Biology subjects

Paulun, V. C.

Publications and source records attributed to Paulun, V. C..

5 recordsLinked to original sources

bioRxiv2023

Efficient Visual Heuristics in the Perception of Physical Object Properties

A key to interacting with the physical world is the ability to visually infer object properties, like elasticity, allowing us to anticipate object behavior. Such perceptual inferences continue to challenge AI systems, highlighting the complexity of the underlying computations. How does the human brain solve this task? Here, we propose a resource-rational model based on learned statistics of object motion to explain how humans judge elasticity. We created 100,000 physics-based simulations of bouncing cubes with different elasticities and found that even tiny changes in initial conditions (e.g., orientation) yield starkly different trajectories. Yet, across these simulations, we identified 23 motion features that capture natural variations in elasticity. Although a weighted combination of these features reliably predicts physical elasticity, surprisingly, humans do not seem to employ cue combination when judging elasticity. Instead, they switch between different cues. A series of experiments designed to carefully tease apart several competing heuristics, suggests that observers switch between different computationally efficient yet informative cues depending on the information available in the stimulus.

neuroscience↗

bioRxiv2023

Distinct neural components of visually guided grasping during planning and execution

Selecting suitable grasps on three-dimensional objects is a challenging visuomotor computation, which involves combining information about an object (e.g., its shape, size, and mass) with information about the actors body (e.g., the optimal grasp aperture and hand posture for comfortable manipulation). Here we used functional magnetic resonance imaging to investigate brain networks associated with these distinct aspects during grasp planning and execution. Human participants viewed and then executed preselected grasps on L-shaped objects made of wood and/or brass. By leveraging a computational approach that accurately predicts human grasp locations, we selected grasp points that disentangled the role of multiple grasp-relevant factors: grasp axis, grasp size, and object mass. Representational Similarity Analysis revealed that grasp axis was encoded along dorsal-stream regions during grasp planning. Grasp size was first encoded in ventral-stream areas during grasp planning, then in premotor regions during grasp execution. Object mass was encoded in ventral-stream and (pre)motor regions only during grasp execution. Premotor regions further encoded visual predictions of grasp comfort, whereas the ventral stream encoded grasp comfort during execution, suggesting its involvement in haptic evaluation. These shifts in neural representations thus capture the sensorimotor transformations that allow humans to grasp objects. Significance StatementGrasping requires integrating object properties with constraints on hand and arm postures. Using a computational approach that accurately predicts human grasp locations by combining such constraints, we selected grasps on objects that disentangled the relative contributions of object mass, grasp size, and grasp axis during grasp planning and execution in a neuroimaging study. Our findings reveal a greater role of dorsal-stream visuomotor areas during grasp planning, and surprisingly, increasing ventral stream engagement during execution. We propose that during planning, visuomotor representations initially encode grasp axis and size. Perceptual representations of object material properties become more relevant instead as the hand approaches the object and motor programs are refined with estimates of the grip forces required to successfully lift the object.

neuroscience↗

bioRxiv2020

Humans can visually judge grasp quality and refine their judgments through visual and haptic feedback

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.

neuroscience↗

bioRxiv2018

Object visibility, not energy expenditure, accounts for spatial biases in human grasp selection

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.

neuroscience↗

bioRxiv2018

How humans grasp three-dimensional objects

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 anothers, 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 SummaryA 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.

neuroscience↗