Three-dimensional Motion Perception Research Articles

Head jitter enhances three-dimensional motion perception.

Motion perception is a critical function of the visual system. In a three-dimensional environment, multiple sensory cues carry information about an object's motion trajectory. Previous work has quantified the contribution of binocular motion cues, such as interocular velocity differences and changing disparities over time, as well as monocular motion cues, such as size and density changes. However, even when these cues are presented in concert, observers will systematically misreport the direction of motion-in-depth. Although in the majority of laboratory experiments head position is held fixed using a chin or head rest, an observer's head position is subject to involuntary small movements under real-world viewing conditions. Here, we considered the potential impact of such “head jitter” on motion-in-depth perception. We presented visual stimuli in a head-mounted virtual reality device that facilitated low latency head tracking and asked observers to judge 3D object motion. We found performance improved when we updated the visual display consistent with the small changes in head position. When we disrupted or delayed head movement–contingent updating of the visual display, the proportion of motion-in-depth misreports again increased, reflected in both a reduction in sensitivity and an increase in bias. Our findings identify a critical function of head jitter in visual motion perception, which has been obscured in most (head-fixed and non-head jitter contingent) laboratory experiments.

Three-Dimensional Motion Perception: Comparing Speed and Speed Change Discrimination for Looming Stimuli.

Judging the speed of objects moving in three dimensions is important in our everyday lives because we interact with objects in a three-dimensional world. However, speed perception has been seldom studied for motion in depth, particularly when using monocular cues such as looming. Here, we compared speed discrimination, and speed change discrimination, for looming stimuli, in order to better understand what visual information is used for these tasks. For the speed discrimination task, we manipulated the distance and duration information available, in order to investigate if participants were specifically using speed information. For speed change discrimination, total distance and duration were held constant; hence, they could not be used to successfully perform that task. For the speed change discrimination task, our data were consistent with observers not responding specifically to speed changes within an interval. Instead, they may have used alternative, arguably less optimal, strategies to complete the task. Evidence suggested that participants used a variety of cues to complete the speed discrimination task, not always solely relying on speed. Further, our data suggested that participants may have switched between cues on a trial to trial basis. We conclude that speed changes in looming stimuli were not used in a speed change discrimination task, and that naïve participants may not always exclusively use speed for speed discrimination.

Eye-specific pattern-motion signals support the perception of three-dimensional motion.

An object moving through three-dimensional (3D) space typically yields different patterns of velocities in each eye. For an interocular velocity difference cue to be used, some instances of real 3D motion in the environment (e.g., when a moving object is partially occluded) would require an interocular velocity difference computation that operates on motion signals that are not only monocular (or eye specific) but also depend on each eye's two-dimensional (2D) direction being estimated over regions larger than the size of V1 receptive fields (i.e., global pattern motion). We investigated this possibility using 3D motion aftereffects (MAEs) with stimuli comprising many small, drifting Gabor elements. Conventional frontoparallel (2D) MAEs were local—highly sensitive to the test elements being in the same locations as the adaptor (Experiment 1). In contrast, 3D MAEs were robust to the test elements being in different retinal locations than the adaptor, indicating that 3D motion processing involves relatively global spatial pooling of motion signals (Experiment 2). The 3D MAEs were strong even when the local elements were in unmatched locations across the two eyes during adaptation, as well as when the adapting stimulus elements were randomly oriented, and specified global motion via the intersection of constraints (Experiment 3). These results bolster the notion of eye-specific computation of 2D pattern motion (involving global pooling of local, eye-specific motion signals) for the purpose of computing 3D motion, and highlight the idea that classically “late” computations such as pattern motion can be done in a manner that retains information about the eye of origin.

Contributions of binocular and monocular cues to motion-in-depth perception.

Intercepting and avoiding moving objects requires accurate motion-in-depth (MID) perception. Such motion can be estimated based on both binocular and monocular cues. Because previous studies largely characterized sensitivity to these cues individually, their relative contributions to MID perception remain unclear. Here we measured sensitivity to binocular, monocular, and combined cue MID stimuli using a motion coherence paradigm. We first confirmed prior reports of substantial variability in binocular MID cue sensitivity across the visual field. The stimuli were matched for eccentricity and speed, suggesting that this variability has a neural basis. Second, we determined that monocular MID cue sensitivity also varied considerably across the visual field. A major component of this variability was geometric: An MID stimulus produces the largest motion signals in the eye contralateral to its visual field location. This resulted in better monocular discrimination performance when the contralateral rather than ipsilateral eye was stimulated. Third, we found that monocular cue sensitivity generally exceeded, and was independent of, binocular cue sensitivity. Finally, contralateral monocular cue sensitivity was found to be a strong predictor of combined cue sensitivity. These results reveal distinct factors constraining the contributions of binocular and monocular cues to three-dimensional motion perception.

Disparity- and velocity-based signals for three-dimensional motion perception in human MT+

How does the primate visual system encode three-dimensional motion? The macaque middle temporal area (MT) and the human MT complex (MT+) have well-established sensitivity to two-dimensional frontoparallel motion and static disparity. However, evidence for sensitivity to three-dimensional motion has remained elusive. We found that human MT+ encodes two binocular cues to three-dimensional motion: changing disparities over time and interocular comparisons of retinal velocities. By varying important properties of moving dot displays, we distinguished these three-dimensional motion signals from their constituents, instantaneous binocular disparity and monocular retinal motion. An adaptation experiment confirmed direction selectivity for three-dimensional motion. Our results indicate that MT+ carries critical binocular signals for three-dimensional motion processing, revealing an important and previously overlooked role for this well-studied brain area.

Collision judgment of objects approaching the head

Recent investigations have indicated that human perception of the trajectory of objects approaching in the horizontal plane is precise but biased away from straight ahead. This is remarkable because it could mean that subjects perceive objects that approach on a collision course as missing the head. Approach within the horizontal plane through the eyes and the fixation point (the plane of regard) is special, as general motions will also have a component of motion perpendicular to the plane of regard. Thus, we investigated three-dimensional motion perception in the vicinity of the head, including vertical components. Subjects judged whether an object that moved in the mid-sagittal plane was going to hit below or above a well-known reference point on the face like the center of the chin or the forehead (perceptual task). Tactile and proprioceptive information about the reference point significantly improved precision. Precision did not change with distance of the approaching target or with fixation direction. Bias was virtually absent for these vertical motions. When subjects pointed with their index finger to the perceived location of impact on their face (visuo-motor task), they overestimated (1.7 cm) the horizontal eccentricity of the point of impact (pointing task). Vertical bias, however, was again virtually absent. Interestingly, when trajectories intersected the plane of regard, higher precision was observed in the perceptual task regardless of the other conditions. In contrast, neither bias nor precision of the pointing task changed significantly when the trajectories intersected the plane of regard. When asked to point to the location where a trajectory intersected the plane of regard, subjects overestimated the depth component of this intersection location by about 3 cm. The absence of perceptual and pointing bias in the vertical direction in contrast to the clear horizontal bias suggests that different (combinations of) cues are used to judge these components of the trajectory of an approaching object. The results of our perceptual task suggest a role for somatosensory signals in the visual judgment of impending impact.

Using visual direction in three-dimensional motion perception

The eyes receive slightly different views of the world, and the differences between their images (binocular disparity) are used to see depth. Several authors have suggested how the brain could exploit this information for three-dimensional (3D) motion perception, but here we consider a simpler strategy. Visual direction is the angle between the direction of an object and the direction that an observer faces. Here we describe human behavioral experiments in which observers use visual direction, rather than binocular information, to estimate an object's 3D motion even though this causes them to make systematic errors. This suggests that recent models of binocular 3D motion perception may not reflect the strategies that human observers actually use.

Direct Perception of Three-Dimensional Motion from Patterns of Visual Motion

Measurements of retinal motion along a set of predetermined orientations on the retina of a moving system give rise to global patterns. Because the form and location of these patterns depend purely on three-dimensional (3D) motion, the effects of 3D motion and scene structure on image motion can be globally separated. The patterns are founded on easily derivable image measurements that depend only on the sign of image motion and do not require information about optical flow. The computational theory presented here explains how the self-motion of a system can be estimated by locating these patterns.

Perception & Control of Self-Motion

Contents: Part I:Phenomena, Problems, & Terms. R. Warren, Preliminary Questions for the Study of Egomotion. D.H. Owen, Lexicon of Terms for the Perception and Control of Self-Motion and Orientation. Part II:Visual Aspects. J.J. Koenderink, Some Theoretical Aspects of Optic Flow. K. Nakayama, Properties of Early Motion Processing: Implications for the Sensing of Egomotion. V. Cornilleau-P r s, J. Droulez, Three-Dimensional Motion Perception: Sensorimotor Interactions and Computational Models. L. Wolpert, Field-of-View Information for Self-Motion Perception. G.J. Andersen, Segregation of Optic Flow Into Object and Self- Motion Components: Foundations for a General Model. Part III:Vestibular & Multisensory Aspects. A.J. Benson, Sensory Functions and Limitations of the Vestibular System. A.H. Wertheim, Visual, Vestibular, and Oculomotor Interactions in the Perception of Object Motion During Egomotion. T. Mergner, W. Becker, Perception of Horizontal Self-Rotation: Multisensory and Cognitive Aspects. Part IV:Skill & Control Aspects. H. Mittelstaedt, Basic Solutions to the Problem of Head-Centric Visual Localization. D.H. Owen, Perception & Control of Change in Self-Motion: A Functional Approach to the Study of Information and Skill. J.M. Flach, G. Lintern, J.F. Larish, Perceptual Motor Skill: A Theoretical Framework. W.A. van de Grind, Smart Mechanisms for the Visual Evaluation and Control of Self-Motion. R.J.A.W. Hosman, J.C. van der Vaart, Motion Perception and Vehicle Control. G.L. Zacharias, An Estimation/Control Model of Egomotion. Part V:Special Environmental Aspects. H.E. Ross, Orientation and Movement in Divers. D.N. Lee, Getting Around With Light or Sound. G. Jansson, Non-Visual Guidance of Walking. L.R. Young, M. Shelhamer, Weightlessness Enhances the Relative Contribution of Visually-Induced Self-Motion. Part IV:Self-Motion and the Perception & Control of the Environment. J.S. Lappin, Perceiving the Metric Structure of Environmental Objects from Motion, Self-Motion and Stereopsis. R.E. Shaw, P.N. Kugler, J. Kinsella-Shaw, Reciprocities of Intentional Systems.

A Lie group approach to a neural system for three-dimensional interpretation of visual motion

A novel approach is presented to neural network computation of three-dimensional rigid motion from noisy two-dimensional image flow. It is shown that the process of 3-D interpretation of image flow can be viewed as a linear signal transform. The elementary signals of this linear transform are the 2-D vector fields of the six infinitesimal generators of the 3-D Euclidean group. This transform can be performed by a neural network. Results are also reported of neural network simulations for the 3-D interpretation of image flow and a comparison of the performance of this approach with that using conventional methods. Computer simulation results verify the Lie-group-based neural network approach to three-dimensional motion perception.

Visual Perception of Three-Dimensional Motion

As an observer moves and explores the environment, the visual stimulation in his eye is constantly changing. Somehow he is able to perceive the spatial layout of the scene, and to discern his movement through space. Computational vision researchers have been trying to solve this problem for a number of years with only limited success. It is a difficult problem to solve because the relationship between the optical-flow field, the 3D motion parameters, and depth is nonlinear. We have come to understand that this nonlinear equation describing the optical-flow field can be split by an exact algebraic manipulation to yield an equation that relates the image velocities to the translational component of the 3D motion alone. Thus, the depth and the rotational velocity need not be known or estimated prior to solving for the translational velocity. The algorithm applies to the general case of arbitrary motion with respect to an arbitrary scene. It is simple to compute and it is plausible biologically.

Responsiveness of Clare-Bishop neurons to visual cues associated with motion of a visual stimulus in three-dimensional space

Photic responsiveness of cells in the medial bank of the lateral suprasylvian cortex (Clare-Bishop area) was studied using a three-dimensional visual stimulator that reproduced two visual cues (motion disparity and change in size) for perception of three-dimensional motion of a visual stimulus. About one third of them (48/148) were selectively responsive to motion disparity corresponding to approaching (AP cells, n = 30) or recessive motion (RC cells, N = 18), another half to motion of retinal images in the same direction between the two eyes corresponding to fronto-parallel motion (FP cells, n = 75), and the remaining cells were rather equally responsive to these types of stimuli (NS cells, n = 25). More than a half of the AP (19/30) or RC (11/18) cells were also responsive to increase or decrease in stimulus size, respectively, and they were optimally activated by a combination of the motion and size stimuli while relatively few FP and NS cells were sensitive to change in stimulus size. These findings indicate that the Clare-Bishop cells encode three-dimensional motion on the basis of photic responsiveness to the motion and size cues.