Modeling Trial-and-Error Navigation With a Sequential Decision Model of Information Scent
This work addresses navigation inefficiencies for users in complex information systems, presenting an incremental extension to existing information scent theory.
The paper tackled the problem of users struggling to locate items in information architectures due to ambiguous links and limited scanning, by extending information scent to a sequential decision-making model under memory constraints, and found that the model replicates key navigation behaviors like premature selections and backtracking.
Users often struggle to locate an item within an information architecture, particularly when links are ambiguous or deeply nested in hierarchies. Information scent has been used to explain why users select incorrect links, but this concept assumes that users see all available links before deciding. In practice, users frequently select a link too quickly, overlook relevant cues, and then rely on backtracking when errors occur. We extend the concept of information scent by framing navigation as a sequential decision-making problem under memory constraints. Specifically, we assume that users do not scan entire pages but instead inspect strategically, looking "just enough" to find the target given their time budget. To choose which item to inspect next, they consider both local (this page) and global (site) scent; however, both are constrained by memory. Trying to avoid wasting time, they occasionally choose the wrong links without inspecting everything on a page. Comparisons with empirical data show that our model replicates key navigation behaviors: premature selections, wrong turns, and recovery from backtracking. We conclude that trial-and-error behavior is well explained by information scent when accounting for the sequential and bounded characteristics of the navigation problem.