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Chessa, M.

Publications and source records attributed to Chessa, M..

2 recordsLinked to original sources

Does Precision Grip Research Extend to Unconstrained, Multidigit Grasping?

Most daily tasks require using our hands. Whether taking a sip from a glass or throwing a ball, we effortlessly select appropriate grasps. Yet, despite many possible hand configurations, most grasping research has focused on the finger-and-thumb precision grip. We thus questioned whether findings on precision grip hold under unconstrained grasping conditions. To test this, we compared how participants grasped 3D objects made of brass and wood, with both precision grip and unconstrained grasps. When unconstrained, participants rarely selected precision grips, favoring multi-digit grasps. Nevertheless, in both conditions, participants shifted grasps towards the objects center of mass and, when grasp factors conflicted, the variability in their selections increased, indicating greater uncertainty about the optimal strategy. Further, despite favoring multidigit grasps, participants consistently placed the thumb and index finger on the same positions on the objects, suggesting that in multidigit grasps, the additional fingers primarily provided support. Our findings thus reveal that object material affects unconstrained grasping similarly to precision grip and imply that previous precision grip research may extend to unconstrained, multidigit conditions. NEW & NOTEWORTHYMost grasping research focuses on two-digit precision grips, yet humans have more than two fingers. Here, we test whether previous precision grip findings apply to unconstrained grasping. We find that participants often use more than two digits when free to choose but consistently place thumb and index finger similarly on objects regardless of the number of fingers used. Our results thus highlight how the large body of precision grip literature can extend to multidigit grasping.

neuroscience↗

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

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 field 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 disparity sensitivity in man. Our findings support the notion that our foveated, binocular visual system has been moulded by the statistics of our visual environment.\n\nAuthor summaryWe investigate how humans perceive depth from binocular disparity at different spatial scales and across different regions of the visual field. We show that small changes in disparity-defined depth are detected best in central vision, whereas peripheral vision best captures the coarser structure of the environment. We also demonstrate that depth information extracted from different regions of the visual field is combined into a unified depth percept. We then construct an image-computable model of disparity processing that takes into account how our brain organizes the visual input at our retinae. The model operates directly in cortical image space, and neatly accounts for human depth perception across the visual field.

neuroscience↗