Conversational Decision Support for Information Search Under Uncertainty: Effects of Gist and Verbatim Feedback

Abstract

Many real-world decisions rely on information search, where people sample evidence and decide when to stop under uncertainty. The uncertainty in the environment, particularly how diagnostic evidence is distributed, causes complexities in information search, further leading to suboptimal decision-making outcomes. Yet AI decision support often targets outcome optimization, and less is known about how to scaffold search without increasing cognitive load. We introduce SERA, an LLM-based assistant that provides either gist or verbatim feedback during search. Across two experiments (N1=54, N2=54), we examined decision-making outcomes and information search in SERA-Gist, SERA-Verbatim, and a no-feedback baseline across three environments varying in uncertainty. The uncertainty in environment is operationalized by the perceived gain of information across the course of sampling, which individuals may experience diminishing return of information gain (decremental; low-uncertainty), or a local drop of information gain (local optimum; medium-uncertainty), or no patterns in information gain (high-uncertainty), as they search more. Individuals show more accurate decision outcomes and are more confident with SERA support, especially under higher uncertainty. Gist feedback was associated with more efficient integration and showed a descriptive pattern of reduced oversampling, while verbatim feedback promoted more extensive exploration. These findings establish feedback representation as a design lever when search matters, motivating adaptive systems that match feedback granularity to uncertainty.