Mental Models in UX: Designing Information the Way Users Think

Understanding users’ mental models is one of the most critical steps in designing effective information architecture (IA) and user experience (UX). A mental model represents how users believe a system works, based on their past experiences, expectations, and goals. If your IA aligns with this internal model, users feel confident and intuitive flow emerges. If it does not, confusion and friction appear. This article explores where to start and how to systematically uncover users’ mental models when considering IA and UX design.

A user’s mental model is not something you guess at the final stage of design. It must be discovered early and refined continuously. By grounding your structure and interactions in how users already think, you reduce cognitive load and increase usability without forcing users to learn your logic.

Understanding mental models is less about opinions and more about observing patterns in behavior, language, and decision-making.

Starting from Users’ Goals, Not Features

The first step in understanding a mental model is to focus on user goals rather than product features. Users do not approach a service thinking about menus, categories, or system constraints. They arrive with intentions such as wanting to compare options, complete a task quickly, or understand what happens next. These goals shape how they expect information to be organized.

To uncover this, begin with qualitative research such as interviews and contextual inquiries. Ask users what they are trying to achieve, what success looks like to them, and what they expect to see first. Pay close attention to the words they use, because language reveals how they group concepts mentally. When users consistently describe tasks in a certain order, that sequence often reflects their internal model more accurately than any flowchart.

By anchoring IA decisions to goals, you ensure the structure supports real intentions instead of internal assumptions.

Observing Existing Behavior and Workflows

Mental models are often best revealed through observation rather than direct questioning. Users may struggle to articulate how they think, but their behavior shows it clearly. Analyzing how users currently navigate similar products, competitor services, or even offline processes provides strong clues about their expectations.

Methods such as usability testing, task analysis, and journey mapping are especially effective. Watch where users hesitate, backtrack, or make incorrect assumptions. These moments indicate a mismatch between the system’s structure and their mental model. Existing workflows, spreadsheets, or manual processes users rely on can also serve as externalized versions of their internal thinking.

The goal is not to copy existing systems blindly, but to understand the logic users already trust and feel comfortable with.

Using Card Sorting to Reveal Concept Grouping

Card sorting is one of the most practical tools for uncovering users’ mental models in IA design. By asking users to group and label content in ways that make sense to them, you gain direct insight into how they categorize information. This process highlights natural relationships that may not be obvious from a business or technical perspective.

Open card sorting is particularly useful in early stages, as it shows how users name categories without guidance. Closed card sorting helps validate whether a proposed structure aligns with their expectations. Patterns that emerge across multiple users often indicate shared mental models, which are ideal foundations for navigation and hierarchy.

This method turns abstract thinking into tangible structure.

Validating Mental Models Through Prototypes

Mental models are hypotheses that must be tested. Low-fidelity prototypes, wireframes, and clickable mockups allow designers to validate whether their interpretation of users’ mental models holds up in practice. When users interact with a prototype, their reactions reveal whether the structure feels intuitive or forced.

Observe whether users can predict what will happen next and whether labels match their expectations. If users consistently guess correctly, your IA likely aligns with their mental model. If not, the design may reflect the system’s logic rather than the user’s.

Iteration at this stage is far less costly than fixing structural problems after launch.

Continuously Refining Mental Models Over Time

Users’ mental models evolve as they gain experience with a product, and new user segments bring different expectations. For this reason, understanding mental models is not a one-time activity. Analytics data, search logs, and user feedback reveal shifts in how users think and what they prioritize.

Frequently searched terms often indicate concepts users expect to find easily. Support questions highlight gaps between system design and user understanding. Treating mental models as living insights allows IA and UX to adapt without becoming inconsistent.

A strong UX respects what users already know while gently guiding them toward new understanding.

In your own UX projects, how do you currently validate whether your information structure truly matches users’ mental models?