Teamwork as Linear Interpersonal Dynamics

2025-09-10

AI Summary

Existing methods for measuring team interpersonal dynamics—such as Cross-Recurrence Quantification Analysis (CRQA), Granger causality, and transfer entropy—are largely confined to either synchrony or unidirectional influence, lacking a unified representation that integrates psychological interpretability with behavioral system relevance. Method: We propose the “contextual matrix”—a linear dynamical model that jointly captures inter-individual behavioral synchrony and directed influence within a single, decomposable framework; its parameters map directly onto psychologically meaningful collaboration features. The approach integrates sequential Bayesian inference with eye-tracking analysis. Results: On synthetic data, the model accurately recovers ground-truth dynamics. In human collaborative experiments, it robustly identifies task-dependent dynamic differences, significantly predicts behavioral performance (p < 0.001), and aligns theoretically with established metrics. This framework establishes a novel, interpretable, and empirically verifiable paradigm for modeling team collaboration mechanisms.

Technology Category

• Game Theory and Economic Paradigms: Coordination and Collaboration
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Teamwork, Team formation

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Successful teamwork depends on interpersonal dynamics, the ways in which individuals coordinate, influence, and adapt to one another over time. Existing measures of interpersonal dynamics, such as CRQA, correlation, Granger causality, and transfer entropy, typically capture only a single dimension: either the synchrony/coordination or the direction of influence between individuals. What is missing is a psychologically meaningful representation that unifies these dimensions and varies systematically with behavior. We propose the context matrix as one such representation. The context matrix is the transition matrix in a linear dynamical system, with entries specifying how much each individual's current behavior is attributable to their own versus every other group member's past behaviors. Its values can be distilled into psychologically interpretable summary features of synchrony and directional influence. Evidence for the context matrix as psychologically meaningful is provided in two steps. First, we develop a sequential Bayesian model that infers context matrices from timeseries data and show that it accurately recovers them in noisy simulations. Second, applying the model to human eyetracking data, we show that summary features of the inferred context matrices capture expected task-based differences in interpersonal dynamics (or lack thereof), predict task accuracy in psychologically reasonable ways, and show some correspondence with existing measures (CRQA and Granger causality). We conclude by situating the context matrix within a broader agenda for modeling interpersonal dynamics.

Problem

• Unifying synchrony and directional influence in teamwork dynamics
• Providing psychologically meaningful representation of interpersonal coordination
• Developing a linear dynamical system to model behavioral attributions

Innovation

• Linear dynamical system with context matrix
• Sequential Bayesian model for inference
• Psychologically interpretable synchrony and influence features