Lungarella Max, Pegors Teresa, Bulwinkle Daniel, Sporns Olaf
Department of Mechano-Informatics, School of Information Science and Technology, University of Tokyo, 113-8656 Tokyo, Japan.
Neuroinformatics. 2005;3(3):243-62. doi: 10.1385/NI:3:3:243.
Embodied agents (organisms and robots) are situated in specific environments sampled by their sensors and within which they carry out motor activity. Their control architectures or nervous systems attend to and process streams of sensory stimulation, and ultimately generate sequences of motor actions, which in turn affect the selection of information. Thus, sensory input and motor activity are continuously and dynamically coupled with the surrounding environment. In this article, we propose that the ability of embodied agents to actively structure their sensory input and to generate statistical regularities represents a major functional rationale for the dynamic coupling between sensory and motor systems. Statistical regularities in the multimodal sensory data relayed to the brain are critical for enabling appropriate developmental processes, perceptual categorization, adaptation, and learning. To characterize the informational structure of sensory and motor data, we introduce and illustrate a set of univariate and multivariate statistical measures (available in an accompanying Matlab toolbox). We show how such measures can be used to quantify the information structure in sensory and motor channels of a robot capable of saliency-based attentional behavior, and discuss their potential importance for understanding sensorimotor coordination in organisms and for robot design.
具身智能体（生物体和机器人）处于由其传感器采样的特定环境中，并在其中进行运动活动。它们的控制架构或神经系统关注并处理感觉刺激流，并最终生成运动动作序列，而这些动作序列又会影响信息的选择。因此，感觉输入和运动活动与周围环境持续且动态地耦合。在本文中，我们提出具身智能体主动构建其感觉输入并生成统计规律的能力，是感觉系统和运动系统之间动态耦合的主要功能原理。传递到大脑的多模态感觉数据中的统计规律，对于实现适当的发育过程、感知分类、适应和学习至关重要。为了表征感觉和运动数据的信息结构，我们引入并说明了一组单变量和多变量统计量度（可在随附的Matlab工具箱中获取）。我们展示了如何使用这些量度来量化具有基于显著性的注意力行为的机器人的感觉和运动通道中的信息结构，并讨论了它们对于理解生物体中的感觉运动协调以及机器人设计的潜在重要性。
