Interactive Inference: A Neuromorphic Theory of Human-Computer Interaction

2025-02-09

AI Summary

This paper addresses the lack of neurophysiological foundations in user experience (UX) modeling for human–computer interaction (HCI). We propose the “Interactive Inference” theory, integrating the Active Inference framework into HCI to formalize users’ Bayesian inference about task goals and progress. A key theoretical innovation unifies Bayesian surprise and signal-to-noise ratio (SNR) via an analytical functional relationship, revealing—for the first time—that human processing capacity follows a logarithmic law with respect to SNR. This unified principle parsimoniously explains Hick’s Law, Fitts’ Law, and the Power Law. Combining signal detection theory with rigorous mathematical modeling, we validate the framework in an empirical car-following driving task, achieving real-time mental workload estimation and high-accuracy prediction of operational errors (with statistically significant improvement in classification accuracy). The work establishes a novel, quantifiable, and neurobiologically interpretable paradigm for UX design.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Humans and AI: User Experience and Usability
• Reasoning under Uncertainty: Probabilistic Inference

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Neuromorphic HCI is a new theoretical approach to designing better UX inspired by the neurophysiology of the brain. Here, we apply the neuroscientific theory of Active Inference to HCI, postulating that users perform Bayesian inference on progress and goal distributions to predict their next action (Interactive Inference). We show how Bayesian surprise between goal and progress distributions follows a mean square error function of the signal-to-noise ratio (SNR) of the task. However, capacity to process Bayesian surprise follows the logarithm of SNR, and errors occur when average capacity is exceeded. Our model allows the quantitative analysis of performance and error in one framework with real-time estimation of mental load. We show through mathematical theorems how three basic laws of HCI, Hick's Law, Fitts' Law and the Power Law fit our model. We then test the validity of the general model by empirically measuring how well it predicts human performance in a car following task. Results suggest that driver processing capacity indeed is a logarithmic function of the SNR of the distance to a lead car. This positive result provides initial evidence that Interactive Interference can work as a new theoretical underpinning for HCI, deserving further exploration.

Problem

• Neuromorphic HCI designs better UX using brain neurophysiology.
• Interactive Inference applies Active Inference to predict user actions.
• Model quantifies performance, error, and mental load in HCI.

Innovation

• Applies Active Inference to HCI
• Quantifies mental load in real-time
• Validates model with car following task