Lina K. Klein

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 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 could either (i) passively view the static objects, (ii) passively view videos of other participants grasping the objects, or (iii) actively grasp the objects themselves. Our results show that participants could already judge grasp optimality from simply viewing the objects, 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.

Most neuroimaging studies of both grasping and object recognition have focused on simple objects made from homogenous materials. However, in behavioral experiments with complex objects, we have shown that participants can use visual object shape and material distribution to select optimal grasps based on a combination of factors: natural grasp axis, grasp aperture, the object’s overall weight, and mass distribution. Given that visual perception of object shape and material distribution are putatively a ventral-stream function; whereas, grasping is putatively a dorsal-stream function, we used functional magnetic resonance imaging (fMRI) to characterize the role of brain regions in both streams during grasping of multi-material objects. We recorded brain activation while participants grasped and lifted objects made of wooden and brass cubes at preselected grasp points. To tease apart the different components of grasp selection, grasps were optimized for either torque, grasp aperture, grasp axis, or a combination of these. For example, some grasps were optimal with respect to minimizing torque, but suboptimal with respect to the aperture. Within visual and visuomotor regions of interest, we examined activation levels and patterns (with representational similarity analysis) to infer neural coding of grasp attributes. When participants grasped objects in configurations with high torque – that is when their grips had to counteract object rotation due to gravity – versus low torque, activation was observed in the grasp-selective anterior intraparietal sulcus, aIPS (in the lateral dorsal stream), the reach-selective superior parieto-occipital sulcus, SPOC (in the medial dorsal stream), and the object-selective lateral occipital cortex, LOC (in the ventral stream). These results suggest that when materials and their distribution are relevant for grasping, both visual streams are recruited.

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 re-analyze 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.

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. Author Summary: A model based on extensive behavioral data unifies the varied and fragmented literature on human grasp selection by correctly predicting human grasps across a wide variety of conditions.