Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures
- Flatiron Institute, United States
- Weill Cornell Medical College, United States
- Howard Hughes Medical Institute, United States
- University of Pennsylvania, United States
Abstract
Previously, in (Hermundstad et al., 2014), we showed that when sampling is limiting, the efficient coding principle leads to a 'variance is salience' hypothesis, and that this hypothesis accounts for visual sensitivity to binary image statistics. Here, using extensive new psychophysical data and image analysis, we show that this hypothesis accounts for visual sensitivity to a large set of grayscale image statistics at a striking level of detail, and also identify the limits of the prediction. We define a 66-dimensional space of local grayscale light-intensity correlations, and measure the relevance of each direction to natural scenes. The 'variance is salience' hypothesis predicts that two-point correlations are most salient, and predicts their relative salience. We tested these predictions in a texture-segregation task using un-natural, synthetic textures. As predicted, correlations beyond second order are not salient, and predicted thresholds for over 300 second-order correlations match psychophysical thresholds closely (median fractional error < 0:13).
Data availability
All the code and data necessary to reproduce the results from the manuscript are available at https://github.com/ttesileanu/TextureAnalysis.
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Natural Images from the Birthplace of the Human Eyehttps://doi.org/10.1371/journal.pone.0020409.
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Independent Component Filters of Natural Images Compared with Simple Cells in Primary Visual Cortexhttps://doi.org/10.1098/rspb.1998.0303.
Article and author information
Author details
Vijay Balasubramanian
Funding
US-Israel Binational Science Foundation (2011058)
- Vijay Balasubramanian
National Eye Institute (EY07977)
- Mary M Conte
- Jonathan D Victor
- Vijay Balasubramanian
Swartz Foundation
- Tiberiu Tesileanu
Howard Hughes Medical Institute
- John J Briguglio
- Ann M Hermundstad
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Stephanie Palmer, University of Chicago, United States
Ethics
Human subjects: This work was carried out with the subjects' informed consent, and in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) and the approval of the Institutional Review Board of Weill Cornell. The IRB protocol number is 0904010359.
Version history
- Received: December 12, 2019
- Accepted: July 31, 2020
- Accepted Manuscript published: August 3, 2020 (version 1)
- Version of Record published: September 16, 2020 (version 2)
Copyright
© 2020, Tesileanu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures
eLife 9:e54347.
https://doi.org/10.7554/eLife.54347
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