The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Human-centered design meets cognitive load theory: designing interfaces that help people think
Abstract
Historically, the development of computer systems has been primarily a technology-driven phenomenon, with technologists believing that "users can adapt" to whatever they build. Human-centered design advocates that a more promising and enduring approach is to model users' natural behavior to begin with so that interfaces can be designed that are more intuitive, easier to learn, and freer of performance errors. In this paper, we illustrate different user-centered design principles and specific strategies, as well as their advantages and the manner in which they enhance users' performance. We also summarize recent research findings from our lab comparing the performance characteristics of different educational interfaces that were based on user-centered design principles. One theme throughout our discussion is human-centered design that minimizes users' cognitive load, which effectively frees up mental resources for performing better while also remaining more attuned to the world around them.
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Citations
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Self-citation
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Integrating cognitive load theory and concepts of human–computer interaction
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Using task-induced pupil diameter and blink rate to infer cognitive load.
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“If this type of load is diagnosed, distractive interface features could be removed to minimize the extraneous complexity (Oviatt, 2006).”
InfluentialMangaroska, K., Sharma, K., Gašević, D., & Giannakos, M. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12590 · Cited by 100
The Low/High Index of Pupillary Activity
Duchowski, A. T., Krejtz, K., Gehrer, N. A., Bafna, T., & Bækgaard, P. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376394 · Cited by 94
Photoplethysmogram-based Cognitive Load Assessment Using Multi-Feature Fusion Model
Zhang, X., Lyu, Y., Qu, T., Qiu, P., Luo, X., Zhang, J., Fan, S., & Shi, Y. (2019) · ACM Transactions on Applied Perception · doi:10.1145/3340962 · Cited by 28
Trusting AI: does uncertainty visualization affect decision-making?
Reyes, J., Batmaz, A. U., & Kersten-Oertel, M. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1464348 · Cited by 27
Multimodal behavioral and physiological signals as indicators of cognitive load
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Natural interaction in virtual reality: impact on the cognitive load
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Multimodal interaction, interfaces, and analytics
Oviatt, S. (2022) · Handbook of Human Computer Interaction · doi:10.1007/978-3-319-27648-9_22-1 · Cited by 12
Self-citationVizer, L. M., & Sears, A. (2017) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2017.03.001 · Cited by 12
Creativity Support and Multimodal Pen-based Interaction
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CoWME: a general framework to evaluate cognitive workload during multimodal interaction
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Metadata
- Authors
- Oviatt, S.
- Venue
- Proceedings of the 14th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2006-10-23
- Pages
- 871-880
- OpenAlex
- W2161662762
- Cited by
- 315
- References
- 8
- FWCI
- 8.45
- Type
- conference paper
- Language
- en
- Open access
- closed
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