Nature in motion: The tuning of the visual system to the spatiotemporal properties of natural scenes
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Updated: Sep 22, 2025

Visualizing Visual Adaptation
Published on: April 24, 2017
Michelle M Roberts1,2,3, Mark M Schira1,4,5, Branka Spehar2,6
1School of Psychology, University of Wollongong, Wollongong, NSW 2500, Australia.
Abstract:
Natural scenes contain several statistical regularities despite their superficially diverse appearances (e.g., mountains, rainforests, deserts). First, they exhibit a unique distribution of luminance intensities decreasing across spatial frequency, known as the 1/fα amplitude spectrum (α ≈ 1). Additionally, natural scenes share consistent geometric properties, comprising similar densities of structure across multiple scales-a property classifying them as fractal (e.g., how the branching patterns of rivers and trees appear similar irrespective of scale). These two properties are intimately related and correlate strongly in natural scenes. However, research using thresholded noise images suggests that spatially, the human visual system is preferentially tuned to natural scene structure more so than 1/fα spectra. It is currently unclear whether this dependency on natural geometry extends to the temporal domain. We used a psychophysics task to measure discrimination sensitivity toward two types of synthetic noise movies: gray scale and thresholded (N = 60). Each movie type shared the same geometric properties (measured fractal D), but substantially differing spectral properties (measured α). In both space and time, we observe a characteristic dependency on stimulus structure across movie types, with sensitivity peaking for stimuli with natural geometry despite having altered 1/fα spectra. Although only measured behaviorally, our findings may imply that the neural processes underlying this tuning have developed to be sensitive to the most stable signal in our natural environment-structure (e.g., the structural properties of a tree are consistent from morning to night despite illumination changes across time points).
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