Comparison between the spectral properties of envelope signals recorded during active and passive self-motion.

<p><b>A:</b> Subject-averaged best-fit power law exponents over the low (gray) and high (black) frequency ranges for all six motion dimensions for active self-motion. Also shown for comparison are the subject-averaged best-fit power law exponents for a single power law over the entire frequency range (blue). <b>B:</b> Subject-averaged best-fit power law exponents over the low (gray) and high (black) frequency ranges for all six motion dimensions for passive self-motion. Also shown for comparison are the subject-averaged best-fit power law exponents for a single power law over the entire frequency range (blue). “*” indicates statistical significance at the p = 0.01 level using a one-way ANOVA.</p

Envelope signals deviate from scale invariance.

<p><b>A:</b> Subject-averaged power spectra (red lines) with best-fit power laws over the low and high frequency ranges (black lines) as well as best-fit single power law over the entire frequency range (blue lines). Also shown are the best-fit power law exponents with confidence interval as well as the transition frequency. The dashed gray lines show the “noise floor”, which is the spectrum of the noise in the measurement obtained when the sensor was not moving (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178664#sec002" target="_blank">Methods</a>). Gray bands show 1 STD. <b>B:</b> Subject-averaged best-fit power law exponents over the low (gray) and high (black) frequency ranges for all six motion dimensions. Also shown for comparison are the subject-averaged best-fit power law exponents for a single power law over the entire frequency range (blue). “*” indicates statistical significance at the p = 0.01 level using a one-way ANOVA. <b>C:</b> Subject-averaged frequency at which the power spectrum starts decreasing more sharply for all six motion dimensions.</p

Statistics of environmental signals obtained when the subject is absent.

<p><b>A:</b> Schematic showing the MEMS module (gold box) located on the subject’s head and placed on the seat during passive self-motion. <b>B,C,D,E, F, G:</b> Trial-averaged power spectra of signals in the external environment (green) during passive self-motion for inter aural (<b>B</b>), Fore-Aft (<b>C</b>), Vertical (<b>D</b>), LARP (<b>E</b>), RALP (<b>F</b>), and YAW (<b>G</b>). The power spectra were in general well fit by a single power law over the entire frequency range (blue lines).</p

Active motion introduces deviation from scale invariance in the envelopes of natural translational self-motion signals recorded along the Inter-Aural and Vertical axes.

<p><b>A:</b> Schematic showing a subject engaged in active self-motion (left) and in passive self-motion (right). <b>B,C,D,E, F, G:</b> Subject-averaged envelope power spectra for active (left panels) and passive (right panels) activities for inter aural (<b>B</b>), Fore-Aft (<b>C</b>), Vertical (<b>D</b>), LARP (<b>E</b>), RALP (<b>F</b>), and YAW (<b>G</b>). In each case, the power spectra were fitted using two power laws over the low and high frequency ranges (black lines) as well as by a single power law over the entire frequency range (blue lines). Also shown are the best-fit power law exponents with confidence interval as well as the transition frequency.</p

Well-established models of the vestibular periphery predict that irregular afferents have greater sensitivities to envelopes than their regular counterparts.

<p><b>A:</b> Schematic showing the vestibular end organs as well as regular and irregular vestibular afferents projecting to the vestibular nuclei. <b>B:</b> Sensitivity to the carrier for the regular (dashed black) and irregular (solid red) model afferents. <b>C:</b> Time series showing a segment of the envelope stimulus (solid black) and the responses of the model regular (dashed black) and irregular (solid red) afferents. <b>D:</b> Gain to the envelope as a function of frequency for the regular (dashed black) and irregular (solid red) model afferents. In both cases the gain is relatively independent of frequency but is about twice higher for the irregular model afferent.</p

Subject-averaged maximum value, mean, and kurtosis for passive everyday activities.

<p>The maximum and mean values are expressed in mG for the Lateral, Fore-Aft and Vertical linear acceleration while they are expressed in deg/s for the LARP, RALP and Yaw angular velocity.</p