人体阻抗调制以改善视触觉感知
Human Impedance Modulation to Improve Visuo-Haptic Perception
AI总结：
研究通过机器人目标跟踪实验发现人体肌肉协同收缩会差异化响应视觉与触觉噪声，并提出视触觉信息与努力优化的 OIE 模型解释该主动感知机制。
摘要：
人类通过激活肌肉来塑造与环境的机械交互，但他们能否利用这一控制机制来最佳地感知环境？我们研究了参与者在使用机器人接口跟踪随机移动目标时，如何根据视觉和触觉信息调整肌肉激活。结果显示这些感觉模态具有差异化效应：参与者的肌肉协同收缩随触觉噪声增加而增强，随视觉噪声增加而减弱，这与先前结果明显矛盾。当考虑肌肉的类弹簧力学特性时，这些结果可以得到解释并与先前发现相协调，即刚度随协同收缩增加，从而调节运动引导。增强协同收缩以更紧密地遵循运动计划，有利于依赖准确的视觉信息而非触觉信息；而降低协同收缩则可避免注入视觉噪声，并依赖准确的触觉信息。我们将这种主动感知机制表述为视触觉信息与努力的优化。该 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，并可用于优化人机交互。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
