Impact of Graphic Design on Information Retention: A Linear Mixed Models Approach to Visual Elements and Memory

Authors

DOI:

https://doi.org/10.56294/saludcyt20251753

Keywords:

design, information, modeling, typography, visual

Abstract

Introduction: The effectiveness of graphic design in enhancing information retention depends largely on how visual elements are structured and perceived. Objective: The study analyzes the impact of graphic design on information retention, using a rigorous statistical and experimental approach. Methodology: Through a linear mixed model, principal component analysis and artificial neural networks, several visual elements such as typography, contrast, visual hierarchy, text density, layout type and content format were evaluated. Results: With a sample of 90 observations, the results showed that sans-serif typefaces, soft contrasts, use of icons, low text density and layouts with white space favor immediate information retention. On the other hand, advertising materials showed better performance compared to academic or infographics, possibly due to their more simplified and attractive design. Likewise, infographics stood out in long-term retention, due to their ability to integrate visual and textual content efficiently. The study also underscored the value of white space as a facilitator of cognitive processing, reducing mental overload. In sum, the findings demonstrated that design is not just aesthetics, but a strategic tool in enhancing learning. Conclusion: It is concluded that a conscious visual design, adapted to the type of content and the cognitive profile of the user, can significantly optimize the way people absorb, process and remember information.

 

 

 

 

References

1. Komilov, J. K., Dehkonov, B. A., & Ortikov, U. K. (2023). The function of metalanguage in the graphic communication. Oriental renaissance: Innovative, educational, natural and social sciences, 3(4), 622-626. https://oriens.uz/media/journalarticles/100_J.K._Komilov_622-626.pdf

2. Huang, Q., Lu, M., Lanir, J., Lischinski, D., Cohen-Or, D., & Huang, H. (2024). Graphimind: Llm-centric interface for information graphics design. arXiv preprint arXiv:2401.13245 https://doi.org/10.48550/arXiv.2401.13245

3. Chen, S., Xie, G., Liu, Y., Peng, Q., Sun, B., Li, H., ... & Shao, L. (2021). Hsva: Hierarchical semantic-visual adaptation for zero-shot learning. Advances in Neural Information Processing Systems, 34, 16622-16634. http://dx.doi.org/10.48550/arXiv.2109.15163

4. Li, L., Zhou, T., Wang, W., Li, J., & Yang, Y. (2022). Deep hierarchical semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1246-1257).. http://dx.doi.org/10.1109/CVPR52688.2022.00131

5. Park, S. (2022). A study on visual scaffolding design principles in web-based learning environments. Electronic Journal of E-Learning, 20(2), pp180-200. http://dx.doi.org/10.34190/ejel.20.2.2604

6. Zhao, J., & Zhao, X. (2022). Computer-aided graphic design for virtual reality-oriented 3D animation scenes. Computer-Aided Design and Applications, 19(1), 65-76. http://dx.doi.org/10.14733/cadaps.2022.S5.65-76

7. Hu, H., Liu, Y., Lu, W. F., & Guo, X. (2022). A quantitative aesthetic measurement method for product appearance design. Advanced Engineering Informatics, 53, 101644. https://doi.org/10.1016/j.aei.2022.101644

8. Sauer, J., & Sonderegger, A. (2022). Visual aesthetics and user experience: A multiple-session experiment. International Journal of Human-Computer Studies, 165, 102837. https://doi.org/10.1016/j.ijhcs.2022.102837

9. Zheng, Y. (2022). Visual memory neural network for artistic graphic design. Scientific Programming, 2022(1), 2243891. http://dx.doi.org/10.1155/2022/2243891

10. Jamal, I. N., & Mustaffa, N. (2023). The Impact of Visual Communication on Students’ Learning Experience Towards Memory Recognition and Enhancement. Al-i’lam-Journal of Contemporary Islamic Communication and Media, 3(1). https://doi.org/10.33102/jcicom.vol3no1.85

11. Locoro, A., Fisher, W. P., & Mari, L. (2021). Visual information literacy: Definition, construct modeling and assessment. IEEE access, 9, 71053-71071. http://dx.doi.org/10.1109/ACCESS.2021.3078429

12. Flores-Gallegos, R., Rodríguez-Leis, P., & Fernández, T. (2022). Effects of a virtual reality training program on visual attention and motor performance in children with reading learning disability. International Journal of Child-Computer Interaction, 32, 100394. https://doi.org/10.1016/j.ijcci.2021.100394

13. Lee, H., & Chen, J. (2022). Predicting memory from the network structure of naturalistic events. Nature Communications, 13(1), 4235. http://dx.doi.org/10.1038/s41467-022-31965-2

14. Rolls, E. T. (2022). The hippocampus, ventromedial prefrontal cortex, and episodic and semantic memory. Progress in Neurobiology, 217, 102334. https://doi.org/10.1016/j.pneurobio.2022.102334

15. Habib, H., Zou, Y., Yao, Y., Acquisti, A., Cranor, L., Reidenberg, J., ... & Schaub, F. (2021, May). Toggles, dollar signs, and triangles: How to (in) effectively convey privacy choices with icons and link texts. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-25). http://dx.doi.org/10.1145/3411764.3445387

16. Lu, S., Liu, Y., & Kong, A. W. K. (2023). Tf-icon: Diffusion-based training-free cross-domain image composition. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2294-2305). http://dx.doi.org/10.48550/arXiv.2307.12493

17. Arunkumar, A., Padilla, L., Bae, G., & Bryan, C. (2023). Image or Information? Examining the Nature and Impact of Visualization Perceptual Classification. IEEE Transactions On Visualization And Computer Graphics, 1-11. https://doi.org/10.1109/tvcg.2023.3326919

18. Abbazio, J. M., & Yang, Z. S. (2022). Are Infographics Worth It?: An Assessment of Information Retention in Relation to Information Embedded in Infographics. Music Reference Services Quarterly, 25(4), 99-130. https://doi.org/10.1080/10588167.2022.2060631

19. Ciccione, L., Caroti, D., Liu, S., Giardino, V., Pasquinelli, E., & Dehaene, S. (2025). The superiority of graphics over text in long-term memory retention. Journal Article. https://doi.org/10.31234/osf.io/vrbfy

20. Awad, M. F., Lin, K., & Franconeri, S. L. (2023). Mixed graph designs do not improve visual memory. Journal Of Vision, 23(9), 5781. https://doi.org/10.1167/jov.23.9.5781

21. Traboco, L., Pandian, H., Nikiphorou, E., & Gupta, L. (2022). Designing Infographics: Visual Representations for Enhancing Education, Communication, and Scientific Research. Journal Of Korean Medical Science, 37(27). https://doi.org/10.3346/jkms.2022.37.e214

22. Bylinskii, Z., Kim, N. W., O’Donovan, P., Alsheikh, S., Madan, S., Pfister, H., Durand, F., Russell, B., & Hertzmann, A. (2017). Learning Visual Importance for Graphic Designs and Data Visualizations. Journal, 57-69. https://doi.org/10.1145/3126594.3126653

23. Ito, K., Kang, Y., Zhang, Y., Zhang, F., & Biljecki, F. (2024). Understanding urban perception with visual data: A systematic review. Cities, 152, 105169. https://doi.org/10.1016/j.cities.2024.105169

24. Dai, L., Zheng, C., Dong, Z., Yao, Y., Wang, R., Zhang, X., ... & Guan, Q. (2021). Analyzing the correlation between visual space and residents' psychology in Wuhan, China using street-view images and deep-learning technique. City and Environment Interactions, 11, 100069. http://dx.doi.org/10.1016/j.cacint.2021.100069

25. Kim, N. W., Joyner, S. C., Riegelhuth, A., & Kim, Y. (2021). Accessible visualization: Design space, opportunities, and challenges. In Computer graphics forum (Vol. 40, No. 3, pp. 173-188). https://doi.org/10.1111/cgf.14298

26. Nhan, L. K., & Yen, P. H. (2021). The effects of using infographics-based learning on EFL learners’ grammar retention. International Journal of Science and Management Studies (IJSMS), 4, I4. http://dx.doi.org/10.51386/25815946/ijsms-v4i4p124

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Published

2025-06-01

How to Cite

1.
Aguilar-Cajas H, Vergara-Zurita HE, Rivera-Abarca AL, García-Guerra JI, Armijos-Arcos F, Mantilla-Miranda AS. Impact of Graphic Design on Information Retention: A Linear Mixed Models Approach to Visual Elements and Memory. Salud, Ciencia y Tecnología [Internet]. 2025 Jun. 1 [cited 2025 Jun. 21];5:1753. Available from: https://sct.ageditor.ar/index.php/sct/article/view/1753