Neural sensitivity to translational self- and object-motion velocities

Abstract

The ability to detect and assess world-relative object-motion is a critical computation performed by the visual system. This computation, however, is greatly complicated by the observer's movements, which generate a global pattern of motion on the observer's retina. How the visual system implements this computation is poorly understood. Since we are potentially able to detect a moving object if its motion differs in velocity (or direction) from the expected optic flow generated by our own motion, here we manipulated the relative motion velocity between the observer and the object within a stationary scene as a strategy to test how the brain accomplishes object-motion detection. Specifically, we tested the neural sensitivity of brain regions that are known to respond to egomotion-compatible visual motion (i.e., egomotion areas: cingulate sulcus visual area, posterior cingulate sulcus area, posterior insular cortex [PIC], V6+, V3A, IPSmot/VIP, and MT+) to a combination of different velocities of visually induced translational self- and object-motion within a virtual scene while participants were instructed to detect object-motion. To this aim, we combined individual surface-based brain mapping, task-evoked activity by functional magnetic resonance imaging, and parametric and representational similarity analyses. We found that all the egomotion regions (except area PIC) responded to all the possible combinations of self- and object-motion and were modulated by the self-motion velocity. Interestingly, we found that, among all the egomotion areas, only MT+, V6+, and V3A were further modulated by object-motion velocities, hence reflecting their possible role in discriminating between distinct velocities of self- and object-motion. We suggest that these egomotion regions may be involved in the complex computation required for detecting scene-relative object-motion during self-motion.

Keywords: brain mapping; flow parsing; functional magnetic imaging; motion detection; optic flow; virtual reality.

FIGURE 1

Virtual reality environment and schematic representations of stimulus conditions. (a) A 2D visual representation of virtual environment from a top perspective is shown. (b) Static frames and still frames from the 3‐s movies reproducing the five motion conditions (self‐only, object‐only, retinal‐null, object‐faster, and object‐slower) are shown. Color codes are used here for illustrative purpose only and correspond to the ones used in Figure 2b. The solid black arrow indicates the simulated observer's motion direction. The dashed black arrow indicates the object (the row of buses) motion direction. The length of the arrows indicates the amount of motion velocity. All the examples show rightward motion. Both leftward and rightward translational motions were presented although in separate runs. (c) Example of trial sequence and timeline. Participants were presented with a series of movies reproducing different combinations of self‐ and object‐motion and they were instructed to answer the question trial referring to the immediately preceding trial.

FIGURE 2

Egomotion areas and their sensitivity to translational motion. (a). Brain location of the individually defined egomotion areas CSv, pCi, PIC, V6+, V3A, IPSmot/VIP, and MT+. ROIs are overlapped onto a brain atlas (Conte69) in different views (lateral and medial) of both left (LH) and right (RH) hemispheres. The intensity of color saturation indicates the percentage of participants whose region encompasses that particular node: greater color saturation corresponds to a higher degree of overlap for each node across individual ROIs. (b) ROIs responses are plotted as a function of the experimental condition. Column histograms plot the mean percentage of signal changes (±SE) estimated in each egomotion region and averaged across subjects and hemispheres. *p < .05; **p < .01. Dashed brackets indicate the preference for pure self‐motion over the other motion conditions. Further details about individual data are provided in Figure S2. CSv, cingulate sulcus visual area; pCi, posterior cingulate sulcus area; PIC, posterior insular cortex; ROI, region of interest.

FIGURE 3

Whole brain activation map showing the involvement of area prostriata. (a) Group activation map, as resulting from the omnibus F contrast, is displayed on the inflated surface reconstruction (postero‐medial view) of atlas Conte69. The MNI coordinates (mm) of the area prostriata are as follows: LH, x = −20, y = −61, z = 6; RH, x = 22, y = −57, z = 6. (b) BOLD response of area prostriata is plotted as a function of the experimental condition. Column histograms plot the mean percentage of signal changes (±SE) averaged across subjects and hemispheres. *p < .05; **p < .01. Further details about individual data are provided in Figure S2. LH, left hemisphere; RH, right hemisphere.

FIGURE 4

Parametric modulation in the egomotion areas. Plots show the parametric modulation of the BOLD activity as a function of different (self and object) motion velocities. Column histograms plot the impact of parametric modulators reflecting self‐ (SM) and object‐motion (OM) velocities, as measured by the beta weights of these regressors, on the neural activity of each ROI. Asterisks mark significant results. ⁺ p < .05; Bonferroni‐uncorrected; *p < 1 × 10⁻⁴; **p < 1 × 10⁻⁵. ROI, region of interest.

FIGURE 5

Neural dissimilarity in the egomotion areas as a function of motion velocities. Plots show the relationship (as measured by beta weights in the regression model) between the neural dissimilarity and the dissimilarity in terms of self‐ (a) and object‐ (b) motion velocity. ⁺ p < .05; Bonferroni‐uncorrected; *p < .001; **p < 1 × 10⁻⁵.

FIGURE 6

Representational dissimilarity matrices reflecting the neural distances (mean crossnobis distances) between pairs of exemplars are plotted in red‐to‐green patches, for each ROI. RDM is symmetric about a diagonal of zeros. Each exemplar reflects a combination of different velocities of both self (S) and object (O) motion. For example, S20O60 corresponds to a stimulus in which the observer moved at 20 km/h and the object moved at 60 km/h. ROI, region of interest.