When do we interact multimodally?

TLDR

Mobile usage patterns often involve high, fluctuating difficulty and dual tasking. The research investigates whether a flexible multimodal interface can help users manage cognitive load and aims to build an empirical basis for designing adaptive multimodal systems. Users spontaneously increase multimodal interaction as load rises—shifting from 18.6 % to 77.1 % when a new context is required and from 59.2 % to 75.0 % across difficulty levels—while task errors and latencies also rise, showing that distributing information across modalities aids self‑management of working‑memory limits in line with cognitive‑load theory.

Abstract

Mobile usage patterns often entail high and fluctuating levels of difficulty as well as dual tasking. One major theme explored in this research is whether a flexible multimodal interface supports users in managing cognitive load. Findings from this study reveal that multimodal interface users spontaneously respond to dynamic changes in their own cognitive load by shifting to multimodal communication as load increases with task difficulty and communicative complexity. Given a flexible multimodal interface, users' ratio of multimodal (versus unimodal) interaction increased substantially from 18.6% when referring to established dialogue context to 77.1% when required to establish a new context, a +315% relative increase. Likewise, the ratio of users' multimodal interaction increased significantly as the tasks became more difficult, from 59.2% during low difficulty tasks, to 65.5% at moderate difficulty, 68.2% at high and 75.0% at very high difficulty, an overall relative increase of +27%. Analysis of users' task-critical errors and response latencies across task difficulty levels increased systematically and significantly as well, corroborating the manipulation of cognitive processing load. The adaptations seen in this study reflect users' efforts to self-manage limitations on working memory when task complexity increases. This is accomplished by distributing communicative information across multiple modalities, which is compatible with a cognitive load theory of multimodal interaction. The long-term goal of this research is the development of an empirical foundation for proactively guiding flexible and adaptive multimodal system design.