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date: 27 May 2020

Abstract and Keywords

This article proposes a neurobiologically motivated theory of meaning as internal representation that holds on to the principle of compositionality, but negates the principle of semantic constituency. The approach builds on neurobiological findings regarding topologically structured cortical feature maps and the mechanism of object-related binding by neuronal synchronization. It incorporates the Gestalt principles of psychology and is implemented by recurrent neural networks. The semantics to be developed is structurally analogous to some variant of model-theoretical semantics. The semantics to be developed is a neuro-emulative model-theoretical semantics of a first-order language. The model-theoretical semantics is merely denotational and does not imply anything about the structures of the mind or the underlying neural mechanisms that enable producing and comprehend meaningful expressions. The relation between a mental representation and its content is some form of causal-informational covariation.

Keywords: principle of compositionality, semantic constituency principle, neuronal synchronization, Gestalt principles, neural networks

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