Systematic distortions of perceived planar surface motion in active vision

Recent studies suggest that the active observer combines optic flow information with extra-retinal signals resulting from head motion. Such a combination allows, in principle, a correct discrimination of the presence or absence of surface rotation. In Experiments 1 and 2, observers were asked to perform such discrimination task while performing a lateral head shift. In Experiment 3, observers were shown the optic flow generated by their own movement with respect to a stationary planar slanted surface and were asked to classify perceived surface rotation as being small or large. We found that the perception of surface motion was systematically biased. We found that, in active, as well as in passive vision, perceived surface rotation was affected by the deformation component of the first-order optic flow, regardless of the actual surface rotation. We also found that the addition of a null disparity field increased the likelihood of perceiving surface rotation in active, but not in passive vision. Both these results suggest that vestibular information, provided by active vision, is not sufficient for veridical 3D shape and motion recovery from the optic flow

Do we perceive a flattened world on the monitor screen

The current model of three-dimensional perception hypothesizes that the brain integrates the depth cues in a statistically optimal fashion through a weighted linear combination with weights proportional to the reliabilities obtained for each cue in isolation (Landy, Maloney, Johnston, & Young, 1995). Even though many investigations support such theoretical framework, some recent empirical findings are at odds with this view (e.g., Domini, Caudek, & Tassinari, 2006). Failures of linear cue integration have been attributed to cue-conflict and to unmodelled cues to flatness present in computer-generated displays. We describe two cue-combination experiments designed to test the integration of stereo and motion cues, in the presence of consistent or conflicting blur and accommodation information (i.e., when flatness cues are either absent, with physical stimuli, or present, with computer-generated displays). In both conditions, we replicated the results of Domini et al. (2006): The amount of perceived depth increased as more cues were available, also producing an over-estimation of depth in some conditions. These results can be explained by the Intrinsic Constraint model, but not by linear cue combination

Recovering slant and angular velocity from a linear velocity field: modeling and psychophysics

AbstractThe data from two experiments, both using stimuli simulating orthographically rotating surfaces, are presented, with the primary variable of interest being whether the magnitude of the simulated gradient was from expanding vs. contracting motion. One experiment asked observers to report the apparent slant of the rotating surface, using a gauge figure. The other experiment asked observers to report the angular velocity, using a comparison rotating sphere. The results from both experiments clearly show that observers are less sensitive to expanding than to contracting optic-flow fields. These results are well predicted by a probabilistic model which derives the orientation and angular velocity of the projected surface from the properties of the optic flow computed within an extended time window

stereovision for action reflects our perceptual experience of distance and depth

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Misperception of rigidity from actively generated optic flow

It is conventionally assumed that the goal of the visual system is to derive a perceptual representation that is a veridical reconstruction of the external world: a reconstruction that leads to optimal accuracy and precision of metric estimates, given sensory information. For example, 3-D structure is thought to be veridically recovered from optic flow signals in combination with egocentric motion information and assumptions of the stationarity and rigidity of the external world. This theory predicts veridical perceptual judgments under conditions that mimic natural viewing, while ascribing nonoptimality under laboratory conditions to unreliable or insufficient sensory information\u2014for example, the lack of natural and measurable observer motion. In two experiments, we contrasted this optimal theory with a heuristic theory that predicts the derivation of perceived 3-D structure based on the velocity gradients of the retinal flow field without the use of egomotion signals or a rigidity prior. Observers viewed optic flow patterns generated by their own motions relative to two surfaces and later viewed the same patterns while stationary. When the surfaces were part of a rigid structure, static observers systematically perceived a nonrigid structure, consistent with the predictions of both an optimal and a heuristic model. Contrary to the optimal model, moving observers also perceived nonrigid structures in situations where retinal and extraretinal signals, combined with a rigidity assumption, should have yielded a veridical rigid estimate. The perceptual biases were, however, consistent with a heuristic model which is only based on an analysis of the optic flow

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

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