  Motion simulators are widely employed in basic and applied research to study
the neural mechanisms of perception and action under inertial stimulations. In
these studies, uncontrolled simulator-introduced noise inevitably leads to a
mismatch between the reproduced motion and the trajectories meticulously
designed by the experimenter, possibly resulting in undesired motion cues to
the investigated system. An understanding of the simulator response to
different motion commands is therefore a crucial yet often underestimated step
towards the interpretation of experimental results. In this work, we developed
analysis methods based on signal processing techniques to quantify the noise in
the actual motion, and its deterministic and stochastic components. Our methods
allow comparisons between commanded and actual motion as well as between
different actual motion profiles. A specific practical example from one of our
studies is used to illustrate the methodologies and their relevance, but this
does not detract from its general applicability. Analyses of the simulator
inertial recordings show direction-dependent noise and nonlinearity related to
the command amplitude. The Signal-to-Noise Ratio is one order of magnitude
higher for the larger motion amplitudes we tested, compared to the smaller
motion amplitudes. Deterministic and stochastic noise components are of similar
magnitude for the weaker motions, whereas for stronger motions the
deterministic component dominates the stochastic component. The effect of
simulator noise on animal/human motion sensitivity is discussed. We conclude
that accurate analyses of a simulator motion are a crucial prerequisite for the
investigation of uncertainty in self-motion perception.
