The Trust Gap in Agentic Search: How Verbal-Imagery Cognitive Styles Shape Behavioural Signals and AI Acceptance
Huimin Tang, Yuhan Zhang, Alejandro Guerra-Manzanares, Boon Giin Lee, Dave Towey, Max L. Wilson, Matthew Pike · 2026
The paradigm of web search is currently shifting from reactive information retrieval to Agentic AI, where proactive systems autonomously synthesise information to assist users. However, for these agents to be effective, they must understand which user parameters drive behaviour to resolve the personalisation cold-start problem. While cognitive architecture is a recognised factor, empirical evidence linking specific traits to web search interaction remains unclear. This paper investigates the Verbal-Imagery (V-I) cognitive style dimension and its influence on proactive search behaviour, mental workload (MWL), and overall search user interface (SUI) alignment. Through a controlled user study (N = 20), web search behaviours were evaluated using interaction logs and think-aloud protocols, while MWL and usability were assessed via NASA-TLX and the System Usability Scale (SUS). Our findings reveal that verbalisers and imagers adopt statistically distinct navigational preferences: ver-balisers prefer sporadic, reactive interactions, while imagers rely on structured, proactive synthesis such as the Knowledge Panel. It is worth noting that qualitative data identifies a "trust gap" in AI-generated overviews based on cognitive modality preferences. These results demonstrate that the V-I dimension is a critical parameter for user modelling in agentic systems. We conclude by proposing requirements for user-aware agentic information retrieval, providing a framework for agents to dynamically adapt their representation strategies to minimise cognitive friction and enhance trust in proactive computing environments. This work is licensed under a Creative Commons "Attribution 4.0 International" license.