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. 2019 Feb 7;9:1637. doi: 10.1038/s41598-018-37631-2

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Gabor wavelet analysis results for stimulus images of three regularity levels (0, 8.3 and 20.3 jitter min) shown as polar image plots of average energy (calculated as for Fig. 7A) as a joint function of spatial frequency (distance from center) and orientation (polar angle). The polar images are generated using the Matlab function ‘polarPcolor’ created by Etienne Cheynet (www.mathworks.com/matlabcentral/fileexchange/49040-pcolor-in-polar-coordinates). Average energy responses were obtained across a broad range of spatial frequencies, and at each of 12 orientations of Gabor filters (0–165 degrees, with a 15-degree spacing, and 180 deg-opposite values re-plotted symmetrically). Higher energy responses are concentrated around the grid orientations (vertical and horizontal) and peak SF (1.08 log cycles/image) for regular texture (left), with the energy gradually spreading out across orientations with increasing irregularity (middle to right).