Cheng Xiaoxiao, Shen Shixian, Ivanova Ekaterina, Carboni Gerolamo, Takagi Atsushi, Burdet Etienne
Department of Bioengineering, Imperial College of Science Technology and Medicine, London, United Kingdom.
Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, United Kingdom.
PLoS Comput Biol. 2025 May 9;21(5):e1013042. doi: 10.1371/journal.pcbi.1013042. eCollection 2025 May.
Humans activate muscles to shape the mechanical interaction with their environment, but can they harness this control mechanism to best sense the environment? We investigated how participants adapt their muscle activation to visual and haptic information when tracking a randomly moving target with a robotic interface. The results exhibit a differentiated effect of these sensory modalities, where participants' muscle coactivation increases with the haptic noise and decreases with the visual noise, in apparent contradiction to previous results. These results can be explained when considering muscle spring-like mechanics, where stiffness increases with coactivation to regulate motion guidance. Increasing coactivation to more closely follow the motion plan favors accurate visual over haptic information, while decreasing it filters visual noise and relies more on accurate haptic information. We formulated this active sensing mechanism as the optimization of visuo-haptic information and effort. This optimal information and effort (OIE) model can explain the adaptation of muscle activity to unimodal and multimodal sensory information when interacting with fixed or dynamic environments, or with another human, and can be used to optimize human-robot interaction.
人类通过激活肌肉来塑造与周围环境的机械交互，但他们能否利用这种控制机制来更好地感知环境呢？我们研究了参与者在使用机器人界面跟踪随机移动目标时，是如何根据视觉和触觉信息来调整肌肉激活的。结果显示了这些感觉模态的不同影响，即参与者的肌肉共同激活随着触觉噪声的增加而增加，随着视觉噪声的增加而减少，这明显与之前的结果相矛盾。当考虑到肌肉类似弹簧的力学特性时，这些结果可以得到解释，即刚度随着共同激活的增加而增加，以调节运动引导。增加共同激活以更紧密地遵循运动计划有利于准确的视觉信息而非触觉信息，而减少共同激活则可以过滤视觉噪声并更多地依赖准确的触觉信息。我们将这种主动感知机制表述为视觉 - 触觉信息与努力的优化。这种最优信息与努力（OIE）模型可以解释在与固定或动态环境、或与另一个人交互时，肌肉活动对单模态和多模态感觉信息的适应性，并且可用于优化人机交互。
