Architectural and Representational Requirements for Seeing Processes, Proto-affordances and Affordances

Abstract

This paper, combining the standpoints of philosophy and
Artificial Intelligence with theoretical psychology, summarises several
decades of investigation by the author of the variety of functions of vision
in humans and other animals, pointing out that biological evolution has
solved many more problems than are normally noticed. For example, the
biological functions of human and animal vision are closely related to the
ability of humans to do mathematics, including discovering and proving
theorems in geometry, topology and arithmetic. Many of the phenomena
discovered by psychologists and neuroscientists require sophisticated controlled
laboratory settings and specialised measuring equipment, whereas
the functions of vision reported here mostly require only careful attention
to a wide range of everyday competences that easily go unnoticed. Currently
available computer models and neural theories are very far from
explaining those functions, so progress in explaining how vision works
is more in need of new proposals for explanatory mechanisms than new
laboratory data. Systematically formulating the requirements for such
mechanisms is not easy. If we start by analysing familiar competences,
that can suggest new experiments to clarify precise forms of these competences,
how they develop within individuals, which other species have
them, and how performance varies according to conditions. This will
help to constrain requirements for models purporting to explain how the
competences work. For example, Gibson’s theory of affordances needs
a number of extensions, including allowing affordances to be composed
in several ways from lower level proto-affordances. The paper ends with
speculations regarding the need for new kinds of information-processing
machinery to account for the phenomena.