What Sensors See, What People Feel: An Exploratory Study of Subjective Collaboration Perception in Mixed Reality

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.