Thermodynamic Human-Computer Interaction

2026-08-07

AI Summary

This work addresses the limited cross-device generalizability of traditional human-computer interaction models, such as cursor prediction. It introduces thermodynamic principles into HCI modeling for the first time, conceptualizing user–target interactions as equilibrium and non-equilibrium phases within a thermodynamic system. By constructing a unified energy potential field based on kinetic and potential energy, the authors derive a target prediction method that naturally yields Fitts’ Law. The resulting framework requires no training data, supports zero-shot cross-modal inference, and enables real-time prediction in constant time (O(1)). Experiments demonstrate 98.1% prediction accuracy in web prefetching tasks, achieving a Fetch:Click ratio as low as 1.37, while revealing that interface properties—such as color—significantly influence user behavior as independent parameters.

Abstract

Traditional human-computer interaction models rely on domain-specific techniques to model target prediction; models designed for cursor interaction prediction fail to generalize to mobile interfaces and vice versa. We introduce a unifying framework grounded in thermodynamics, proposing that human interaction is composed of phases in thermodynamic equilibrium and non-equilibrium. To demonstrate this, we derive Fitts' law and the proposed target prediction model from equilibrium thermodynamics by assigning kinetic and potential energies to a moving agent and target. Subsequently, we analyze the shortcomings of the prediction model and Fitts' law in edge cases, such as predicting intent for large targets. This analysis demonstrates that large targets cannot be accurately modeled using equilibrium thermodynamics. The proposed model scales across interaction modalities without modification, requires zero training data, and evaluates in constant O(1) time. Furthermore, we show that design properties such as the color of a button act as independent parameters that influence the attractive force exerted on an agent. Applied to live web prefetching tasks, the framework achieved an efficient Fetch:Click ratio of 1.37 and predicted the user's target with an accuracy of 98.1%.

Problem

• human-computer interaction
• target prediction
• interaction modalities
• Fitts' law
• generalization

Innovation

• thermodynamic human-computer interaction
• equilibrium thermodynamics
• target prediction
• cross-modality generalization
• zero-shot interaction modeling