Formal semantics for perceptual classification

摘要

A formal semantics for low-level perceptual aspects of meaning is presented, tying these together with the logical-inferential aspects of meaning traditionally studied in formal semantics. The key idea is to model perceptual meanings as classifiers of perceptual input. Furthermore, we show how perceptual aspects of meaning can be updated as a result of observing language use in interaction, thereby enabling fine-grained semantic plasticity and semantic coordination. This requires a framework where intensions are (i) represented independently of extensions, and (ii) structured objects which can be modified as a result of learning. We use Type Theory with Records (TTR), a formal semantics framework that starts from the idea that information and meaning is founded on our ability to perceive and classify the world, i.e. to perceive objects and situations as being of types. As an example of our approach, we show how a simple classifier of spatial information based on the Perceptron can be cast in TTR.