Fast prediction in marmoset reach-to-grasp movements for dynamic prey.
Fast prediction in marmoset reach-to-grasp movements for dynamic prey.
复制标题
快速预测狨猴抓取动态猎物的动作。
DOI：
10.1016/j.cub.2023.05.032
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Mitchell,Jude
中科院分区：
文献类型：
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作者：
Shaw,Luke;Wang,KuanHong;Mitchell,Jude
灵长类动物在觅食过程中进化出了复杂的、视觉引导的伸展行为，以便在觅食过程中与动态对象(如昆虫)互动。1、2、3、4、5在动态自然条件下进行伸展控制需要主动预测目标的未来位置，以补偿视觉运动处理延迟并增强在线运动调整。6、7、8、9、10、11、12过去对非人类灵长类动物的研究主要集中在坐着的受试者进行重复的弹道手臂运动，以达到静止目标或在移动过程中瞬间改变位置的目标。13、14、15、16、17然而，这些方法施加了任务限制，限制了伸展的自然动力学。最近在绒猴身上进行的一项实地研究强调了野生绒猴在捕食昆虫过程中视觉引导伸展的预测性方面。5为了在实验室背景下检验类似自然行为的互补动力学，我们开发了一个涉及活蟋蟀的生态动机、不受限制的伸展抓取任务。我们使用了多个高速摄像机来立体捕捉常见的绒猴和蟋蟀的运动，并应用机器视觉算法进行无标记物体和手的跟踪。与传统受限到达范式下的估计相反，我们发现到达动态目标可以在约80ms的令人难以置信的短视觉运动延迟下操作，与闭环式视觉追逐期间典型的眼动系统的速度相当。18对手和板球速度之间的运动学关系的多元线性回归建模显示，对预期未来位置的预测可以补偿快速到达期间的视觉运动延迟。这些结果表明，视觉预测在促进动态猎物的在线运动调整方面发挥了关键作用。
Primates have evolved sophisticated, visually guided reaching behaviors for interacting with dynamic objects, such as insects, during foraging.1,2,3,4,5Reaching control in dynamic natural conditions requires active prediction of the target's future position to compensate for visuo-motor processing delays and to enhance online movement adjustments.6,7,8,9,10,11,12Past 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.13,14,15,16,17However, 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.5To 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 common marmosets (Callithrix jacchus) 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 ms, rivaling the speeds that are typical of the oculomotor systems during closed-loop visual pursuit.18Multivariate 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.