10.2 The Motor Control Hierarchy

    I propose a motor control hierarchy which is closely analogous to the perceptual hierarchy, but
works in the opposite direction. In the motor control hierarchy, the lower levels deal directly
with muscle movements, with bodily functions; whereas the higher levels deal with patterns in
bodily movements, with schemes for arranging bodily movements. This much is similar to the
perceptual hierarchy. But in the motor control hierarchy, the primary function of a processor on
level n is to instruct processors on level n-1 as to what they should do next. The most crucial
information transmission is top-down. Bottom-up information transmission is highly simplistic:
it is of the form "I can do what you told me to do with estimated effectiveness E".
    Let us be more precise. When we say a processor on the n’th level tells a processor on the n-
1’th level what to do, we mean it gives it a certain goal and tells it to fulfill it. That is, we mean:
it poses it a certain optimization problem. It tells it: do something which produces a result as
near to this goal as possible. The processor on the n-1’th level must then implement some
scheme for solving this problem, for approximating the desired goal. And its scheme will, in
general, involve giving instructions to certain n-2’nd level processors. The important point is that
each level need know nothing about the operation of processors 2 or 3 levels down from it. Each
processor supplies its subordinates with ends, and the subordinates must conceive their own
means. As with the perceptual hierarchy, consciousness plays a role only on certain relatively
high levels. So, from the point of view of consciousness, the motor control hierarchy has no
definite end. But, from the point of view of external reality, there is an indisputable bottom level:
physical actions. The lowest level of the motor control hierarchy therefore has no subordinates
except for physical, nonintelligent systems. It must therefore prescribe means, not merely ends.
    Now, where do these "schemes" for optimization come from? Some are certainly
preprogrammed — e.g. a human infant appears to have an inborn"sucking reflex". But — as
observed above — even a cursory examination of motor development indicates that a great deal
of learning is involved.
    Let us assume that each processor is not entirely free to compute any function within its
capacity; that it has some sort of general "algorithm scheme", which may be made more precise
by the specification of certain "parameter values". Then there is first of all the problem of
parameter adaptation: given an optimization problem and a method of solution which contains a
number of parameter values, which parameter values are best? In order to approximately solve
this problem according to the scheme given above, all that is required is an estimate of how
"effective" each parameter value tends to be. In the motor control hierarchy, a processor on level
n must obtain this estimate from the processors on level n-1 which it has instructed. The
subordinate processors must tell their controlling processor how well they have achieved their
goal. The effectiveness with which they have achieved their goal is a rough indication of how
effective the parameter values involved are for that particular problem.
   So, on every level but the lowest, each processor in the hierarchy tells certain subordinate
lower-level processors what to do. If they can do it well, they do it and are not modified. But if
then cannot do their assigned tasks well, they are experimentally modified until they can do a
satisfactory job. The only loose end here is the nature of this experimental modification.
Parameter adaptation is only part of the story.
   Knowing how effective each vector of parameter values is for each particular problem is
useful, but not adequate for general motor control. After all, what happens when some new
action is required, some action for which optimal parameter values have not already been
estimated? It would be highly inefficient to begin the parameter optimization algorithm from
some random set of values. Rather, some sort of educated guess is in order. This means
something very similar to analogical reasoning is required. Presented with a new task, a motor
control processor must ask: what parameter values have worked for similar tasks?
   So, each motor control processor must first of all have access to the structurally associative
memory, from which it can obtain information as to which tasks are similar to which tasks. And
it must also have access to a memory bank storing estimates of optimal parameter values for
given tasks. In this way it can select appropriate schemes for regulating action.
   Based on the biological facts reviewed above, it is clear that this aspect of motor control is
native to the motor cortex rather than the cerebellum. To learn a complex action, the brain must
invoke the greater plasticity of the cortex.
   Introspectively speaking, all this is little more than common sense. To figure out how to throw
a certain object, we start out with the motions familiar to us from throwing similar objects. Then,
partly consciously but mainly unconsciously, we modify the "parameters" of the motions: we
change the speed of our hand or the angle at which the object is tilted. Based on trial-and-error
experimentation with various parameters, guided by intuition, we arrive at an optimal, or at least
adequate, set of motions.
   This process may be simple or sophisticated. For instance, when first throwing a frisbee with a
hole in the middle, one throws it as if it were an ordinary frisbee; but then one learns the subtle
differences. In this case the major problem is fine-tuning the parameters. But when learning to
throw a shot-put, or a football, the only useful item to be obtained from memory is the general
scheme of "throwing" — all the rest must be determined by conscious thought or, primarily,
   And when learning to juggle, or when learning to throw for the first time, the mind must
synthesize whole new patterns of timing and coordination: there is not even any "scheme" which
can be applied. Fragments of known programs must be pieced together and augmented to form a
new program, which then must be fine-tuned.
   More and more difficult tasks require higher and higher levels of the motor control hierarchy –
– both for learning and for execution. Even the very low levels of the motor control hierarchy are
often connected to the perceptual hierarchy; but the higher levels involve a great deal of
interaction with yet other parts of the mind.
Kaynak: A New Mathematical Model of Mind

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