Chapter 2. Prehension

92 THE PHASES OF PREHENSION
A Pfingels sfingels B
Ifin 4 fingas
Task Cylinder
Difficulty Length
LM M-R I-M-R
Figure 4.11 Networks modelling virtual to real finger mapping.
A. Using task difficulty and cylinder length as inputs, the network
computes the size of virtual finger two. B. Using task difficulty,
cylinder length, hand length, hand width, and movement amplitude
as inputs, the network selects which real fingers constitute virtual
finger two. hindex finger, M=middle finger, R=ring finger,
L=little finger (from Iberall, Preti, & Zemke, 1989).
across all subjects took 1033 cycles to converge (total error .003).
Using a different network (as in Figure 4.1 la) with four outputs, each
one telling the liklihood of a virtual finger size, took 2072 cycles to
converge (total error .004). The training set used percentages across
all subjects. A network with seven outputs, one for each combination,
and percentages across all subjects, took 3000 cycles to converge
(total error .OOl).
However, the goal is to model one person’s brain, not the average
brain. When Iberall et al. tried the same network using one subject’s
data, the network could not compute a solution. This is because sub-
jects used different combinations for each condition. Even though a
subject typically used the middle finger in opposition to the thumb, for
a few of the trials he or she would use the index in addition to the
middle finger, and then revert back to the standard grasp. In order to
more closely model the person, more inputs were added, such as fea-
tures of that individual’s anatomy and more about the task, as seen in
Figure 4.1 lb. The network still didn’t converge. Likely, more dy-
namic inputs are needed, such as level of muscle fatigue.
Adaptive artificial neural networks can be trained using data from
experimental evidence, and then tested on other data. Converging on a
solution means that a solution has to exist. However, this depends