Traditional HCI interaction models assume a single monolithic interface and a stable sensorimotor loop. These models fit poorly with cross-device (XVA) and ubiquitous analytics (UA), where interactive data sensemaking unfolds across multiple devices, artifacts, and people in disparate settings from the office to the factory floor. In this paper, we show how interaction in ubiquitous analytics can be modeled using distributed cognition as propagation of representational state across substrates -- minds, speech, bodies, artifacts, and devices -- rather than as traffic through a single interface. On this basis we introduce input and output channels as generalizations of the visual channels from data visualization: just as visual channels carry data through properties of the visual substrate, input and output channels carry representational state through substrates whose availability, suitability, and preferability depend on context. We demonstrate the channels and substrates framework by reanalyzing several ubiquitous, immersive, and situated analytics systems.

翻译：传统人机交互模型假设单一的整体界面和稳定的感知-运动回路，这种模型难以适应跨设备分析（XVA）与泛在分析（UA）——在这类场景中，交互式数据意义构建跨越多个设备、人工制品和人员，在从办公室到工厂车间的不同环境中展开。本文展示如何通过分布式认知理论对泛在分析中的交互行为建模：将交互视为表征状态在基质（包括心智、语言、身体、人工制品和设备）间的传播，而非通过单一界面的信息流转。基于此，我们引入输入通道与输出通道作为数据可视化中视觉通道的泛化——正如视觉通道通过视觉基质的属性传递数据，输入与输出通道通过基质传递表征状态，且这些基质的可用性、适配性与偏好性取决于具体情境。通过重新分析多个泛在式、沉浸式与情境式分析系统，我们验证了通道与基质框架的有效性。