Summary
Primates have evolved sophisticated visually guided reaching behaviors for interacting with dynamic objects, such as insects during foraging(P. S. Archambault, Ferrari-Toniolo, & Battaglia-Mayer, 2011; Bicca-Marques, 1999; Ngo et al., 2022; Smith & Smith, 2013; Sustaita et al., 2013). Reaching control in dynamic natural conditions requires active prediction of the target’s future position to compensate for visuo-motor processing delays and enhance online movement adjustments(Catania, 2009; Desmurget & Grafton, 2000; Fujioka, Aihara, Sumiya, Aihara, & Hiryu, 2016; Merchant & Georgopoulos, 2006; Mischiati et al., 2015; R. Shadmehr, Smith, & Krakauer, 2010; Wolpert & Kawato, 1998). Past reaching research in non-human primates mainly focused on seated subjects engaged in repeated ballistic arm movements to either stationary targets, or targets that instantaneously change position during the movement(Philippe S. Archambault, Caminiti, & Battaglia-Mayer, 2009; Battaglia-Mayer et al., 2013; Dickey, Amit, & Hatsopoulos, 2013; Georgopoulos, Kalaska, Caminiti, & Massey, 1983; Georgopoulos, Kalaska, & Massey, 1981). However, those approaches impose task constraints that limit the natural dynamics of reaching. A recent field study in marmoset monkeys highlights predictive aspects of visually-guided reaching during insect prey capture among wild marmoset monkeys(Ngo et al., 2022). To examine the complementary dynamics of similar natural behavior within a laboratory context we developed an ecologically motivated unrestrained reach-to-grasp task involving live crickets. We used multiple high-speed video cameras to capture the movements of marmosets and crickets stereoscopically and applied machine vision algorithms for marker-free object and hand tracking. Contrary to estimates under traditional constrained reaching paradigms, we find that reaching for dynamic targets can operate at incredibly short visuo-motor delays around 80 milliseconds, rivaling the speeds that are typical of the oculomotor systems during closed-loop visual pursuit(Cloherty, Yates, Graf, DeAngelis, & Mitchell, 2020). Multivariate linear regression modeling of the kinematic relationships between the hand and cricket velocity revealed that predictions of the expected future location can compensate for visuo-motor delays during fast reaching. These results suggest a critical role of visual prediction facilitating online movement adjustments for dynamic prey.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵3 Lead Contact
First we have characterized hand grasping patterns in greater detail to reveal that in our task marmosets use a power grasp strategy in which variations in the timing of grasp aperture opening are related to the reaching speed. More so, we have included analyses to characterize marmoset reach kinematics with respect to pure pursuit, proportional navigation, and mixed strategy steering models that have been previously used to explain prey capture (Brighton & Taylor, 2019) (Mischiati et al., 2015). These analyses demonstrate that marmoset reach kinematics are more consistent with proportional navigation in which the lateral component of target velocity relative to the range vector connecting target to pursuer is matched by the lateral hand velocity. Although these pursuit strategies perform at explaining reach kinematics where there is no visuo-motor delay, their performance deteriorates once realistic visuo-motor delays are considered, which we estimate to range from 80 to 100 ms in our task. The predictive pursuit model performs better than reactive strategies once a realistic visuo-motor delay is taken into account. We have revised our manuscript to incorporate these new data analyses and discussion of them.
