The nature and function of content in computational models

Abstract

Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. This chapter sketches an account of the nature and function of representation in computational cognitive models. David Marr's theory of early vision purports to explain edge detection, in part, by positing the computation of the Laplacean of a Gaussian of the retinal array. The mechanism takes as input intensity values at points in the image and calculates the rate of intensity change over the image. The chapter also sketches an alternative picture of the nature and function of representational content in computational theorizing – what is called as a deflationary account of content. It concludes by considering the deflationary account of content in light of the adequacy conditions for a theory of content for computational neuroscience.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 57 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.