estimation-contact-regions-between-hands-objects-during-human-multi

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

To grasp an object successfully, we must select appropriate contact regions for our hands on the surface of the object. However, identifying such regions ...

Justus Liebig University Giessen

The formation of focused and corresponding foveal images requires a close synergy between the accommodation and vergence systems. This linkage is usually decoupled in virtual reality systems and may be dysfunctional in people who are at risk of developing myopia. We study how refractive error affects vergence-accommodation interactions in stereoscopic displays. Vergence and accommodative responses were measured in 21 young healthy adults (n=9 myopes, 22-31 years) while subjects viewed naturalistic stimuli on a 3D display. In Step 1, vergence was driven behind the monitor using a blurred, non-accommodative, uncrossed disparity target. In Step 2, vergence and accommodation were driven back to the monitor plane using naturalistic images that contained structured depth and focus information from size, blur and/or disparity. In Step 1, both refractive groups converged towards the stereoscopic target depth plane, but the vergence-driven accommodative change was smaller in emmetropes than in myopes (F=5.13, p=0.036). In Step 2, there was little effect of peripheral depth cues on accommodation or vergence in either refractive group. However, vergence responses were significantly slower (F=4.55, p=0.046) and accommodation variability was higher (F=12.9, p=0.0019) in myopes. Vergence and accommodation responses are disrupted in virtual reality displays in both refractive groups. Accommodation responses are less stable in myopes, perhaps due to a lower sensitivity to dioptric blur. Such inaccuracies of accommodation may cause long-term blur on the retina, which has been associated with a failure of emmetropization.

Vergence driven accommodation with simulated disparity in myopia and emmetropia.

Sensorimotor coupling in healthy humans is demonstrated by the higher accuracy of visually tracking intrinsically-rather than extrinsically-generated hand movements in the fronto-parallel plane. It is unknown whether this coupling also facilitates vergence eye movements for tracking objects in depth, or can overcome symmetric or asymmetric binocular visual impairments. Human observers were therefore asked to track with their gaze a target moving horizontally or in depth. The movement of the target was either directly controlled by the observer's hand or followed hand movements executed by the observer in a previous trial. Visual impairments were simulated by blurring stimuli independently in each eye. Accuracy was higher for self-generated movements in all conditions, demonstrating that motor signals are employed by the oculomotor system to improve the accuracy of vergence as well as horizontal eye movements. Asymmetric monocular blur affected horizontal tracking less than symmetric binocular blur, but impaired tracking in depth as much as binocular blur. There was a critical blur level up to which pursuit and vergence eye movements maintained tracking accuracy independent of blur level. Hand-eye coordination may therefore help compensate for functional deficits associated with eye disease and may be employed to augment visual impairment rehabilitation.

Three-dimensional binocular eye-hand coordination in normal vision and with simulated visual impairment.

Humans exhibit spatial biases when grasping objects. These biases may be due to actors attempting to shorten their reaching movements and therefore minimize energy expenditures. An alternative explanation could be that they arise from actors attempting to minimize the portion of a grasped object occluded from view by the hand. We reanalyze data from a recent study, in which a key condition decouples these two competing hypotheses. The analysis reveals that object visibility, not energy expenditure, most likely accounts for spatial biases observed in human grasping.

Object Visibility, Not Energy Expenditure, Accounts For Spatial Biases in Human Grasp Selection.

The human visual system is foveated: we can see fine spatial details in central vision, whereas resolution is poor in our peripheral visual field, and this loss of resolution follows an approximately logarithmic decrease. Additionally, our brain organizes visual input in polar coordinates. Therefore, the image projection occurring between retina and primary visual cortex can be mathematically described by the log-polar transform. Here, we test and model how this space-variant visual processing affects how we process binocular disparity, a key component of human depth perception. We observe that the fovea preferentially processes disparities at fine spatial scales, whereas the visual periphery is tuned for coarse spatial scales, in line with the naturally occurring distributions of depths and disparities in the real-world. We further show that the visual system integrates disparity information across the visual field, in a near-optimal fashion. We develop a foveated, log-polar model that mimics the processing of depth information in primary visual cortex and that can process disparity directly in the cortical domain representation. This model takes real images as input and recreates the observed topography of human disparity sensitivity. Our findings support the notion that our foveated, binocular visual system has been moulded by the statistics of our visual environment.

Near-optimal combination of disparity across a log-polar scaled visual field.

We rarely experience difficulty picking up objects, yet of all potential contact points on the surface, only a small proportion yield effective grasps. Here, we present extensive behavioral data alongside a normative model that correctly predicts human precision grasping of unfamiliar 3D objects. We tracked participants' forefinger and thumb as they picked up objects of 10 wood and brass cubes configured to tease apart effects of shape, weight, orientation, and mass distribution. Grasps were highly systematic and consistent across repetitions and participants. We employed these data to construct a model which combines five cost functions related to force closure, torque, natural grasp axis, grasp aperture, and visibility. Even without free parameters, the model predicts individual grasps almost as well as different individuals predict one another's, but fitting weights reveals the relative importance of the different constraints. The model also accurately predicts human grasps on novel 3D-printed objects with more naturalistic geometries and is robust to perturbations in its key parameters. Together, the findings provide a unified account of how we successfully grasp objects of different 3D shape, orientation, mass, and mass distribution.

Predicting precision grip grasp locations on three-dimensional objects.

A Model for Neural Network Modeling in Neuroscience.

Strabismus is a prevalent impairment of binocular alignment that is associated with a spectrum of perceptual deficits and social disadvantages. Current treatments for strabismus involve ocular alignment through surgical or optical methods and may include vision therapy exercises. In the present study, we explore the potential of real-time dichoptic visual feedback that may be used to quantify and manipulate interocular alignment. A gaze-contingent ring was presented independently to each eye of 11 normally-sighted observers as they fixated a target dot presented only to their dominant eye. Their task was to center the rings within 2° of the target for at least 1 s, with feedback provided by the sizes of the rings. By offsetting the ring in the non-dominant eye temporally or nasally, this task required convergence or divergence, respectively, of the non-dominant eye. Eight of 11 observers attained 5° asymmetric convergence and 3 of 11 attained 3° asymmetric divergence. The results suggest that real-time gaze-contingent feedback may be used to quantify and transiently simulate strabismus and holds promise as a method to augment existing therapies for oculomotor alignment disorders.

A dichoptic feedback-based oculomotor training method to manipulate interocular alignment.

How humans visually select where to grasp objects is determined by the physical object properties (e.g., size, shape, weight), the degrees of freedom of the arm and hand, as well as the task to be performed. We recently demonstrated that human grasps are near-optimal with respect to a weighted combination of different cost functions that make grasps uncomfortable, unstable, or impossible, e.g., due to unnatural grasp apertures or large torques. Here, we ask whether humans can consciously access these rules. We test if humans can explicitly judge grasp quality derived from rules regarding grasp size, orientation, torque, and visibility. More specifically, we test if grasp quality can be inferred (i) by using visual cues and motor imagery alone, (ii) from watching grasps executed by others, and (iii) through performing grasps, i.e., receiving visual, proprioceptive and haptic feedback. Stimuli were novel objects made of 10 cubes of brass and wood (side length 2.5 cm) in various configurations. On each object, one near-optimal and one sub-optimal grasp were selected based on one cost function (e.g., torque), while the other constraints (grasp size, orientation, and visibility) were kept approximately constant or counterbalanced. Participants were visually cued to the location of the selected grasps on each object and verbally reported which of the two grasps was best. Across three experiments, participants were required to either (i) passively view the static objects and imagine executing the two competing grasps, (ii) passively view videos of other participants grasping the objects, or (iii) actively grasp the objects themselves. Our results show that, for a majority of tested objects, participants could already judge grasp optimality from simply viewing the objects and imagining to grasp them, but were significantly better in the video and grasping session. These findings suggest that humans can determine grasp quality even without performing the grasp-perhaps through motor imagery-and can further refine their understanding of how to correctly grasp an object through sensorimotor feedback but also by passively viewing others grasp objects.

Humans Can Visually Judge Grasp Quality and Refine Their Judgments Through Visual and Haptic Feedback.

How humans visually select where to grasp an object depends on many factors, including grasp stability and preferred grasp configuration. We examined how endpoints are selected when these two factors are brought into conflict: Do people favor stable grasps or do they prefer their natural grasp configurations? Participants reached to grasp one of three cuboids oriented so that its two corners were either aligned with, or rotated away from, each individual's natural grasp axis (NGA). All objects were made of brass (mass: 420 g), but the surfaces of their sides were manipulated to alter friction: ) all-brass, ) two opposing sides covered with wood, and the other two remained of brass, or ) two opposing sides covered with sandpaper, and the two remaining brass sides smeared with Vaseline. Grasps were evaluated as either clockwise (thumb to the left of finger in frontal plane) or counterclockwise of the NGA. Grasp endpoints depended on both object orientation and surface material. For the all-brass object, grasps were bimodally distributed in the NGA-aligned condition but predominantly clockwise in the NGA-unaligned condition. These data reflected participants' natural grasp configuration independently of surface material. When grasping objects with different surface materials, endpoint selection changed: Participants sacrificed their usual grasp configuration to choose the more stable object sides. A model in which surface material shifts participants' preferred grip angle proportionally to the perceived friction of the surfaces accounts for our results. Our findings demonstrate that a stable grasp is more important than a biomechanically comfortable grasp configuration. When grasping an object, humans can place their fingers at several positions on its surface. The selection of these endpoints depends on many factors, with two of the most important being grasp stability and grasp configuration. We put these two factors in conflict and examine which is considered more important. Our results highlight that humans are not reluctant to adopt unusual grasp configurations to satisfy grasp stability.

Friction is preferred over grasp configuration in precision grip grasping.

Shape is a defining feature of objects, and human observers can effortlessly compare shapes to determine how similar they are. Yet, to date, no image-computable model can predict how visually similar or different shapes appear. Such a model would be an invaluable tool for neuroscientists and could provide insights into computations underlying human shape perception. To address this need, we developed a model ('ShapeComp'), based on over 100 shape features (e.g., area, compactness, Fourier descriptors). When trained to capture the variance in a database of >25,000 animal silhouettes, ShapeComp accurately predicts human shape similarity judgments between pairs of shapes without fitting any parameters to human data. To test the model, we created carefully selected arrays of complex novel shapes using a Generative Adversarial Network trained on the animal silhouettes, which we presented to observers in a wide range of tasks. Our findings show that incorporating multiple ShapeComp dimensions facilitates the prediction of human shape similarity across a small number of shapes, and also captures much of the variance in the multiple arrangements of many shapes. ShapeComp outperforms both conventional pixel-based metrics and state-of-the-art convolutional neural networks, and can also be used to generate perceptually uniform stimulus sets, making it a powerful tool for investigating shape and object representations in the human brain.

An image-computable model of human visual shape similarity.

Sitting in a static railway carriage can produce illusory self-motion if the train on an adjoining track moves off. While our visual system registers motion, vestibular signals indicate that we are stationary. The brain is faced with a difficult challenge: is there a single cause of sensations (I am moving) or two causes (I am static, another train is moving)? If a single cause, integrating signals produces a more precise estimate of self-motion, but if not, one cue should be ignored. In many cases, this process of causal inference works without error, but how does the brain achieve it? Electrophysiological recordings show that the macaque medial superior temporal area contains many neurons that encode combinations of vestibular and visual motion cues. Some respond best to vestibular and visual motion in the same direction ("congruent" neurons), while others prefer opposing directions ("opposite" neurons). Congruent neurons could underlie cue integration, but the function of opposite neurons remains a puzzle. Here, we seek to explain this computational arrangement by training a neural network model to solve causal inference for motion estimation. Like biological systems, the model develops congruent and opposite units and recapitulates known behavioral and neurophysiological observations. We show that all units (both congruent and opposite) contribute to motion estimation. Importantly, however, it is the balance between their activity that distinguishes whether visual and vestibular cues should be integrated or separated. This explains the computational purpose of puzzling neural representations and shows how a relatively simple feedforward network can solve causal inference.

How multisensory neurons solve causal inference.

The discovery of mental rotation was one of the most significant landmarks in experimental psychology, leading to the ongoing assumption that to visually compare objects from different three-dimensional viewpoints, we use explicit internal simulations of object rotations, to 'mentally adjust' one object until it matches the other. These rotations are thought to be performed on three-dimensional representations of the object, by literal analogy to physical rotations. In particular, it is thought that an imagined object is continuously adjusted at a constant three-dimensional angular rotation rate from its initial orientation to the final orientation through all intervening viewpoints. While qualitative theories have tried to account for this phenomenon, to date there has been no explicit, image-computable model of the underlying processes. As a result, there is no quantitative account of why some object viewpoints appear more similar to one another than others when the three-dimensional angular difference between them is the same. We reasoned that the specific pattern of non-uniformities in the perception of viewpoints can reveal the visual computations underlying mental rotation. We therefore compared human viewpoint perception with a model based on the kind of two-dimensional 'optical flow' computations that are thought to underlie motion perception in biological vision, finding that the model reproduces the specific errors that participants make. This suggests that mental rotation involves simulating the two-dimensional retinal image change that would occur when rotating objects. When we compare objects, we do not do so in a distal three-dimensional representation as previously assumed, but by measuring how much the proximal stimulus would change if we watched the object rotate, capturing perspectival appearance changes.

Mental object rotation based on two-dimensional visual representations.

[This corrects the article DOI: 10.3389/fnins.2020.591898.].

Guido Maiello

Department of Experimental Psychology

Justus Liebig University Giessen

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Guido Maiello

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Experimental Psychology

Justus Liebig University Giessen

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Department of Experimental Psychology