Here  is a profile of one of my “lost” papers: “Range-based techniques for discovering optimality and analyzing scaling relationships in neuromechanical systems”. This paper was published in 2009 at Nature Preceedings , but I’m not sure it ever got much exposure (it may have been a bit ahead of its time). The paper introduces something called the rescaled range technique, which I will describe below.
The objective of this study was to take data collected from experiments  conducted in virtual environments, and then compare metrics of performance (movement behavior and muscle activity) to something called a “morphological scaling” (e.g. the proportion of forelimb length or volume to humeral length or volume). The application of mathematical modeling provides us with a tool called the “rescaled range”.
What if we were to manipulate the length of these limbs well beyond their natural range? Could the tasks still be performed? Or would there be significant performance gains at certain scalings of size? The idea of the rescale range technique is to simulate this possibility based on empirical observations and mathematical modeling. The second picture from the top summarizes this idea in terms of physical implementation and the theoretical concept of hypo- and hyper-allometry .
By scaling each component of observed limb measurements by a certain factor, we effective also resample the performance data (as shown in the third and fourth figures from top). Indeed, this resampling shows that there are large-scale increases and decreases in performance for various manipulations.
How are these findings useful? The first point is that these modeled manipulations of limb lengths serve as an analogue for what might be possible in the use and design of tools, devices used for teleoperation, and virtual representations of touch and the body. There are also a number of interesting relationships between physical perturbations and performance in such tasks . This work has potentially great relevance to the design of immersive virtual environments, or touch- and movement-based virtual environments that have a significant real-world component.
 More information on Figure 1:
Picture at lower right from the Gritsenko Lab Website, University of West Virginia. This lab does research on the neural mechanisms behind the online correction of sensorimotor control using virtual environments. Somewhat similar to what I am getting at here — the difference is that I am focusing more on the distortion capabilities/potential of the virtual environment itself.
Picture at lower left from the following article: Pappas, S. Machine That Feels May Usher in ‘Jedi’ Prosthetics. LiveScience, October 5 (2011).
 Alicea, B. Range-based techniques for discovering optimality and analyzing scaling relationships in neuromechanical systems. Nature Precedings, npre.2009.2845.2 (2009).
 The experiments involved reaching for and manipulating virtual objects (e.g. arm swinging and touching) with feedback. The feedback was distorted not only by the virtuality of the task, but also by distorting the tools (e.g. physics, shape) used to perform these tasks.
 Hypo- and hyper-allometry normally occur in the development of organisms, and usually describe evolutionary changes that unfold across related taxa. For example, in the case of the order Primates, the forearm:humerus relationship is highly variable across species, but not so much across individuals within a species.
 See the following papers for other work that provide more detail about these type of experiments:
Alicea, B. Naturally Supervised Learning in Motion and Touch-driven Technologies. arXiv, arXiv: 1106:1105. [cs.HC, q-bio.NC] (2011).
Alicea, B. Performance Augmentation in Hybrid Systems: techniques and experiment. arXiv, arXiv:0810.4629 [q-bio.NC, q-bio.QM] (2008).