Human Impedance Modulation to Improve Visuo-Haptic PerceptionHuman Impedance Modulation to Improve Visuo-Haptic Perception

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 cocontraction increases with the haptic noise and decreases with the visual noise, in apparent contradiction to previous results. These results can be explained, and reconciled with previous findings, when considering muscle spring-like mechanics, where stiffness increases with cocontraction to regulate motion guidance. Increasing cocontraction to more closely follow the motion plan favors accurate visual over haptic information, while decreasing it avoids injecting visual noise and relies on accurate haptic information. We formulated this active sensing mechanism as the optimization of visuo-haptic information and effort. This 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. All the experiment data, processing codes and modelling codes could be found in this file.

人类通过激活肌肉来调控与周遭环境的机械交互，但能否借助这一控制机制实现最优的环境感知？本研究探讨了受试者在借助机器人交互接口追踪随机移动目标的过程中，如何调整肌肉激活模式以适配视觉与触觉（haptic）信息。研究结果显示两类感知模态存在差异化影响：受试者的肌肉共激活（muscle cocontraction）水平随触觉噪声升高而上升，随视觉噪声升高而下降，这与既往研究结论存在明显矛盾。若结合肌肉类弹簧力学特性展开分析，即可对上述结果作出合理解释，并使其与既往研究结论达成统一——该特性下肌肉刚度随共激活水平提升而升高，以此调控运动引导过程。提升共激活水平以更贴合运动规划，会使受试者更倾向于依赖精准的视觉信息而非触觉信息；而降低共激活水平则可避免引入视觉噪声，转而依托精准的触觉信息开展感知与交互。我们将这一主动感知（active sensing）机制建模为视触觉信息与生理付出的优化问题。该OIE模型可解释受试者在与固定/动态环境或其他人类进行交互时，肌肉活动如何适配单模态与多模态感知信息，同时可用于优化人机交互（human-robot interaction）系统。本研究的全部实验数据、处理代码与建模代码均可在此文件中获取。