Mixed Reality (MR) enables rich, embodied collaboration; however, it is uncertain whether sensor- and system-logged behavioral signals capture how users experience that collaboration. This disconnect stems from a fundamental gap. Behavioral signals are observable and continuous, while collaboration is interpreted subjectively and shaped by internal states like presence, cognitive availability, and social awareness. Our core insight is that sensor signals serve as observable manifestations of subjective experiences in MR collaboration, and they can be captured through sensor data such as shared gaze, speech, spatial movement, and other system-logged performance metrics. We propose the Sensor-to-Subjective (S2S) Mapping Framework, a conceptual model that links observable interaction patterns to users' subjective perceptions of collaboration and internal cognitive states through sensor-based indicators and task performance metrics. To evaluate this model, we conducted an exploratory study with 48 participants across 12 MR groups engaged in a collaborative image-sorting task. Our findings show a correlation between sensed behavior and perceived collaboration, particularly through shared attention and proximity.

翻译：混合现实（MR）能够实现丰富且具身化的协作；然而，传感器与系统记录的行为信号是否能捕捉用户对该协作的体验尚不确定。这种脱节源于一个根本性差距：行为信号是可观测且连续的，而协作则被主观解读，并受到临场感、认知可用性和社会意识等内在状态的影响。我们的核心见解是，传感器信号可作为MR协作中主观体验的可观测表现，并且可以通过共享注视、语音、空间移动及其他系统记录的性能指标等传感器数据来捕捉。我们提出了传感器到主观（S2S）映射框架，这是一个概念模型，通过基于传感器的指标和任务性能指标，将可观测的交互模式与用户对协作的主观感知及内在认知状态联系起来。为了评估该模型，我们进行了一项探索性研究，招募48名参与者组成12个MR小组，共同完成一项协作图像分类任务。我们的研究结果表明，感知到的行为（尤其是通过共享注意力和空间邻近性）与感知到的协作之间存在相关性。