Chapter 2. Prehension

94 THE PHASES OF PREHENSION
forces, precision requirements). The second set was used to deter-
mine the mapping for VF1 for each chosen opposition. As a simplify-
ing assumption, this was fixed (i.e., the palm is VF1 in palm opposi-
tion, the thumb is VF1 in pad and side oppositions). The third set
determined the mappings for VF2. This depended both on the type of
opposition and on object and task characteristics. For example, VF2
could not be wider than the length of the object at the grasp site loca-
tion. The advantage to such an encoding of postures is that it provides a
set of parameters that are potentially being controlled in human pre-
hension. Further, these parameters can be quantified experimentally,
used in computational models and implemented in artificial hands.
4.5.3 Selecting hand opening
We discussed earlier that humans are quite accurate at matching the
distance between two opposable surfaces of an object with an aperture
between the thumb and index finger, whether the two opposable
surfaces are assessed visually (Jeannerod and Decety, 1990) or
haptically, by holding the object in the other hand (Chan, Carello, and
Turvey, 1990). These experiments demonstrate that the CNS can plan
a hand opening to match a perceived object. Further, in the context of
reaching and grasping, Marteniuk et al. (1990) found that peak
aperture increased by 0.77 cm for every 1 cm increase in the diameter
of the cylinders, with a correlation of .99. Aperture evolution is
continuous, and reveals that even at onset of hand opening, aperture
size increases with object size. Thus experimental evidence suggests
that the hand opening appropriate for the magnitude of the opposition
vector is selected prior to movement onset.
4.6 Planning a Hand Location and Orientation
Once the object’s location and orientation are perceived, arm
movements can be planned that will put the hand into a suitable loca-
tion and orientation. One way to view planning is the process of
transforming information from a visual reference frame to arm muscle
activity. Computations such as transforming a desired goal into motor
commands lend themselves to modelling with artificial neural network
models of CNS activity, as is seen in this section.