbolic Architecture (ICS) of [32] within the categorical compositional semantics (CatCo) of [13], forming a model of categorical compositional cognition (CatCog). This resolves intrinsic problems with ICS such as the fact that representations inhabit an unbounded space and that sentences with differing tree structures cannot be directly compared. We do so in a way that makes the most of the grammatical structure available, in contrast to strategies like circular convolution. Using the CatCo model also allows us to make use of tools developed for CatCo such as the representation of ambiguity and logical reasoning via density matrices, structural meanings for words such as relative pronouns, and addressing over- and under-extension, all of which are present in cognitive processes. Moreover the CatCog framework is sufficiently flexible to allow for entirely different representations of meaning, such as conceptual spaces. Interestingly, since the CatCo model was largely inspired by categorical quantum mechanics, so is CatCog.