Some Observations on Handwriting from a Motor Learning Perspective

We discuss the dynamics of signatures in the light of recent findings in motor learning, according to which a signature is a highly automated motor task and, as such, it is stored in the brain as both a trajectory plan and a motor plan. We then conjecture that such a stored representation does not necessarily include the entire signature, but can be limited to only parts of it, those that have been learned better and therefore are executed more automatically than others. Because these regions are executed more automatically than others, they are less prone to significant variations depending on the actual writing conditions, and therefore should represent better than other regions the distinctive features of signatures. To support our conjecture, we report and discuss the results of experiments conducted by using an algorithm for finding those regions in the signature ink and eventually using them for automatic signature verification.