Fractal Fluency: An Intimate Relationship Between the Brain and Processing of Fractal Stimuli
Humans are continually exposed to the rich visual complexity generated by the repetition of fractal patterns at different size scales. Fractals are prevalent in natural scenery and in patterns generated by artists and mathematicians. In this chapter, we will investigate the powerful significance of fractals for the human visual system. In particular, we propose that fractals with midrange complexity (D = 1.3–1.5 measured on a scale between D = 1.1 for low complexity and D = 1.9 for high complexity) play a unique role in our visual experiences because the visual system has adapted to these prevalent natural patterns. This adaption is evident at multiple stages of the visual system, ranging from data acquisition by the eye to processing of this data in the higher visual areas of the brain. For example, eye-movement studies show that the eye traces out mid-D fractal trajectories that facilitate visual searches through fractal scenery. Furthermore, quantitative electroencephalography (qEEG) and preliminary fMRI investigations demonstrate that mid-D fractals induce distinctly different neurophysiological responses than less prevalent fractals. Based on these results, we will discuss a fluency model in which the visual system processes mid-D fractals with relative ease. This fluency optimizes the observer’s capabilities (such as enhanced attention and pattern recognition) and generates an aesthetic experience accompanied by a reduction in the observer’s physiological stress levels. In addition to exploring the fundamental science of our visual system, the results have important practical consequences. For example, mid-D fractals have the potential to address stress-related illnesses.
KeywordsFractals Complexity Perception Stress reduction qEEG
We thank our collaborators Cooper Boydston, Colin Clifford, Caroline Hagerhall, and Margaret Sereno for their useful discussions. This work was supported by an Australian Research Council grant DP120103659 to BS and RPT.
- 4.Fairbanks MS, Taylor RP. Scaling analysis of spatial and temporal patterns: from the human eye to the foraging albatross. In: Non-linear dynamical analysis for the behavioral sciences using real data. Boca Raton: Taylor and Francis Group; 2011.Google Scholar
- 7.Hagerhall CM, Laike T, Küller M, Marcheschi E, Boydston C, Taylor RP. Human physiological benefits of viewing nature: EEG response to exact and statistical fractal patterns. J Nonlinear Dyn Psychol Life Sci. 2015;19:1–12.Google Scholar
- 11.Kolb B, Whishaw IQ. Fundamentals of human neuropsychology. New York: Worth Publishers; 2003.Google Scholar
- 14.Spehar B, Clifford C, Newell B, Taylor RP. Universal aesthetic of fractals. Chaos Graph. 2003;37:813–20.Google Scholar
- 15.Spehar B, Taylor RP. Fractals in art and nature: why do we like them? SPIE Electron Imaging. 2013;865:1–18.Google Scholar
- 17.Taylor RP. Splashdown. New Sci. 1998;2144:30–1.Google Scholar
- 23.Taylor RP, Sprott JC. Biophilic fractals and the visual journey of organic screen-savers. J Non-linear Dyn Psychol Life Sci. 2008;12:117–29.Google Scholar
- 25.Ulrich RS. Biophilia, biophobia and natural landscapes. In: The biophilia hypothesis. Washington, DC: Island Press; 1993.Google Scholar
- 26.Ulrich RS, Simons RF. Recovery from stress during exposure to everyday outdoor environments. Proc EDRA. 1986;17:115–22.Google Scholar
- 28.Zeki S. Inner vision: an exploration of art and the brain. Oxford: Oxford University Press; 1999.Google Scholar