Fast prediction in marmoset reach-to-grasp movements for dynamic prey

Abstract

SummaryPrimates 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.