Methods for quantifying the informational structure of sensory and motor data
Methods for quantifying the informational structure of sensory and motor data
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DOI：
10.1385/ni:3:3:243
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发表时间：
2005-01-01
期刊：
影响因子：
3
通讯作者：
Sporns, O
中科院分区：
文献类型：
--
作者：
Lungarella, M;Pegors, T;Sporns, O
具体代理(生物体和机器人)位于由其传感器采样的特定环境中，并在其中进行运动活动。它们的控制结构或神经系统负责并处理感觉刺激流，最终产生一系列运动动作，进而影响信息的选择。因此，感觉输入和运动活动与周围环境持续、动态地耦合。在这篇文章中，我们提出，体验式代理主动构建其感觉输入并生成统计规则的能力代表了感觉和运动系统之间动态耦合的主要功能基础。传递给大脑的多模式感觉数据中的统计规则对于实现适当的发育过程、知觉分类、适应和学习至关重要。为了描述感觉和运动数据的信息结构，我们介绍和说明了一组单变量和多变量的统计测量(可在附带的MatLab工具箱中获得)。我们展示了如何使用这种测量方法来量化能够基于显著注意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感觉运动协调和机器人设计的潜在重要性。
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 catergorization, 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.