Augmented Memory and Attention in UI Interaction: NTDC as an Information-Theoretic Framework for Learning and Multitasking

Abstract

Modern user interfaces require continuous learning, rapid attentional shifts, and the sustained appearance of multitasking, yet attention, memory, and learning are often modeled separately in HCI and cognitive theory. This paper presents the Networked Two-Dimensional Communication Channels (NTDC) framework as an information-theoretic model of UI-mediated interaction under uncertainty. Within NTDC, Shannon’s six entropy relations are instantiated as a directional Shannon-system and paired into bidirectional TDC nodes within a networked interaction model. Attention is modeled as decoding through mutual information, while memory is modeled as stabilized actionable interface entropy through the CAIO-UAIO partition or, under an alternative boundary specification, as part of the user’s cognitive state. Learning is represented as the progressive stabilization of previously uncertain actionable options across interaction states, and multitasking is interpreted as rapid sequential relocation of attention rather than parallel cognition. NTDC therefore offers a compact, boundary-relative analytical framework for examining informational alignment, interface scaffolding, and learning dynamics in complex UI systems.

Keywords: Human–computer Interaction (HCI), User Interface Design, Cognitive Ergonomics, Attention Modelling, Memory Modelling, Augmented Cognition, Information Theory, Entropy, Multitasking, Learning Dynamics, Interaction Modelling, Cognitive Computing, System Modeling, AI Alignment

DOI: 10.54941/ahfe1007366