Misperceptions of angular velocities influence the perception of rigidity in the kinetic depth effect

Perceived surface slant is systematically biased in the actively-generated optic flow

Humans make systematic errors in the 3D interpretation of the optic flow in both passive and active vision. These systematic distortions can be predicted by a biologically-inspired model which disregards self-motion information resulting from head movements (Caudek, Fantoni, & Domini 2011). Here, we tested two predictions of this model: (1) A plane that is stationary in an earth-fixed reference frame will be perceived as changing its slant if the movement of the observer's head causes a variation of the optic flow; (2) a surface that rotates in an earth-fixed reference frame will be perceived to be stationary, if the surface rotation is appropriately yoked to the head movement so as to generate a variation of the surface slant but not of the optic flow. Both predictions were corroborated by two experiments in which observers judged the perceived slant of a random-dot planar surface during egomotion. We found qualitatively similar biases for monocular and binocular viewing of the simulated surfaces, although, in principle, the simultaneous presence of disparity and motion cues allows for a veridical recovery of surface slant.

Bayesian modeling of perceived surface slant from actively-generated and passively-observed optic flow

We measured perceived depth from the optic flow (a) when showing a stationary physical or virtual object to observers who moved their head at a normal or slower speed, and (b) when simulating the same optic flow on a computer and presenting it to stationary observers. Our results show that perceived surface slant is systematically distorted, for both the active and the passive viewing of physical or virtual surfaces. These distortions are modulated by head translation speed, with perceived slant increasing directly with the local velocity gradient of the optic flow. This empirical result allows us to determine the relative merits of two alternative approaches aimed at explaining perceived surface slant in active vision: an "inverse optics" model that takes head motion information into account, and a probabilistic model that ignores extra-retinal signals. We compare these two approaches within the framework of the bayesian theory. The "inverse optics" bayesian model produces veridical slant estimates if the optic flow and the head translation velocity are measured with no error; because of the influence of a "prior" for flatness, the slant estimates become systematically biased as the measurement errors increase. The bayesian model, which ignores the observer's motion, always produces distorted estimates of surface slant. Interestingly, the predictions of this second model, not those of the first one, are consistent with our empirical findings. The present results suggest that (a) in active vision perceived surface slant may be the product of probabilistic processes which do not guarantee the correct solution, and (b) extra-retinal signals may be mainly used for a better measurement of retinal information.

A new theory of structure-from-motion perception

Humans can recover 3-D structure from the projected 2D motion field of a rotating object, a phenomenon called structure from motion (SFM). Current models of SFM perception are limited to the case in which objects rotate about a frontoparallel axis. However, as our recent psychophysical studies showed, frontoparallel axes of rotation are not representative of the general case. Here we present the first model to address the problem of SFM perception for the general case of rotations around an arbitrary axis. The SFM computation is cast as a two-stage process. The first stage computes the structure perpendicular to the axis of rotation. The second stage corrects for the slant of the axis of rotation. For cylinders, the computed object shape is invariant with respect to the observer's viewpoint (that is, perceived shape doesn't change with a change in the direction of the axis of rotation). The model uses template matching to estimate global parameters such as the angular speed of rotation, which are then used to compute the local depth structure. The model provides quantitative predictions that agree well with current psychophysical data for both frontoparallel and non-frontoparallel rotations.

Shape constancy and depth-order violations in structure from motion: a look at non-frontoparallel axes of rotation

Humans can recover the structure of a 3D object from motion cues alone. Recovery of structure from motion (SFM) from the projected 2D motion field of a rotating object has been studied almost exclusively in one particular condition, that in which the axis of rotation lies in the frontoparallel plane. Here, we assess the ability of humans to recover SFM in the general case, where the axis of rotation may be slanted out of the frontoparallel plane. Using elliptical cylinders whose cross section was constant along the axis of rotation, we find that, across a range of parameters, subjects accurately matched the simulated shape of the cylinder regardless of how much the axis of rotation is inclined away from the frontoparallel plane. Yet, we also find that subjects do not perceive the inclination of the axis of rotation veridically. This combination of results violates a relationship between perceived angle of inclination and perceived shape that must hold if SFM is to be recovered from the instantaneous velocity field. The contradiction can be resolved if the angular speed of rotation is not consistently estimated from the instantaneous velocity field. This, in turn, predicts that variation in object size along the axis of rotation can cause depth-order violations along the line of sight. This prediction was verified using rotating circular cones as stimuli. Thus, as the axis of rotation changes its inclination, shape constancy is maintained through a trade-off. Humans perceive the structure of the object relative to a changing axis of rotation as unchanging by introducing an inconsistency between the perceived speed of rotation and the first-order optic flow. The observed depth-order violations are the cost of the trade-off.