  Recent developments in hybrid biological-technological systems (hybrid bionic
systems) has made clear the need for evaluating ergonomic fit in such systems,
especially as users first become adjusted to using such systems. This training
is accompanied by physiological adaptation, and can be thought of
computationally as a relative degree of matching between prosthetic devices,
physiology, and behavior. Achieving performance augmentation involves two
features of performance: a specific form of learning, memory, and
mechanotransduction called sensorimotor learning, and physiological adaptation
to novel physical information imposed by the augmented environment of hybrid
bionic systems. A method borrowed from environmental medicine involving
perturbing the environment for a range of internal physiological conditions was
used to induce sensorimotor learning and memory associated physiological
changes. In addition, features of the adult phenotype were considered as a
mitigator of learning-related adaptations. Using a series of statistical tests
and techniques, the results demonstrate than three forms of regulation are at
work related to morphological, neural, and muscular control. A discussion of
the conceptual relationship between homeostasis and adaptation will then be
discussed in addition to potential applications to performance augmentation
strategies.
