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Guido Maiello, Marcel Schepko, Lina K Klein, Vivian C Paulun, Roland W Fleming; Visual Judgements of Grasp Optimality. Journal of Vision 2019;19(10):173b. doi: https://doi.org/10.1167/19.10.173b.
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© ARVO (1962-2015); The Authors (2016-present)
Humans strongly rely on vision to guide grasping. Visual grasp selection is highly systematic and consistent across repetitions and participants, suggesting that humans employ a common set of constraints when visually selecting grasps. We formalized these constraints as a set of grasp-cost functions related to torque, grasp axis, grasp aperture, and object visibility, which we have shown predict grasping behavior with striking fidelity. Here, we test if humans can explicitly estimate grasp optimality derived from these grasp-cost functions. We additionally ask whether vision alone is sufficient to compute grasp optimality, or whether sensorimotor feedback is required to link vision to action selection. Stimuli were novel objects made of 10 cubes of brass and wood (side length 2.5 cm) in various configurations. On each object, an optimal and a sub-optimal grasp were selected based on one of the cost functions, while cost for the other constraints was kept approximately constant or counterbalanced. Participants were visually cued as to the location of the grasps on each object via colored markers. In a vision-only session, participants were required to judge which of the two grasps they believed to be better, without ever having grasped the object. In a vision-plus-grasp session, participants were required to attempt both grasps on each object, and again indicate which of the two grasps they judged to be better. Participants (N=11) were already able to judge grasp optimality above chance in the vision-only session (65+/−13% correct, p= 0.0035). Additionally, participants were significantly better at judging grasp optimality in the vision-plus-grasp session (77+/−7% correct, p=0.0081). Together, these findings show that humans can consciously access the visuomotor computations underlying grasp selection, and highlight the fundamental role of sensorimotor feedback in linking visual perception to motor control.
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