Skip to main navigation menu Skip to main content Skip to site footer

Vol. 12 No. 10 (2025)
International Multidisciplinary Journal for Research & Development

Articles

COGNITIVE FIDELITY IN DATA REPRESENTATION: A PSYCHOPHYSICAL ANALYSIS OF VISUAL PERCEPTION CONSTRAINTS IN ANALYTIC DASHBOARDS

Published 2025-10-31

  • Dr. Sebastian M. Krüger

Dr. Sebastian M. Krüger
Independent Researcher, High-Fidelity Data Representation & Human-Centered Analytics, Berlin, Germany

Abstract

Background: As data volume increases, the design of analytic dashboards often prioritizes information density over cognitive accessibility. This study investigates the psychophysical limitations of human visual perception—specifically visual working memory (VWM) and feature binding—within the context of modern business intelligence interfaces. Methods: We conducted a multi-stage experiment (n=240) comparing three visualization modalities: static aggregated views, interactive filtering dashboards, and narrative "scrollytelling" formats. Participants performed low-level analytic activities and complex insight generation tasks while under varying cognitive loads. Stimuli were assessed using the Chromatic Vision Simulator to ensure accessibility. Results: Quantitative analysis revealed that while interactive dashboards offer the highest theoretical retrieval capacity, they induce significantly higher cognitive fatigue. The "scrollytelling" format resulted in a 34% improvement in long-term information retention and superior performance in risk assessment tasks involving proportion estimates. Furthermore, the use of anthropomorphic icons (stick figures) significantly improved probability comprehension among participants with lower baseline numeracy compared to abstract geometric shapes. Conclusion: The findings suggest that the bottleneck in data analytics is no longer computational but perceptual. Effective dashboard design must account for the "binding capacity" of VWM. We propose a "Cognitive Fidelity" framework that prioritizes narrative structure and perceptual grouping over raw data density to enhance decision-making accuracy.

Keywords

Visual Working Memory, Data Visualization, Cognitive Load, Health Numeracy

pdf

References

  1. The Psychology of Visual Perception in Data Dashboards: Designing for Impact. (2025). International Journal of Data Science and Machine Learning, 5(02), 79-86. https://doi.org/10.55640/ijdsml-05-02-07
  2. Alvarez, G. A., & Thompson, T. W. (2009). Overwriting and rebinding: Why feature-switch detection tasks underestimate the binding capacity of visual working memory. Visual Cognition, 17(1–2), 141–159. https://doi.org/10.1080/13506280802265496
  3. Amabili, L. (2019, August 22). From storytelling to scrollytelling: A short introduction and beyond. Medium. https://medium.com/nightingale/from-storytelling-to-scrollytelling-a-short-introduction-and-beyond-fbda32066964
  4. Amar, R., Eagan, J., & Stasko, J. (2005). Low-level components of analytic activity in information visualization. In J. Stasko & M. Ward (Eds.), IEEE Symposium on Information Visualization (InfoVis 2005, pp. 111–117). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/INFVIS.2005.1532136
  5. Ancker, J. S., & Kaufman, D. (2007). Rethinking health numeracy: A multidisciplinary literature review. Journal of the American Medical Informatics Association, 14(6), 713–721. https://doi.org/10.1197/jamia.M2464
  6. Ancker, J. S., Senathirajah, Y., Kukafka, R., & Starren, J. B. (2006). Design features of graphs in health risk communication: A systematic review. Journal of the American Medical Informatics Association, 13(6), 608–618. https://doi.org/10.1197/jamia.M2115
  7. Ancker, J. S., Weber, E. U., & Kukafka, R. (2011). Effect of arrangement of stick figures on estimates of proportion in risk graphics. Medical Decision Making, 31(1), 143–150. https://doi.org/10.1177/0272989X10369006
  8. Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1), 17–21. https://doi.org/10.1080/00031305.1973.10478966
  9. Asada, K. (2019). Chromatic vision simulator. https://asada.website/cvsimulator/e/index.html
  10. Badger, E., Cain Miller, C., Pearce, A., & Quealy, K. (2018, March 19). Extensive data shows punishing reach of racism for black boys. The New York Times. https://www.nytimes.com/interactive/2018/03/19/upshot/race-classwhite-and-black-men.html
  11. Burlinson, D., Subramanian, K., & Goolkasian, P. (2017). Open vs. closed shapes: New perceptual categories? IEEE Transactions on Visualization and Computer Graphics, 24(1), 574–583. https://doi.org/10.1109/TVCG.2017.2745086
  12. Burns, A., Xiong, C., Franconeri, S., Cairo, A., & Mahyar, N. (2020). How to evaluate data visualizations across different levels of understanding. In 2020 IEEE Workshop on Evaluation and Beyond: Methodological Approaches to Visualization (BELIV) (pp. 19–28). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BELIV51497.2020.00010

Downloads

Download data is not yet available.