Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis

2024-12-19

AI Summary

This study investigates the efficacy of collaborative visual graph analysis in mixed reality (MR), examining how task complexity moderates performance differences among ad hoc pairs, individuals, and nominal pairs (a virtual collaboration baseline). Method: A cross-lingual, cross-national experiment with 72 participants quantified collaboration gains via task-instance complexity—a novel operationalization—while controlling for confounding social factors. Contribution/Results: Ad hoc pairs achieved 4.6% higher accuracy than individuals but incurred a 1.46× time cost; this trade-off intensified with complexity, albeit with non-monotonic exceptions. Critically, MR does not inherently enhance collaborative quality. The work establishes a complexity-driven paradigm for evaluating collaborative benefit in immersive environments and lays the foundation for temporal modeling of collaborative behavior in MR.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Web Mining and Content Analysis: Web data visualization

Abstract

Problem solving is a composite cognitive process, invoking a number of systems and subsystems, such as perception and memory. Individuals may form collectives to solve a given problem together, in collaboration, especially when complexity is thought to be high. To determine if and when collaborative problem solving is desired, we must quantify collaboration first. For this, we investigate the practical virtue of collaborative problem solving. Using visual graph analysis, we perform a study with 72 participants in two countries and three languages. We compare ad hoc pairs to individuals and nominal pairs, solving two different tasks on graphs in visuospatial mixed reality. The average collaborating pair does not outdo its nominal counterpart, but it does have a significant trade-off against the individual: an ad hoc pair uses 1.46 more time to achieve 4.6 higher accuracy. We also use the concept of task instance complexity to quantify differences in complexity. As task instance complexity increases, these differences largely scale, though with two notable exceptions. With this study we show the importance of using nominal groups as benchmark in collaborative virtual environments research. We conclude that a mixed reality environment does not automatically imply superior collaboration.

Problem

• Mixed Reality Environment
• Collaborative Problem Solving
• Task Difficulty

Innovation

• Mixed Reality Environment
• Cross-cultural Multilingual Collaboration
• Problem-solving Effectiveness