Category: Tier 1 - Core Traits Scale: 0.0 (very low comprehension) to 1.0 (very high comprehension)

Definition

Comprehension is a user's ability to understand interface elements, follow instructions, and build accurate mental models. It covers both text literacy and procedural understanding of interface mechanics.

Low comprehension users struggle with technical terms, complex navigation, and multi-step flows. High comprehension users grasp system logic quickly and adapt to unfamiliar interfaces.

Research Foundation

Primary Citation

"The GOMS model provides a framework for predicting the time it takes users to accomplish tasks and the errors they will make... User performance depends critically on the methods they have learned for accomplishing goals."

Card, Moran, & Newell, 1983, p. 139

Full Citation (APA 7): Card, S. K., Moran, T. P., & Newell, A. (1983). The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates.

ISBN: 978-0898592436

Supporting Research

"Cognitive load theory suggests that instructional design should minimize extraneous cognitive load while promoting germane cognitive load... When intrinsic load is high, even small amounts of extraneous load can overwhelm working memory."

Sweller, 1988, p. 266

Full Citation (APA 7): Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1207/s15516709cog1202_4

Key Numerical Values

Metric	Value	Source
Average adult reading level (US)	7th-8th grade	National Assessment of Adult Literacy (2003)
Recommended web content level	6th grade	Nielsen Norman Group (2015)
Comprehension drop per grade level above target	10-15%	Klare (1963)
Users understanding privacy policies	9%	McDonald & Cranor (2008)
Error rate increase with jargon	32%	Lazar et al. (2006)
GOMS prediction accuracy	r = 0.9 with actual times	Card, Moran, & Newell (1983)

Behavioral Levels

Value	Label	Behaviors
0.0-0.2	Very Low	Cannot parse technical terminology. Gets lost in multi-step processes. Clicks randomly when confused. Cannot distinguish between similar-looking buttons. Requires step-by-step hand-holding. May not understand error messages at all. Frequently backs out of processes due to confusion.
0.2-0.4	Low	Struggles with industry jargon (e.g., "authenticate," "configure," "deploy"). Needs visual cues alongside text. May misinterpret instructions. Follows only very simple navigation. Often unsure which button to click. Reads but doesn't fully understand help documentation.
0.4-0.6	Moderate	Understands standard web conventions (shopping cart icon, hamburger menu). Follows clear instructions reliably. May struggle with advanced features. Understands common error messages. Can complete multi-step forms with clear progress indicators. Baseline GOMS model performance.
0.6-0.8	High	Quickly grasps new interface patterns. Understands technical documentation. Anticipates next steps in processes. Transfers knowledge from similar systems. Can troubleshoot common issues independently. Comfortable with complex forms and workflows.
0.8-1.0	Very High	Immediately understands novel interface paradigms. Reads and applies API documentation. Predicts system behavior accurately. Can use keyboard shortcuts and advanced features. Self-teaches from minimal instruction. Builds accurate mental models rapidly.

The GOMS Model

Components

Card, Moran, and Newell's GOMS model breaks user behavior into:

Goals: What the user wants to accomplish (e.g., "buy a book")
Operators: Basic actions (click, type, scroll, read)
Methods: Sequences of operators to achieve goals
Selection Rules: How users choose between methods

Comprehension's Role in GOMS

Comprehension Level	GOMS Impact
Low	Limited method repertoire, slower operator execution, poor selection rules
Moderate	Standard methods, typical operator times, basic selection
High	Rich method library, efficient operators, optimal selection

Estimated Trait Correlations

Correlation estimates are derived from related research findings and theoretical models. Empirical calibration is planned (GitHub #95).

Related Trait	Correlation	Mechanism
Trait-WorkingMemory	r = 0.52	Memory capacity enables complex comprehension
Trait-ProceduralFluency	r = 0.61	Comprehension enables procedure learning
Trait-TransferLearning	r = 0.48	Understanding enables cross-domain transfer
Trait-ReadingTendency	r = 0.35	Reading enables text-based comprehension
Trait-SelfEfficacy	r = 0.42	Understanding builds confidence

Readability and Comprehension

Flesch-Kincaid Guidelines

Reading Level	Grade Level	Comprehension Score Range
Very Easy	5th grade	0.0-0.3
Easy	6th grade	0.3-0.5
Standard	8th grade	0.5-0.7
Difficult	10th-12th grade	0.7-0.9
Very Difficult	College+	0.9-1.0

Web Content Implications

Low comprehension users: Need 5th-6th grade reading level, visual cues, minimal jargon
High comprehension users: Can handle technical documentation, complex interfaces

Impact on Web Behavior

Error Recovery

Very Low: Cannot understand error messages, gives up
Low: Understands simple errors ("wrong password"), confused by technical errors
Moderate: Follows basic troubleshooting steps
High: Interprets error codes, tries multiple solutions
Very High: Debugs issues independently, consults documentation

Navigation

Low comprehension: Relies on familiar patterns, lost with novel navigation
High comprehension: Quickly learns new navigation paradigms, uses advanced features

Form Completion

Low comprehension: Confused by field labels, validation messages unclear
High comprehension: Understands field requirements, anticipates validation rules

Persona Values

Persona	Comprehension Value	Rationale
Persona-AnxiousFirstTimer	0.4	Anxiety impairs comprehension
Persona-MethodicalSenior	0.5	Slower but thorough processing
Persona-DistractedParent	0.5	Divided attention limits comprehension
Persona-RushedProfessional	0.7	Experienced but hurried
Persona-TechSavvyExplorer	0.85	High baseline + practice
Persona-AccessibilityUser	0.6	Variable, depends on accommodations

UX Design Implications

For Low-Comprehension Users

Use plain language (6th grade reading level)
Provide visual cues alongside text labels
Show examples rather than just instructions
Break complex processes into small steps
Use progressive disclosure for advanced features
Avoid jargon and technical terminology
Include contextual help tooltips

For High-Comprehension Users

Can provide power-user features
Documentation can be more technical
Fewer hand-holding elements needed
Can use industry-standard terminology
Advanced features can be more accessible

Bibliography

Card, S. K., Moran, T. P., & Newell, A. (1983). The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates. ISBN 978-0898592436

Klare, G. R. (1963). The Measurement of Readability. Iowa State University Press.

Kutner, M., Greenberg, E., Jin, Y., Boyle, B., Hsu, Y., & Dunleavy, E. (2007). Literacy in Everyday Life: Results from the 2003 National Assessment of Adult Literacy. U.S. Department of Education.

Lazar, J., Feng, J. H., & Hochheiser, H. (2006). Research Methods in Human-Computer Interaction. John Wiley & Sons.

McDonald, A. M., & Cranor, L. F. (2008). The cost of reading privacy policies. I/S: A Journal of Law and Policy for the Information Society, 4(3), 543-568.

Nielsen Norman Group. (2015). How users read on the web. https://www.nngroup.com/articles/how-users-read-on-the-web/

Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285. https://doi.org/10.1207/s15516709cog1202_4

Copyright: (c) 2026 Alexa Eden.

License: MIT License

Contact: [email protected]

Trait Comprehension