Spatial Memory with Immersive Visualization Design and Semantic Grounding across Virtual Museum Tours

Authors

  • Mohammed White Department of Computer Science, School of Engineering, University of Birmingham, Birmingham, England, United Kingdom Author

Keywords:

Spatial Memory, Immersive Visualization, Semantic Grounding, Virtual Reality, Museum Tours

Abstract

Spatial memory, the cognitive mechanism responsible for recording and recovering information about spatial environments and spatial orientation, plays a foundational role in how human beings navigate complex environments. With the rapid advancement of immersive visualization technologies, virtual reality environments have emerged as highly potent platforms for both the study and the enhancement of spatial memory. This paper investigates the intricate relationship between immersive visualization design and the cognitive retention of spatial information, placing a specific focus on the concept of semantic grounding within the context of virtual museum tours. By mapping abstract spatial coordinates to meaningful semantic constructs, semantic grounding provides cognitive anchors that facilitate more robust mental representations of virtual spaces. Through a comprehensive examination of theoretical models of spatial cognition and a rigorously designed empirical study involving navigated virtual museum environments, this research demonstrates that integrating semantically rich visualization cues significantly improves spatial recall accuracy and reduces navigational disorientation. The findings provide critical insights for the development of educational virtual environments, suggesting that the conscious alignment of visual design with semantic narratives can fundamentally alter the efficacy of spatial memory encoding. Ultimately, this work offers a novel framework for cognitive scientists and virtual environment designers to optimize immersive learning spaces.

References

1. Vahmani, P.; Luo, X.; Jones, A.; Hong, T. Anthropogenic Heating of the Urban Environment: An Investigation of Feedback Dynamics between Urban Micro-Climate and Decomposed Anthropogenic Heating from Buildings. Build. Environ. 2022, 213, 108841.

2. Amasyali, K.; El-Gohary, N.M. A Review of Data-Driven Building Energy Consumption Prediction Studies. Renew. Sustain. Energy Rev. 2018, 81, 1192–1205.

3. Ma, X.; Zou, B.; Deng, J.; Gao, J.; Longley, I.; Xiao, S.; Guo, B.; Wu, Y.; Xu, T.; Xu, X.; et al. A Comprehensive Review of the Development of Land Use Regression Approaches for Modeling Spatiotemporal Variations of Ambient Air Pollution: A Perspective from 2011 to 2023. Environ. Int. 2024, 183, 108430.

4. Long, J.; Liu, Y.; Xing, S.; Qiu, L.; Huang, Q.; Zhou, B.; Shen, J.; Zhang, L. Effects of sampling density on interpolation accuracy for farmland soil organic matter concentration in a large region of complex topography. Ecol. Indic. 2018, 93, 562–571.

5. Poggio, L.; de Sousa, L.M.; Batjes, N.H.; Heuvelink, G.B.M.; Kempen, B.; Ribeiro, E.; Rossiter, D. SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL 2021, 7, 217–240.

6. Ngu, N.H.; Trung, N.H.; Shinjo, H.; Chotpantarat, S.; Thanh, N.N. Improving spatial prediction of soil organic matter in central Vietnam using Bayesian-enhanced machine learning and environmental covariates. Arch. Agron. Soil Sci. 2025, 71, 1–17.

7. Siqueira, R.G.; Moquedace, C.M.; Fernandes-Filho, E.I.; Schaefer, C.E.G.R.; Francelino, M.R.; Sacramento, I.F.; Michel, R.F.M. Modelling and prediction of major soil chemical properties with Random Forest: Machine learning as tool to understand soil-environment relationships in Antarctica. CATENA 2024, 235, 107677.

8. Huang, B.; Yang, G.; Lei, J.; Wang, X. A partitioned conditioned Latin hypercube sampling method considering spatial heterogeneity in digital soil mapping. Sci. Rep. 2025, 15, 12851.

9. Radočaj, D.; Jug, I.; Vukadinović, V.; Jurišić, M.; Gašparović, M. The Effect of Soil Sampling Density and Spatial Autocorrelation on Interpolation Accuracy of Chemical Soil Properties in Arable Cropland. Agronomy 2021, 11, 2430.

10. Fongaro, C.T.; Demattê, J.A.M.; Rizzo, R.; Lucas Safanelli, J.; Mendes, W.D.S.; Dotto, A.C.; Vicente, L.E.; Franceschini, M.H.D.; Ustin, S.L. Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images. Remote Sens. 2018, 10, 1555.

11. Climate Zone 3C: Insulation R-Values, Design Temps & HVAC Guide. Available online: https://autohvac.ai/climate-zones/zone/3c (accessed on 27 March 2026).

12. Reinhart, W.F.; Statt, A. Large Language Models Design Sequence-Defined Macromolecules via Evolutionary Optimization. npj Comput. Mater. 2024, 10, 262.

13. Ding, Z.; Liu, K.; Grunwald, S.; Smith, P.; Ciais, P.; Wang, B.; Wadoux, A.M.J.C.; Ferreira, C.; Karunaratne, S.; Shurpali, N.; et al. Advancing Soil Organic Carbon Prediction: A Comprehensive Review of Technologies, AI, Process-Based and Hybrid Modelling Approaches. Adv. Sci. 2025, 12, e04152.

14. Shi, Z.; Jain, A.; Swersky, K.; Hashemi, M.; Ranganathan, P.; Lin, C. A hierarchical neural model of data prefetching. In Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Virtual Event, 19–23 April 2021; pp. 861–873.

15. Arrieta, A.B.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; García, S.; Gil-López, S.; Molina, D.; Benjamins, R.; et al. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 2020, 58, 82–115.

16. Manoli, G.; Fatichi, S.; Schläpfer, M.; Yu, K.; Crowther, T.W.; Meili, N.; Burlando, P.; Katul, G.G.; Bou-Zeid, E. Magnitude of Urban Heat Islands Largely Explained by Climate and Population. Nature 2019, 573, 55–60.

17. Purlis, E. Browning development in bakery products—A review. J. Food Eng. 2010, 99, 239–249.

18. Mannaro, K.; Baire, M.; Fanti, A.; Lodi, M.B.; Didaci, L.; Fedeli, A.; Cocco, L.; Randazzo, A.; Mazzarella, G.; Fumera, G. A robust svm color-based food segmentation algorithm for the production process of a traditional carasau bread. IEEE Access 2022, 10, 15359–15377.

19. Hashemi, F.; Mills, G. On the Impact of Urban Climate and Heat Islands on Building Energy Performance: A Critical Review. Energy Build. 2025, 343, 115946.

20. Hong, T.; Xu, Y.; Sun, K.; Zhang, W.; Luo, X.; Hooper, B. Urban Microclimate and Its Impact on Building Performance: A Case Study of San Francisco. Urban Clim. 2021, 38, 100871.

21. Laga, A.; Boukhobza, J.; Koskas, M.; Singhoff, F. Lynx: A learning linux prefetching mechanism for SSD performance model. In Proceedings of the 2016 5th Non-Volatile Memory Systems and Applications Symposium (NVMSA), Daegu, Republic of Korea, 17–19 August 2016; pp. 1–6.

22. Zhang, P.; Shao, M. Spatial Variability and Stocks of Soil Organic Carbon in the Gobi Desert of Northwestern China. PLoS ONE 2014, 9, e93584.

23. Adeniyi, O.D.; Brenning, A.; Maerker, M. Spatial prediction of soil organic carbon: Combining machine learning with residual kriging in an agricultural lowland area (Lombardy region, Italy). Geoderma 2024, 448, 116953.

24. Zheng, Z.; Liu, K.; Zhu, X. Machine Learning-Based Prediction of Metal-Organic Framework Materials: A Comparative Analysis of Multiple Models. arXiv 2025.

25. da Costa Barbon, A.P.A.; Barbon, S., Jr.; Campos, G.F.C.; Seixas, J.L., Jr.; Peres, L.M.; Mastelini, S.M.; Andreo, N.; Ulrici, A.; Bridi, A.M. Development of a flexible computer vision system for marbling classification. Comput. Electron. Agric. 2017, 142, 536–544.

26. Medeiros, M.C.; Vasconcelos, G.F.R.; Veiga, A.; Zilberman, E. Forecasting Inflation in a data-rich environment: The benefits of machine learning methods. J. Bus. Econ. Stat. 2021, 39, 98–119.

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Published

2026-05-16

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