The recent advancements in mixed reality (MR) research are significantly enhancing our understanding of group dynamics, user presence, and interaction modalities. A notable trend is the integration of passive sensing and sociometry to analyze collaborative group behavior in MR environments. This approach leverages the rich sensory capabilities of MR headsets to capture data on conversation, shared attention, and proximity, which are then processed using social network analysis techniques. The findings suggest that balanced participation in various types of interactions leads to higher group cohesion, enabling real-time assessments that can enhance collaborative experiences.

Another key development is the exploration of the impact of interactions on presence and reaction time in MR. Studies are increasingly focusing on objective metrics like reaction time as proxies for presence, particularly in the context of different interaction scenarios and tasks. The correlation between presence scores and reaction times underscores the importance of interaction design in enhancing user immersion.

Additionally, research is delving into the effects of distractions on user experience in MR, with a particular emphasis on cognitive load, reaction time, and Break in Presence (BIP). Theoretical models are being developed to understand how different types of distractions influence these constructs, revealing that incongruent distractions significantly increase cognitive load and BIP frequency, while also slowing reaction times.

Lastly, there is a growing interest in leveraging machine learning models to predict user selection intention in real-time using gaze data. Bayesian-based models are proving particularly effective, offering high accuracy and enabling more comfortable and accurate interactions compared to traditional techniques.

Noteworthy Papers:

• GroupBeaMR: A framework for analyzing group behavior in MR using passive sensing and sociometry, demonstrating the correlation between balanced interaction and higher group cohesion.
• Tap into Reality: A study exploring the correlation between presence and reaction time in MR, highlighting the impact of interaction scenarios on user immersion.
• Reaction Time as a Proxy for Presence in MR with Distraction: A theoretical model and study examining the effects of distractions on presence, cognitive load, and reaction time in MR.
• Predicting Selection Intention in Real-Time with Bayesian-based ML Model in Unimodal Gaze Interaction: A Bayesian-based machine learning model for predicting user selection intention using gaze data, achieving high accuracy in real-time inference.