Search

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.

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: 1) all-brass, 2) two opposing sides covered with wood, while the other two remained of brass, or 3) 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.NEW & NOTEWORTHYWhen 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 in order to satisfy grasp stability.

We rarely experience difficulty picking up objects, yet of all potential grasp points on an object’s surface, only a small proportion yield stable, comfortable grasps. Here, we present extensive behavioral data alongside a computational 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. The model combines five cost functions related to force closure, torque, natural grasp axis, grasp aperture, and visibility. Even without free parameters, we find that the model predicts human grasps with striking fidelity: indeed, it predicts individual grasps almost as well as different individuals predict one another’s. Adding fittable weights to the model reveals the relative importance of the different constraints: the combination of force closure, hand posture, and grasp size explains most of human grasping behavior, while our participants cared surprisingly little about minimizing torque and optimizing object visibility. Together, these findings provide a unified account of how we derive effective grasps from objects’ 3D shape and material properties to interact with them successfully. Significance Statement Working out how we pick up and interact with objects effectively is one of the most important challenges in behavioral science. Of all the potential contact points on an object’s surface, only a small proportion yield effective grasps. Despite this, we rarely experience any difficulty choosing where and how to pick objects up. Here, we present a computational model that unifies the varied and fragmented literature on human grasp selection. We find that the model correctly predicts human grasps across a wide variety of conditions, taking into account the object’s 3D shape, material properties and orientation.