The key to a successful simulation of speech acquisition with a parametric articulatory synthesizer lies, inter alia, in the successful exploration of its articulatory dimensions. However, such an exploration (regardless of the respective algorithm) may be non-trivial due to the high dimensionality of a modeled vocal tract and the associated high probability of creating unnatural or humanly impossible vocal tract shapes. In this work, a method based on principal component analysis is used to reduce the scope of motor space of the articulatory synthesizer VOCALTRACTLAB. It is shown that such a technique can be used to increase the computational efficiency of vocal learning simulations and thus may help to establish better exploration-based acoustic-to-articulatory-inversion models.