Humans tend to be lazy so most of them would pick the cookie 10cm away. So, scientists concluded that humans more likely choose the option that minimizes the energy cost. Clearly, it takes less energy to grab the 10cm away cookie than the 15cm away one. However, a new study in the Journal of Neurophysiology demonstrates that the energy cost is not the only factor we take into account before making decisions (Cos, Belanger and Cisek, JNP, 2011).
The stability of a given arm posture can be represented by a mobility ellipse whose major and minor axes are aligned with the directions of maximal and minimal sensitivity to perturbations. In the above figure, the minor axis will be along the directions of the arrows in panel A and the major axis along the arrows of panel B.
Perturbations along the direction of the movement do not have to be necessarily corrected. Sometimes, they can help getting to the target. In contrast, perturbations orthogonal to the movements should be avoided because they always require a corrective action. This is reminiscent of the fact that extent and direction of movements are controlled by separate mechanisms. (Krakauer et al. 2000). Therefore, Cos and colleagues assumed that subjects would preferentially choose movements that ended along the unstable direction, which corresponds to the major axis of the mobility ellipse. With such movements, the direction orthogonal to the movement is really stable (aligned with the minor axis of the mobility ellipse). Therefore, the movement is less sensitive to orthogonal perturbation.
Panel B illustrates the predicted results following this hypothesis. It shows how often T1 is chosen (y-axis) vs. the distance between the starting points and the targets T1 and T2 (D1 and D2, resp.). On the x-axis, a positive number is associated with target T1 being further than target T2 and a negative number with target T1 being closer to starting position than target T2. Clearly, the distance between the targets and the starting point influences the choice made by the subjects (red solid curve). How often T1 is chosen (red solid curve in panel B) should decrease when T1 becomes further of the starting point compared to T2 (from left to right of the x-axis). Importantly, when T1 and T2 are equally far from the starting point (D1=D2 and log(D1/D2)=0), target T1 should be chosen more often that T2 (red solid curve is above 0.5 at log(D1/D2)=0).
If the movements to T1 ends up along the minor axis of the ellipse (right subplot of panel A, labeled T1-minor), then target T1 becomes undesirable and should be chosen less frequently. This prediction is illustrated by the dashed red curve on panel B.
These predictions were well confirmed by the data as show in the figure below (the blue trace corresponds to other target positions but the results were similar).
The authors of the paper also checked for the possible influence of confounding factors such as energy expenditure, error rates (you might avoid the target where you make more errors), difference in interaction torque, location of the target, etc. None of these factors could account for the observed effect of biomechanics on target choice.
In summary, this paper demonstrates that the biomechanical properties are taken into account during the decision-making process. The ability of the brain to identify the biomechanically desirable movements depends on its knowledge of the body biomechanical features. Therefore, this study demonstrates that the brain has access to an internal representation of the body, i.e. an internal model.
Krakauer, J. W., Pine, Z. M., Ghilardi, M. F., & Ghez, C. (2000). Learning of visuomotor transformations for vectorial planning of reaching trajectories. The Journal of neuroscience, 20(23), 8916-24.