Cognitive Load Modeling in Mobile Touch Interaction and Optimization of Marketing Information Presentation Strategies.

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Bibliographic Details
Title:	Cognitive Load Modeling in Mobile Touch Interaction and Optimization of Marketing Information Presentation Strategies.
Authors:	Wang, Sufeng¹, Hu, Lili¹ hulili5015@163.com
Source:	International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 9, p80-94. 15p.
Subjects:	Cognitive load, Reinforcement learning, Decision making, Touch screens, Marketing, Human-computer interaction
Abstract:	In mobile touch interaction environments, a mismatch between marketing information presentation and users' cognitive load often results in suboptimal interaction experiences and low commercial conversion efficiency, thereby constraining the advancement of mobile marketing optimization. This study proposes an integrated technical framework that combines real-time multimodal cognitive load quantification with reinforcement learning-based adaptive decision-making to dynamically align marketing information presentation with users' real-time cognitive states. The framework consists of two core modules: a multimodal real-time cognitive load estimation model and a reinforcement learning-driven adaptive information presentation decision engine. The former synchronously collects multimodal data--including touch interaction behaviors, eye-tracking signals, and basic physiological indicators--and constructs a high-discriminability feature system integrated with a temporal multi-head attention fusion network. This design enables precise, millisecond-level cognitive load quantification without reliance on bulky laboratory equipment. The latter module treats cognitive load as the primary state signal, designs a multi-objective reward mechanism that balances shortterm user experience and long-term commercial value, and employs a cloud-edge collaborative deployment architecture to achieve dynamic and adaptive adjustment of marketing information presentation strategies. The two modules are tightly coupled through a real-time data streaming pipeline, effectively addressing the challenges of multimodal synchronization and low-latency decision-making in mobile environments. Experimental results in mobile marketing scenarios demonstrate that the proposed framework accurately captures users' real-time cognitive load, significantly optimizes information presentation effectiveness, and enhances both interaction experience and commercial conversion efficiency. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	In mobile touch interaction environments, a mismatch between marketing information presentation and users' cognitive load often results in suboptimal interaction experiences and low commercial conversion efficiency, thereby constraining the advancement of mobile marketing optimization. This study proposes an integrated technical framework that combines real-time multimodal cognitive load quantification with reinforcement learning-based adaptive decision-making to dynamically align marketing information presentation with users' real-time cognitive states. The framework consists of two core modules: a multimodal real-time cognitive load estimation model and a reinforcement learning-driven adaptive information presentation decision engine. The former synchronously collects multimodal data--including touch interaction behaviors, eye-tracking signals, and basic physiological indicators--and constructs a high-discriminability feature system integrated with a temporal multi-head attention fusion network. This design enables precise, millisecond-level cognitive load quantification without reliance on bulky laboratory equipment. The latter module treats cognitive load as the primary state signal, designs a multi-objective reward mechanism that balances shortterm user experience and long-term commercial value, and employs a cloud-edge collaborative deployment architecture to achieve dynamic and adaptive adjustment of marketing information presentation strategies. The two modules are tightly coupled through a real-time data streaming pipeline, effectively addressing the challenges of multimodal synchronization and low-latency decision-making in mobile environments. Experimental results in mobile marketing scenarios demonstrate that the proposed framework accurately captures users' real-time cognitive load, significantly optimizes information presentation effectiveness, and enhances both interaction experience and commercial conversion efficiency. [ABSTRACT FROM AUTHOR]
ISSN:	18657923
DOI:	10.3991/ijim.v20i09.61741