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Arthur Juliani, Alexander Bies, Cooper Boydston, Richard Taylor, Margaret Sereno; Spatial localization accuracy varies with the fractal dimension of the environment. Journal of Vision 2016;16(12):1370. doi: 10.1167/16.12.1370.
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© ARVO (1962-2015); The Authors (2016-present)
Fractal geometry has been utilized as a means of mathematically describing various natural environments ranging from British coastlines to the Wisconsin wilderness. Despite the use of fractal geometry in environmental analysis, it has yet to be studied systematically in human navigational research. In order to establish a relationship between the fractal dimension of a natural landscape and humans' ability to navigate such spaces, we conducted an experiment using virtual environments that simulated the fractal properties of nature. In this experiment, participants were instructed to explore a series of circular island environments by controlling a virtual avatar from a first person perspective. The island environments were generated such that the feature edges were of fractal dimensions (D) varying from 1.0 to 1.9, and the surface of the environment extended into three dimensions. Overlaid in the displayed environment was a map describing both the environmental topography, as well as the location of a target in the environment, indicated with a red dot. Participants were instructed to move their avatar to the area of the island they believed corresponded to the target on the map and press a button to indicate their arrival at the target's position. Twenty-two participants completed the task, with a mean of 65 trials completed each. Mean accuracy score was found to be highest on trials where the environment was within the fractal dimension range of D = 1.1-1.5, and indicative of uninformed guessing at D=1.0 and D=1.9. The low-to-mid range of fractal dimension that we find optimal here has previously been found to elicit high aesthetic ratings. This evidence supports a visual fluency theory in which there is an optimization of processing spatial information within the low-to-mid fractal dimension range.
Meeting abstract presented at VSS 2016
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