We can use a motion-controlled sports videogame to simulate various human movement regimes (e.g. swinging, reaching). A weighted instrument (e.g. misshapen baseball bat with a forcing chamber ) was used to introduce chaotic motion during performance. By switching between this distortion and normal gameplay, we have created an environmental switch that can induce natural variation and the biological substrates that underlie performance.
This type of environmental switch is found in nature as brain-related preconditioning  in humans, or as a means to speed up adaptation in a given population . By presenting each type of movement regime in different sequences, we can augment performance under normal circumstances or control chaotic fluctuations .
IV. Review of Performance Mitigation Architectures
Two posts ago in the human augmentation thread, we were introduced to the role of allostasis and first-order linear control in correcting (e.g. mitigating) sub-optimal behaviors related to human performance. In this post, we will explore this theme further using architectures that adaptively control (e.g. augment) optimal levels of cognition and human performance.
The first architecture is the simple feedback with band-pass filter. This is often used to mitigate performance profiles that conform to the inverted U (e.g. arousal). The bandpass filter implements a simple rule used as feedback that reinforces parameter values within a certain range. This first-order linear control manages a unimodal response function as a signal-to-noise problem without excessive computational overhead.
But what about cases where our measure exhibits a greater number of measures? The second architecture demonstrates the simple feedback motif as a parallel array (in this case, two arrays) that contribute to a global assessment of performance (long rectangle). In this case, mitigation is treated as an optimization problem rather than a signal-to-noise problem. This allows us to search for optimal mitigation configurations on a n-dimensional landscape rather than extracting one-dimensional signal from noise.
In cases where the contributions of physiological variance are great, from example in systems which are not well-understood, we can use something called I call an allostatic control architecture. This type of model accounts for a dynamic physiological background as it interacts with performance embedded in its environment. To enforce this type of control, environmental switching  can be used. In this case, there is no feedback, but there is a linear filter that enforces a threshold on the response to both sets of environmental conditions. Levels of performance that are robust in both environments are selected for using the filter, and treats mitigation as a problem of stability during adaptation.
 Alicea, B. Stochastic Resonance (SR) can drive adaptive physiological processes. Nature Preceedings, npre.2009.3301.1 [http://precedings.nature.com/documents/3301/version/1] (2009).
 Hunt and Johnson Keeping Chaos at Bay. IEEE Spectrum, 30(11), 32-36 (1993).
 A forcing chamber is a container filled with a liquid or other material (with a specific gravity) to create a distorted radius of gyration during a swing, a reach, or a stroke.
 Gidday, J.M Cerebral preconditioning and ischemic tolerance. Nature Reviews Neuroscience, 7, 437-448 (2006).
 Kashtan, N., Noor, E., Alon, U. Varying environments can speed up evolution. PNAS USA, 104(34), 13711-13716 (2007).
 Ott, E., Grebogi, C., and Yorke, J.A. Controlling Chaos. Physical Review Letters, 1196-1199 (1990).
Ott, E. Controlling Chaos. Scholarpedia, 1(8), 1699 (2006).
 Shifting between environments continually or in a patterned way. See the last #human-augmentation post for more.