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

Relaxation labelling was the leading edge of a broad perspective for understanding
vision. This was the natural computation approach to vision (Hildreth, 1983;
Marr, 1976, 1982; Marr & Hildreth, 1980; Marr & Nishihara, 1978; Marr, Palm, & Poggio,
1978; Marr & Poggio, 1979; Marr & Ullman, 1981; Richards, 1988; Ullman, 1979).
Researchers who endorse the natural computation approach to vision use naïve
realism to solve problems of underdetermination. They hypothesize that the visual
world is intrinsically structured, and that some of this structure is true of any
visual scene. They assume that a visual system that has evolved in such a structured
world is able to take advantage of these visual properties to solve problems of
underdetermination.
The properties of interest to natural computation researchers are called natural
constraints. A natural constraint is a property of the visual world that is almost
always true of any location in any scene. For example, a great many visual properties
of three-dimensional scenes (depth, texture, colour, shading, motion) vary
smoothly. This means that two locations very near one another in a scene are very
likely to have very similar values for any of these properties. Locations that are further
apart will not be as likely to have similar values for these properties.
Natural constraints can be used to solve visual problems of underdetermination
by imposing restrictions on scene interpretations. Natural constraints are properties
that must be true of an interpretation of a visual scene. They can therefore be used
to filter out interpretations consistent with the proximal stimulus but not consistent
with the natural constraint. For example, an interpretation of a scene that violated
the smoothness constraint, because its visual properties did not vary smoothly in the
sense described earlier, could be automatically rejected and never experienced.
The natural computation approach triumphed because it was able to identify
a number of different natural constraints for solving a variety of visual problems of
underdetermination (for many examples, see Marr, 1982). As in the scene labelling
approach described above, the use of natural constraints did not require scene-specific
knowledge. Natural computation researchers did not appeal to problem solving
or inference, in contrast to the knowledge-based models of an earlier generation
(Barrow & Tenenbaum, 1975; Tenenbaum & Barrow, 1977). This was because natural
constraints could be exploited using data-driven algorithms, such as neural networks.
For instance, one can exploit natural constraints for scene labelling by using
processing units to represent potential labels and by defining natural constraints
between labels using the connection weights between processors (Dawson, 1991).
The dynamics of the signals sent through this network will turn on the units for
labels consistent with the constraints and turn off all of the other units.
In the context of the current discussion of the cognitive sciences, the natural
computation approach to vision offers an interesting perspective on how a
useful synthesis of divergent perspectives is possible. This is because the natural
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