SCEIMA: Social Coordination Evaluation through Integrated Model Analysis

Computational models are increasingly used as interactive partners in studies of human coordination, yet it remains unclear whether observed differences in human behavior reflect properties of the models themselves, changes in human behavior elicited by such artificial partners, or both. We introduce SCEIMA (Social Coordination Evaluation through Integrated Model Analysis), a two-stage framework designed to disentangle human-specific, model-specific, and interaction-driven contributions to coordination in human–machine interaction paradigms. In the empirical stage, human participants perform a coordination task with both human partners and computational models, establishing reference human–human and human–model interaction patterns. In the analytical stage, the same models are paired with one another and optimized through simulations to reproduce empirical coordination metrics. Comparing human–human, human–model, and simulated model–model interactions reveals whether coordination differences arise from intrinsic model dynamics, from human adaptation to artificial partners, or from their interaction. SCEIMA treats computational models as contrastive instruments whose capacity to elicit and reproduce human behavior can be systematically evaluated. We illustrate the framework with two distinct case-studies, a sensorimotor synchronization task and a conversational turn-taking task, showing how distinct outcome patterns diagnose the sources of coordination differences. By providing a principled methodological framework for evaluating interactive computational models, SCEIMA improves interpretability in human–machine interaction research and informs the design of artificial agents that coordinate with humans more naturally and responsively.