2kk
MacKAY
Engineering details do not here concern us, but in the next
section we shall consider briefly how such an automaton could function.
5 . A Statistical Abstractive Mechanism
Automata have before been described, for example by Ashby [5],
in which the elements are interconnected by deterministic links, but the
error-signal functions by stimulating trial-and-error activity among relay-
switches altering the interconnections.
The mechanism we shall here consider is of a more general type.
We could describe it loosely as one in which the interconnections are
not strictly deterministic, but have a variable probability of functioning.
More precisely, it is a mechanism in which the probability of excitation
of each element (in a given time interval) can be made to depend on
continuously-variable physical factors as well as on the current states of
any number of other elements linked to it [2, 6, 7, and 8].
In the limiting case an element may be spontaneously active,
with a frequency depending in like manner on the current state of its
(topological) environment.
In an automaton of the general type of Figure 3 constructed of
such elements, adaptive activity can be guided by modifying the relative
probabilities of excitation. Trial-and-error normally takes place spon
taneously, and the error-signal functions by controlling the statistical
structure of the trial process.
The term "threshold” is used to denote, roughly speaking, the
resistance of an element to stimulation. In general each connecting link
to one element from another can have its own particular threshold, deter
mining the extent to which a signal in that link affects the probability
of excitation. The higher the threshold the lower the probability, or the
longer the interval of time that must elapse (with a given configuration
of stimuli) before excitation.
Error-stimulated adjustment of thresholds, then, or evaluatory
threshold-control for short, is the key notion on which we shall base our
statistical abstractive mechanism.
Assuming that we have elements in which the thresholds may be at least
semi-permanently modified according to the success or failure of current
trials, we can envisage a system initially devoid of instruction evolving
for itself a satisfactory pattern of adaptive activity.
To see how such statistically negative feedback can function, the
simple illustration provided by the model of Figure 4 may be helpful. It
was originally constructed to illustrate some points made in Reference [6],
and has of course no pretensions to animal status.