Human Linguistic Perception of Distances: Examining Intrapersonal & Interpersonal Perceptions


  • Akeem Olowolayemo


Human navigation systems implemented on mobile devices often define distances or localization relative to landmarks using quantitative metrics. However, human minds abstract and articulate distances in linguistic forms using words rather than numeric distances, hence the current locations descriptions are not completely in tune with human cognition. The proposal in this work is to further evaluate the challenges inherent in linguistic description of distances, specifically by examining the intrapersonal & interpersonal perceptions variations.

A general challenge in location referencing is the impossibility of adequately estimating distances accurately from landmarks and also due to the fact that human minds, that is, majority of the people, cannot adequately grasp or perceive quantitative distances, but rather reprocess the quantitative distances into linguistic articulations. This work focuses how best qualitative or linguistic descriptions of distances can be accomplished. The overall aim of the research in this direction is to fashion out the best approach to building models that reprocess quantitative distances in indoor environment into linguistic form suitable for different groups of users such as old, young, handicapped, blind, etc. The results from the study demonstrated that there are no significant variations in the intrapersonal perceptions while there exist considerable significant differences in interpersonal perceptions among the various subject groups examined. It is hoped that the results may provide further guidance in the modelling of quantitative distances for mobile gadgets such as PDAs, firemen localisation devices.



2020-07-02 — Updated on 2020-10-21


How to Cite

Olowolayemo, A. (2020). Human Linguistic Perception of Distances: Examining Intrapersonal & Interpersonal Perceptions. International Journal on Perceptive and Cognitive Computing, 6(1), 1–8. Retrieved from (Original work published July 2, 2020)




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