Chapter 2. Prehension

88 THE PHASES OF PREHENSION
influence on the overall computation. The top row of squares
represents the weights coming from the hidden layer to the output
layer. The first two hidden units have a large influence, whereas the
other two hidden units have a moderate influence.
The point of using a neural network approach is to learn how it
generalizes. In analyzing how the network generalized the in-
pudoutput space, Iberall noted that it learned to use palm opposition
when the forces were large. With higher precision requirements in the
task, the network choose pad opposition. Also, there was a tendency
toward using palm opposition when the length of the object increased,
particularly when the forces were increasing as well. These results
mirror information observed in human prehension.
Uno et al.( 1993) developed a neural network for determining
optimal hand shapes. As seen in Figure 4.9, the network had five
layers. During the learning phase, the network sampled object shapes
and hand postures. During the optimization phase, the network was
visual
image
+ x
hand
shape * Y
visual
image
___)
hand
shape
Figure 4.9 Neural network for objects and hand postures. During
the learning phase, two dimensional visual images of objects and
DataGlove sensory data representing hand postures are presented to
the network. During the optimization phase, an optimal posture
is chosen based on a criterion function (from Uno et al., 1993;
reprinted by permission).