Information Technology Reference
In-Depth Information
examples of putatively homeostatic adaptation. Here are a couple of the more
horrible of them (Ashby 1952, 117-18):
Over thirty years ago, Marina severed the attachments of the internal and ex-
ternal recti muscles of a monkey's eyeball and re-attached them in crossed posi-
tion so that a contraction of the external rectus would cause the eyeball to turn
not outwards but inwards. When the wound had healed, he was surprised to
discover that the two eyeballs still moved together, so that binocular vision was
preserved.
More recently Sperry severed the nerves supplying the flexor and extensor
muscles in the arm of the spider monkey, and re-joined them in crossed posi-
tion. After the nerves had regenerated, the animal's arm movements were at
first grossly inco-ordinated, but improved until an essentially normal mode of
progression was re-established.
And, of course, as Ashby pointed out, the homeostat showed just this sort of
adaptive behavior. The commutators,
X
, precisely reverse the polarities of the
homeostat's currents, and a uniselector-controlled homeostat can cope with
such reversals by reconfiguring itself until it returns to equilibrium. A very
similar example concerns rats placed in an electrified box: after some random
leaping about, they learn to put their foot on a pedal which stops the shocks
(1952, 106-8). Quite clearly, the brain being modelled by the homeostat here
is not the cognitive brain of AI; it is the performative brain, the Ur-referent
of cybernetics: “excitations in the motor cortex [which] certainly control
the rat's bodily movements” (1952, 107). In the second edition of
Design for a
Brain
, Ashby added some less brutal examples of training animals to perform
in specified ways, culminating with a discussion of training a “house-dog” not
to jump on chairs (1960, 113): “Suppose then that jumping into a chair always
results in the dog's sensory receptors being excessively stimulated [by physical
punishment, which drives some essential variable beyond its limits]. As an
ultrastable system, step-function values which lead to jumps into chairs will
be followed by stimulations likely to cause them to change value. But on the
occurrence of a set of step-function values leading to a remaining on the
ground, excessive stimulation will not occur, and the values will remain.” He
then goes on to show that similar training by punishment can be demonstrated
on the homeostat. He discusses a set up in which just three units were con-
nected with inputs running 1→2→3→1, where the trainer, Ashby, insisted
that an equilibrium should be reached in which a small forced movement of
the needle on 1 was met by the opposite movement of the needle on 2. If the
