Mind, Body, World- Foundations of Cognitive Science, 2013a

8.5 Vision, Cognition, and Visual Cognition
It was argued earlier that the classical approach to underdetermination, unconscious
inference, suffered from the fact that it did not include any causal links
between the world and internal representations. The natural computation approach
does not suffer from this problem, because its theories treat vision as a data-driven
or bottom-up process. That is, visual information from the world comes into contact
with visual modules—special purpose machines—that automatically apply natural
constraints and deliver uniquely determined representations. How complex are the
representations that can be delivered by data-driven processing? To what extent
could a pure bottom-up theory of perception succeed?
On the one hand, the bottom-up theories are capable of delivering a variety
of rich representations of the visual world (Marr, 1982). These include the primal
sketch, which represents the proximal stimulus as an array of visual primitives,
such as oriented bars, edges, and terminators (Marr, 1976). Another is the
2½-D sketch, which makes explicit the properties of visible surfaces in viewercentred
coordinates, including their depth, colour, texture, and orientation
(Marr & Nishihara, 1978). The information made explicit in the 2½-D sketch is
available because data-driven processes can solve a number of problems of underdetermination,
often called “shape from” problems, by using natural constraints
to determine three-dimensional shapes and distances of visible elements. These
include structure from motion (Hildreth, 1983; Horn & Schunk, 1981; Ullman, 1979;
Vidal & Hartley, 2008), shape from shading (Horn & Brooks, 1989), depth from binocular
disparity (Marr, Palm, & Poggio, 1978; Marr & Poggio, 1979), and shape from
texture (Lobay & Forsyth, 2006; Witkin, 1981).
It would not be a great exaggeration to say that early vision—part of visual processing
that is prior to access to general knowledge—computes just about everything
that might be called a ‘visual appearance’ of the world except the identities and
names of the objects. (Pylyshyn, 2003b, p. 51)
On the other hand, despite impressive attempts (Biederman, 1987), it is generally
acknowledged that the processes proposed by natural computationalists cannot
deliver representations rich enough to make full contact with semantic knowledge
of the world. This is because object recognition—assigning visual information to
semantic categories—requires identifying object parts and determining spatial relationships
amongst these parts (Hoffman & Singh, 1997; Singh & Hoffman, 1997).
However, this in turn requires directing attention to specific entities in visual representations
(i.e., individuating the critical parts) and using serial processes to
determine spatial relations amongst the individuated entities (Pylyshyn, 1999, 200
1, 2003c, 2007; Ullman, 1984). The data-driven, parallel computations that characterize
natural computation theories of vision are poor candidates for computing
Seeing and Visualizing 379