Computational Imagery

TLDR

Mental imagery, once neglected, is now a vibrant research area, and computational imagery is defined as the ability to represent, retrieve, and reason about spatial and visual information not explicitly stored in long‑term memory. The article proposes a concept of computational imagery and a knowledge‑representation scheme that integrates long‑term memory, descriptive representation, and two working‑memory representations for visual and spatial components. The scheme specifies three representations and primitive functions using a formal theory of arrays, implemented in the array‑based language Nial. The study emphasizes that while prior mental‑imagery research motivates the scheme, its success hinges on expressive power, inferential adequacy, and efficiency.

Abstract

After many years of neglect, the topic of mental imagery has recently emerged as an active area of research and debate in the cognitive science community. This article proposes a concept of computational imagery, which has potential applications to problems whose solutions by humans involve the use of mental imagery. Computational imagery can be defined as the ability to represent, retrieve, and reason about spatial and visual information not explicitly stored in long‐term memory. The article proposes a knowledge representation scheme for computational imagery that incorporates three representations: a long‐term memory, descriptive representation and two working‐memory representations, corresponding to the distinct visual and spatial components of mental imagery. The three representations, and a set of primitive functions, are specified using a formal theory of arrays and implemented in the array‐based language Nial. Although results of studies in mental imagery provide initial motivation for the representations and functionality of the scheme, our ultimate concerns are expressive power, inferential adequacy, and efficiency.