Multisensory Integration in Self Motion Perception

in Multisensory Research
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Self motion perception involves the integration of visual, vestibular, somatosensory and motor signals. This article reviews the findings from single unit electrophysiology, functional and structural magnetic resonance imaging and psychophysics to present an update on how the human and non-human primate brain integrates multisensory information to estimate one’s position and motion in space. The results indicate that there is a network of regions in the non-human primate and human brain that processes self motion cues from the different sense modalities.

Multisensory Integration in Self Motion Perception

in Multisensory Research

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Figures

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    Retinal flow fields and neuronal responses. The panels in the left column depict exemplary retinal flow fields seen by an observer moving across a ground plane. Here, heading as indicated by the arrow was always to the left, but the simulated eye movements differed, leading to markedly different retinal flow across the three eye movement conditions. Monkeys had to fixate a central target () in all cases. The right columns show the time resolved responses of a neuron from area VIP for all three different headings (as indicated by the post-stimulus time histograms, PSTHs, color traces) and the three different eye movement conditions. This neuron responded strongly for self motion to the left (indicated by the green response curves), irrespective of the underlying simulated eye movement. Medium responses were observed for movement straight ahead (blue response curves), while movement to the right (red response curves) induced in all three eye movement conditions inhibition of the ongoing activity with respect to baseline (Mann–Whitney rank test, p<0.001). Responses for a given self motion direction did not differ across eye movement conditions (ANOVA on ranks, 2 df, p>0.05 for each of the three heading directions).

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    Population responses. Panels A and B depict the distribution of rank-order triplets for areas MST (light grey, online version: orange) and VIP (medium grey, online version: green) as raw numbers (A) and in percent (B). Data from 64 MST neurons and 48 VIP neurons resulted in a set of n=64×3+48×3=336 rank-order triplets. Peak average discharges for identical heading directions (i.e., triplet ⟨1-1-1⟩) were observed in 60/336 = 17.9% of the cases. Weakest discharges for identical headings (⟨3-3-3⟩) occurred in 62/336 = 18.4% of the cases and medium discharges for identical headings (⟨2-2-2⟩) occurred in 44/336 = 13.1% of the cases. Each of these rank-order triplets indicates the invariance of a neuron’s heading response with respect to simulated eye movements. Considering the occurrence of these three triplets together, eye movement invariances were found in 168/336 = 49.4% of the cases. This proportion was significantly larger than would have been expected, if responses for a given heading direction across the different eye movement conditions had been independent (χ2=117.5, 1 df, p<0.001). Importantly, we found this overrepresentation of eye movement invariance in each of the two areas individually: in 82/192 = 42.7% of the cases in area MST (χ2=49.4, 1 df, p<0.001), and in 84/144 = 58.3% of the cases in area VIP (χ2=72.6, 1 df, p<0.001). Panels C and D depict the time courses of the establishment of eye movement invariance (⟨1-1-1⟩) and of a random response scheme (⟨1-2-3⟩). Panel E depicts the distribution of the rank-order quadruplets. In area MST, we found a coincidence of heading preferences for simulated and real eye movements (rank-order quadruplet ⟨1-1-1-1⟩) in 20 out of 147 cases (13.6%). The weakest response for a given heading direction (⟨3-3-3-3⟩) was observed in 19/147 = 12.9% of the cases. A medium response for a given heading (⟨2-2-2-2⟩) was found in 13/147 = 8.8% of the cases. Each observed number of cases of the three eye movement invariances occurred significantly more often than would have been expected if tunings had been distributed uniformly (smallest χ2 value=8.5, 1 df, p<0.005). A similar result was obtained from the population of VIP neurons. Here, response peaks for a given heading direction were observed in 16/111 = 14.4% of the cases. Medium and weakest responses for a given heading direction were found in 18/111 = 16.2% and 10/111 = 9% of the cases, respectively. Again, each of these proportions differed significantly from a uniform distribution (smallest χ2 value=5.64, 1 df, p<0.03). Panel F shows the distribution of the response modulation for simulated (abscissa) and real (ordinate) eye movements. For details see main text. This figure is published in colour in the online version.

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    Crossmodal self motion aftereffects. (A) Illustration of the experimental protocol. During the adaptation phase (left), subjects were presented with an optic flow simulating either forward or backward self motion at constant velocity (3 m/s). In the test phase (right), subjects experienced a 2-s passive linear fore–aft translation with a Gaussian velocity profile (vestibular-only test), and subsequently indicated the perceived movement direction. In a control condition, a visual-only test stimulus (far right) composed of expanding or contracting optic flow was used instead, and subjects indicated the direction of optic flow. Different adaption conditions were run in separate blocks of 50 trials each: baseline (no adaptation), forward (15 s), backward (15 s), as well as three forward blocks with shorter adapter durations of 7.5, 3.75, or 1.5 s. (B) Mean aftereffect (PSE) across subjects (n=20) following forward and backward adaptation with 15 s duration compared with the no-adapter baseline. (C) Mean aftereffect (expressed as forward minus baseline PSE) as a function of adapter duration (n=17). Subjects additionally rated their vection on a scale of 1 to 7, with 1 representing perception of object motion only and 7 representing perception of self motion only. Gray dots indicate mean subjective ratings. (D) Crossmodal and visual-only aftereffects (expressed as forward or backward minus baseline PSE) are uncorrelated (r=0.003; p=0.98) (n=15). To allow for comparison, backward aftereffects are multiplied by −1. All error bars show SE.

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    Experimental setup and results. Participants are seated in the motion platform with a screen in front of them. Upper panel: Experiment 1. Lower panel: Experiment 2. Participants show optimal visuo-vestibular integration, their bimodal threshold (grey square, online version: red) being lower than their unimodal visual (open square, online version: blue) and vestibular (black) thresholds, and not different from the predictions of a Bayesian optimal integration model (open circle, online: red circle). This figure is published in colour in the online version.

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    (A) Experimental setup testing for the effects of natural vestibular stimulation on tactile detection. A congruent trial is depicted: rotation direction corresponds to the side of tactile stimulation. (B) Results. Independently of congruency tactile sensitivity was improved during rotation in comparison to a no-rotation baseline. This figure is published in colour in the online version.

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    (A) Cortical regions of interest studied. The figure shows an inflated representation of the right cerebral hemisphere of one individual with the locations and extents of hMT, hMST, hVIP, V6 and CSv indicated as grey-scaled (online version: coloured) overlays. In each case, BOLD activity elicited by the visual localizer that was used to define the region is shown in a slice through the brain of the same participant. A corresponding set of visual areas is present in the left hemisphere (not shown). Modified from Smith et al. (2012). (B) A ‘cut-out’ section of the flattened grey matter representation from each hemisphere of one participant, centered on the MT complex (dashed white line is the superior temporal sulcus). Vestibular activity (grey-scaled, online version: orange/yellow) is superimposed, together with the outlines of hMT (light grey, online version: green) and hMST (dark grey, online version: magenta) as defined with a visual localizer. Vestibular activity is apparent in hMST but not hMT and is confined to the anterior portion of hMST. Modified from Smith et al. (2012). (C) Visual stimulus used by Billington and Smith (2015). A circular patch of white dots appears to rotate during GVS. Physical rotation of the patch on the screen was used to null this illusory motion. (D) MVPA results for classifying the temporal phase of sinusoidal visual and vestibular rotations in the roll plane for five cortical visual areas. Chance performance is shown, along with the 95th percentile obtained from permutation testing as an indicator of statistical significance. Modified from Billington and Smith et al. (2015). This figure is published in colour in the online version.

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    Organization of human vestibular cortex in lateral sulcus (also called sylvian fissure). Shown are activations in a sample participant (left hemisphere) during stimulation with visual motion and caloric vestibular cues (p<0.001, uncorrected). (A) Visual motion stimulation shows significant activations in the posterior insular cortex area (PIC) in the posterior end of the lateral sulcus. In addition to PIC, other motion-sensitive regions in visual and parietal cortex respond well to visual motion stimuli. (B) Caloric stimulation elicits activations in the vestibular network in lateral sulcus, including the putative center of cortical vestibular processing, the parieto-insular vestibular cortex area (PIVC). Activations during caloric stimulation are also evident in area PIC, suggesting that PIC is part of both, the vestibular and the visual motion processing networks After Frank et al., 2014 (with permission of the publisher) and Frank et al., submitted for publication. This figure is published in colour in the online version.

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