Proceedings of the Annual Meeting of the Cognitive Science Society

Effective collaboration requires accurate mental models of one's partners. Current AI agents, however, may not model humans the way humans model each other under real-time constraints, creating an asymmetry in human-AI teamwork. Prior research has focused on one-human-one-AI settings, leaving open how coordination changes when multiple humans work with multiple AI agents simultaneously. We developed Co-Overcooked, a four-player cooking game where teams of humans and LLM agents must coordinate in real time. In a within-subjects experiment (N=40), participants experienced four team compositions varying in human-to-AI ratio: four humans (H4A0), three humans with one AI (H3A1), two humans with two AIs (H2A2), and one human with three AIs (H1A3). Results revealed nonlinear patterns: H3A1 and H1A3 teams performed worse than pure AI teams, while H2A2 teams showed better performance through spontaneous one-human-one-AI pairing strategies. We identified expectation conflict, where multiple humans hold divergent models of a shared AI partner, as a coordination difficulty specific to hybrid teams, and propose cognitive ownership as a design principle for human-AI team configuration.