cs.HC · 2609.05552 Copy arXiv ID · Sep 3, 2026 Save An Agent Model Abstraction for Human-AI Teaming Cognitive Coupling Authors: Kolitha Kottagaha W. M , Jos A. C. Bokhorst , Ben Gaffinet , Christos Emmanouilidis
Organizations: University of Groningen, 9700 AV, Groningen, The Netherlands · Luxembourg Institute of Science and Technology, Belval, Luxembourg
In Abstract Industrial environments increasingly rely on collaboration between humans and AI-enabled agents. Effective teamwork requires aligning how agents perceive situations, plan actions to pursue goals, and adapt to changing conditions, yet existing systems lack mechanisms for cross-agent cognitive processes coupling. This paper presents a conceptual cognitive agent model that formalises cognitive coupling through eight components: Input, Process, Output, State, Value, Memory, World Model, and Goal. The model abstracts how agents coordinate and co-regulate their cognitive cycles, providing a basis for analysing distributed cognition and designing cognitively interoperable human-AI systems.
More in this topic Human-AI collaboration studies how people and AI systems divide, coordinate, and adapt work to support decisions, creativity, and learning.
See all 445 papers → Also in Mar 19, 2026 · Eduardo Di Santi, Carla Florida Human-AI Interaction Human-AI Decision Making
College of Engineering and Applied Science, University of Colorado Boulder · Facultad de Ciencias y Tecnología, Universidad Mayor de San Simon
May 8, 2026 · Junjie Xu, Xingjiao Wu, Zihao Zhang +6 Human-AI Interaction AI Agent Governance
East China Normal University, Shanghai, China. · National University of Singapore, Singapore.
Oct 8, 2026 · Hanan Gani, Lulu Shao, Manmohan Chandraker Multi-Agent Reasoning Theory of Mind
Mar 19, 2026 J/K move · Enter open · S save
Eduardo Di Santi, Carla Florida
College of Engineering and Applied Science, University of Colorado Boulder · Facultad de Ciencias y Tecnología, Universidad Mayor de San Simon
Artificial intelligence is increasingly embedded in human decision-making, yet distinguishing systems that genuinely amplify human cognition from those promoting excessive dependence remains underdefined. This paper introduces a framework to distinguish cognitive amplification (improving hybrid performance without degrading human capability) from cognitive delegation (outsourcing reasoning to the AI). We define four metrics: the Cognitive Amplification Index (CAI*), Dependency Ratio (D), Human Reliance Index (HRI), and Human Cognitive Drift Rate (HCDR). We test this framework in an agent-based NetLogo simulation across three reliance regimes and multiple dependency-atrophy configurations, performing constrained optimizations and parameter sweeps to determine if positive collaborative gain is recoverable. Finally, we introduce an extension with an explicit human-AI interaction term. Our metrics effectively distinguish degenerate AI-dominated delegation, capability-preserving but weakly competitive interaction, and structurally dependent boundary regimes. Across all baseline configurations, no regime achieves positive collaborative gain relative to the best standalone baseline, even when reducing capability atrophy to zero. This limitation proves structural rather than merely parametric. Positive collaborative gain (CAI* > 0) becomes attainable only after introducing an explicit interaction term allowing retained human capability to contribute directly to the assisted output. This framework provides a basis for evaluating whether human-AI systems remain cognitively sustainable. The results suggest that preventing capability erosion alone is insufficient for genuine amplification if the architecture remains delegation-oriented. Amplification requires both preserved human capability and a coupling mechanism through which it contributes productively to the hybrid outcome.