  This paper contributes to the human-machine interface community in two ways:
as a critique of the closed-loop AC (augmented cognition) approach, and as a
way to introduce concepts from complex systems and systems physiology into the
field. Of particular relevance is a comparison of the inverted-U (or Gaussian)
model of optimal performance and multidimensional fitness landscape model.
Hypothetical examples will be given from human physiology and learning and
memory. In particular, a four-step model will be introduced that is proposed as
a better means to characterize multivariate systems during behavioral processes
with complex dynamics such as learning. Finally, the alternate approach
presented herein is considered as a preferable design alternate in
human-machine systems. It is within this context that future directions are
discussed.
