Chapter 2. Prehension

200 THE PHASES OF PREHENSION
might be due to visual feedback correction during the second phase,
the second phase exists in conditions where vision is lacking, arguing
against the notion that the second phase is due to the need for visual
feedback corrections. Importantly, however, when visual information
is available (or possibly available), it will be used. With vision,
movements are longer. Finally, an important issue is the distinction
between the use of peripheral and central vision. According to Sivak
and MacKenzie (1992), when only peripheral vision is available, both
the grasping component and transport components are affected.
Movements are slower and location is more uncertain, when the
temporal link is broken between transport and grasping.
Two major goals seem to be at work, which in their own way in-
fluence both the arm and the hand. Firstly, perception of the location
of the object influences movement parameters; uncertainty in object lo-
cation dictates slowing down in the vicinity of the object, particularly
if the objective is not to bowl it over or crush it. This affects both the
transport component (change in velocity profile) and the grasping
component (open hand before contact is anticipated). Secondly, per-
ception of force-related object properties (e.g., weight, surface tex-
ture, surface sizes) and goals for task performance (e.g., direction and
type of motions to impart, forces to apply) affect the transport compo-
nent (kinematic and kinetic effects effects) and the grasping component
(posturing force generation muscles for the task).
Various models have been proposed for trajectory planning.
Bullock and Grossberg (1989) used an internal model of muscle
length in their VITE model and a time-varying GO signal to update the
model. Kawato et al. (1987) suggested a model for combining three
controllers: a slow cortical feedback controller, a faster cerebellar
feedback controller, and a even faster cerebellar feedforward
controller. Jordan (1988) designed a two tiered network for first
learning the forward kinematics of an arm and then learning sequences
of movements. Constraints were identified to smooth out the
movements and solve ill-posed problems. Massone and Bizzi (1989)
used the Jordan network to generate trajectories of muscle activations.
Instead of derived constraints, they minimized a potential-energy cost
function in order to generate unique solutions.
The CNS may be trying to get some parameters into the ‘right
ballpark’ which can then be fine tuned. Prehensile planning can be
performed by a network of ‘neuron-like’ processes working together
to compute a desired posture and position and orientation from
estimated object location, orientation, and size. These desired values
can then generalized into some ballpark for performing the first phase