Cognitive Psychology
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Developmental PsychologistsJean PiagetLev VygotskyJerome Bruner
About

UX/UI

Key Concepts

These are the core concepts of the field — the disciplines themselves and the named principles, laws, and effects they rely on. They build on the cognitive functions below.

Scope

  • User experience (UX) — the whole of a person's perceived experience with a product, not just the interface.
  • User interface (UI) — the surface a user perceives and acts on.
  • Human–computer interaction (HCI) — the research field studying how people and computers interact.
  • Usability — effectiveness, efficiency, and satisfaction in use.
  • Interaction design — specifying how a product behaves in response to the user.
  • Information architecture — how content is structured, organized, and labeled.
  • Human factors / ergonomics — fitting systems to human capabilities and limits.

Laws and Effects

  • Fitts's Law — time to reach a target depends on its size and distance.
  • Hick's Law — decision time grows with the number of choices.
  • Miller's Law — working memory holds only about seven items (7 ± 2).
  • Power law of practice — performance speeds up predictably with repetition.
  • Doherty threshold — interaction holds attention when the system responds within ~400 ms.
  • Aesthetic–usability effect — attractive designs are perceived as easier to use.
  • Serial position effect — items at the start and end of a list are remembered best.

Principles and Theories

  • Mental models — users' internal expectations of how a system works.
  • Affordances and signifiers — what an element suggests about how it can be used.
  • Cognitive load — the mental effort an interface demands; minimizing extraneous load aids usability.
  • Gestalt principles — how the mind groups visual elements (proximity, similarity, closure, continuity).
  • Direct manipulation — acting on on-screen objects directly, with immediate visible results.
  • Distributed cognition — thinking spread across people, artifacts, and the environment.
  • Gulfs of execution and evaluation — the gaps between intention and action, and between system state and the user's understanding of it.
  • Recognition over recall — recognizing options is easier than recalling them from memory.
  • Heuristics and biases — mental shortcuts and the systematic errors they produce.
  • Dual-process theory — fast, intuitive thinking versus slow, deliberate thinking.

Key Functions

User experience, user interface, and human–computer interaction design all rest on a shared set of cognitive functions — the mental processes by which people perceive, attend to, remember, and act on an interface. The most central are:

  • Visual perception — recognizing icons, controls, and layouts, and organizing them into groups and hierarchy through Gestalt principles.
  • Attention — selecting what to focus on and dividing limited resources across a display, which determines what users notice and what they miss.
  • Working memory and cognitive load — the small, brief capacity for holding information in mind; minimizing extraneous load and chunking choices keeps interfaces usable.
  • Recognition over recall — seeing options is easier than remembering them, favoring visible menus and cues over memorized commands.
  • Mental models and schemas — stored expectations of how a system should behave; effective design matches them rather than fighting them.
  • Decision-making — judgment under uncertainty and the heuristics and biases that shape navigation, defaults, and persuasion.
  • Motor control — planning and executing aimed movements such as clicks, taps, and gestures; the basis for target size and placement.
  • Learning and automaticity — building skill with repeated use, turning deliberate actions into fluent habits.

Key Methods

UX, UI, and HCI practice relies on methods for studying users and testing designs — and each method rests on a cognitive process, which is also where its strengths and blind spots come from. They fall into four groups:

Discovery

Exploratory, generative work to understand the problem space and how people actually behave in context — grounded in observed behavior rather than self-report.

  • Contextual inquiry — A field method in which the researcher observes and questions users while they work in their own environment, treating the user as the expert and the researcher as apprentice. Because it captures behavior in situ rather than relying on what people remember or report, it surfaces the tacit workarounds, interruptions, and environmental constraints that users rarely think to mention.
  • Field studies — Research conducted in the user's real-world setting rather than a lab, observing how a product is actually used amid the noise, multitasking, and social context of everyday life. They trade experimental control for ecological validity, exposing needs and obstacles that controlled settings strip away.
  • Diary studies — Participants self-record their activities, thoughts, and frustrations over days or weeks, capturing behavior that unfolds over time or occurs too infrequently to observe directly. Because entries are logged close to the moment, they reduce the retrospective memory distortion that weakens after-the-fact accounts.
  • Ethnographic observation — Extended, immersive observation of people in their natural context to understand the routines, culture, and shared practices surrounding a product. It aims to reveal the unspoken norms and goals that shape behavior but that users cannot easily put into words.

User Research

Eliciting users' goals, attitudes, and self-reported behavior. These depend on introspection and retrospective recall, so they inherit memory's limits — recall error, telescoping, and social-desirability effects, plus sensitivity to how a question is worded.

  • Interviews — One-on-one conversations, structured or semi-structured, that elicit users' goals, attitudes, motivations, and recollected experiences. They depend on introspection and retrospective recall, so they reveal what people believe and remember rather than necessarily what they do — strongest when paired with observation.
  • Surveys and questionnaires — Standardized question sets administered at scale to quantify attitudes, preferences, and self-reported behavior across many users. They yield broad, comparable data quickly but inherit the limits of self-report: recall error, social-desirability bias, and sensitivity to question wording.
  • Focus groups — Moderated group discussions that surface attitudes, reactions, and vocabulary through interaction among participants. Useful for exploring perceptions and generating ideas, but group dynamics such as conformity and dominant voices make them weak predictors of how individuals actually behave.
  • Personas (as a synthesis of the above) — Composite, evidence-based profiles that consolidate findings from interviews, surveys, and observation into a small set of archetypal users with concrete goals, contexts, and pain points. They give the team a shared, memorable model of who is being designed for, anchoring decisions to real user needs rather than the designers' own assumptions.

Information Architecture

Modeling how content should be organized and labeled. These methods probe users' categorization and mental models — how people group and name things.

  • Card sorting — A method that reveals how users expect content to be organized and labeled, conducted in three variants:
    • Open card sort — Participants sort the cards into groups they create and name themselves, revealing how users naturally categorize content and what labels they expect.
    • Closed card sort — Participants sort the cards into categories the researcher has defined in advance, testing whether a proposed structure matches users' expectations.
    • Hybrid card sort — Participants sort into predefined categories but may also create their own, balancing open-ended discovery with validation of an existing structure.
  • Tree testing — A task-based method that tests the findability of items in a proposed site hierarchy by asking users to locate specific items without the aid of navigation design or visual cues. It isolates the information architecture itself, revealing where labels or category structures mislead.

Evaluation

Testing a design against real users or expert criteria. Think-aloud testing builds on verbal-protocol analysis of ongoing thought; heuristic evaluation applies the cognitive principles in the Key Concepts section as a checklist; behavioral measures capture task time and errors.

  • Usability testing (including think-aloud) — Representative users attempt realistic tasks while the team observes where they succeed, hesitate, or fail. In the think-aloud variant, participants verbalize their thoughts as they work — a form of concurrent verbal-protocol analysis that exposes the user's mental model, expectations, and points of confusion in real time.
  • Heuristic evaluation — Expert reviewers inspect an interface against a set of established usability principles to identify likely problems without involving users. Fast and inexpensive, it applies accumulated cognitive and design principles as a checklist, but it flags potential issues rather than confirming what real users will actually encounter.
  • Cognitive walkthrough — An expert method that steps through the actions a task requires and asks, at each step, whether a first-time user would know what to do, notice the right control, and understand the feedback. It is grounded in a theory of exploratory learning, which makes it especially suited to evaluating how easily new users can learn an interface.
  • A/B testing — A controlled experiment in which two or more versions of a design are shown to different user groups and outcomes (clicks, conversions, task completion) are compared statistically. It measures what people actually do at scale and establishes the causal effect of a change, but it optimizes among existing options rather than explaining why users behave as they do.

Key Researchers

The following researchers have made foundational contributions to the cognitive psychology of UX/UI design, ordered alphabetically by surname.

  • Stuart K. Card — Adjunct Professor, Computer Science, Stanford University; retired Senior Research Fellow, Xerox PARC; developed the Model Human Processor, GOMS, and the Keystroke-Level Model — formal cognitive models of the user (Card, Moran, & Newell, 1980).
    Google Scholar
  • Paul M. Fitts (1912–1965) — Ohio State University; University of Michigan; established Fitts's Law — target size/distance; the most-applied quantitative law in UI (Fitts, 1954).
  • Thomas R. G. Green — Cognitive scientist; most recently Visiting Professor, Dept. of Computer Science, University of York; co-developed the Cognitive Dimensions of Notations framework (Green & Petre, 1996).
  • W. E. Hick (1912–1974) — MRC Applied Psychology Unit, Cambridge; established Hick's Law — decision time vs. number of choices; menu/navigation design (Hick, 1952).
  • James D. Hollan — Distinguished Professor of Cognitive Science, UC San Diego; contributed to distributed cognition for HCI and cognitive consequences of interactive media (Hollan, Hutchins, & Kirsh, 2000).
    Google Scholar · UC San Diego
  • Edwin Hutchins — Professor Emeritus of Cognitive Science, UC San Diego; developed distributed cognition as a foundation for HCI (Hollan, Hutchins, & Kirsh, 2000) and the cognitive account of direct manipulation (Hutchins, Hollan, & Norman, 1985).
    Google Scholar · UC San Diego
  • Daniel Kahneman (1934–2024) — Eugene Higgins Professor of Psychology Emeritus, Princeton; developed dual-process theory and judgment under uncertainty; behavioral design (Tversky & Kahneman, 1974).
  • David Kirsh — Professor of Cognitive Science, UC San Diego; contributed to distributed/embodied cognition and thinking with external structure (Hollan, Hutchins, & Kirsh, 2000).
    Google Scholar · UC San Diego
  • Richard E. Mayer — Distinguished Professor of Psychological & Brain Sciences, UC Santa Barbara; developed the cognitive theory of multimedia learning; onboarding and instructional UI (Mayer & Moreno, 2003).
    Google Scholar · UC Santa Barbara
  • George A. Miller (1920–2012) — James S. McDonnell Distinguished University Professor of Psychology Emeritus, Princeton; established working-memory capacity and chunking — limiting and grouping interface choices (Miller, 1956).
  • Thomas P. Moran — Most recently Distinguished Engineer, IBM Almaden Research Center; formerly Xerox PARC; founding Editor-in-Chief, journal Human-Computer Interaction; co-developed GOMS / the Model Human Processor (Card, Moran, & Newell, 1980).
  • Allen Newell (1927–1992) — Carnegie Mellon University; co-developed GOMS / the Model Human Processor; information-processing psychology (Card, Moran, & Newell, 1980).
  • Donald A. Norman — Professor Emeritus of Cognitive Science & Psychology, UC San Diego; co-founder, Nielsen Norman Group; developed the theory of action underlying the gulfs of execution and evaluation (Norman, 1981), and is known for popularizing affordances in interface design and coining "UX".
    Google Scholar · UC San Diego
  • Marian Petre — Professor of Computing, The Open University; co-developed the Cognitive Dimensions of Notations framework (Green & Petre, 1996).
    Google Scholar · The Open University
  • Ben Shneiderman — Distinguished University Professor Emeritus of Computer Science, University of Maryland; originated and theorized direct manipulation (Shneiderman, 1983).
    Google Scholar · University of Maryland
  • John Sweller — Emeritus Professor, School of Education, UNSW Sydney; developed Cognitive Load Theory — minimizing extraneous interface complexity (Sweller, 1988).
    UNSW Sydney
  • Anne Treisman (1935–2018) — Princeton University; developed feature-integration theory of attention; preattentive processing, visual hierarchy (Treisman & Gelade, 1980).
  • Amos Tversky (1937–1996) — Stanford University; pioneered heuristics and biases research; backbone of behavioral/persuasive design (Tversky & Kahneman, 1974).
  • Christopher D. Wickens — Research Professor (Cognitive Psychology), Colorado State University; Professor Emeritus, University of Illinois Urbana-Champaign; developed multiple resource theory of attention; display design and workload (Wickens, 2002).
    Colorado State University

References

1Card, S. K., Moran, T. P., & Newell, A. (1980). The keystroke-level model for user performance time with interactive systems. Communications of the ACM, 23(7), 396–410. https://doi.org/10.1145/358886.358895
2Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381–391. https://doi.org/10.1037/h0055392
3Green, T. R. G., & Petre, M. (1996). Usability analysis of visual programming environments: A 'cognitive dimensions' framework. Journal of Visual Languages & Computing, 7(2), 131–174. https://doi.org/10.1006/jvlc.1996.0009
4Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4(1), 11–26. https://doi.org/10.1080/17470215208416600
5Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction, 7(2), 174–196. https://doi.org/10.1145/353485.353487
6Hutchins, E. L., Hollan, J. D., & Norman, D. A. (1985). Direct manipulation interfaces. Human-Computer Interaction, 1(4), 311–338. https://doi.org/10.1207/s15327051hci0104_2
7Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52. https://doi.org/10.1207/S15326985EP3801_6
8Miller, G. A. (1956). The magical number seven, plus or minus two. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158
9Norman, D. A. (1981). Categorization of action slips. Psychological Review, 88(1), 1–15. https://doi.org/10.1037/0033-295X.88.1.1
10Shneiderman, B. (1983). Direct manipulation: A step beyond programming languages. IEEE Computer, 16(8), 57–69. https://doi.org/10.1109/MC.1983.1654471
11Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
12Treisman, A., & Gelade, G. (1980). A feature-integration theory of attention. Cognitive Psychology, 12(1), 97–136. https://doi.org/10.1016/0010-0285(80)90005-5
13Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
14Wickens, C. D. (2002). Multiple resources and performance prediction. Theoretical Issues in Ergonomics Science, 3(2), 159–177. https://doi.org/10.1080/14639220210123806