1. Neuroscience
  2. Physics of Living Systems

Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures

  1. Tiberiu Tesileanu  Is a corresponding author
  2. Mary M Conte
  3. John J Briguglio
  4. Ann M Hermundstad
  5. Jonathan D Victor
  6. Vijay Balasubramanian
  1. Flatiron Institute, United States
  2. Weill Cornell Medical College, United States
  3. Howard Hughes Medical Institute, United States
  4. University of Pennsylvania, United States
https://doi.org/10.7554/eLife.54347
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  1. Tiberiu Tesileanu
  2. Mary M Conte
  3. John J Briguglio
  4. Ann M Hermundstad
  5. Jonathan D Victor
  6. Vijay Balasubramanian
(2020)
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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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.

The following previously published data sets were used

Article and author information

Author details

  1. Tiberiu Tesileanu

    Center for Computational Biology, Flatiron Institute, New York, United States
    For correspondence
    ttesileanu@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3107-3088

  2. Mary M Conte

    Brain and Mind Institute, Weill Cornell Medical College, New York, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. John J Briguglio

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Ann M Hermundstad

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0377-0516

  5. Jonathan D Victor

    Brain and Mind Research Institute and Department of Neurology, Weill Cornell Medical College, New-York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9293-0111

  6. Vijay Balasubramanian

    Department of Physics, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6497-3819

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.

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.

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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  1. Tiberiu Tesileanu
  2. Mary M Conte
  3. John J Briguglio
  4. Ann M Hermundstad
  5. Jonathan D Victor
  6. Vijay Balasubramanian
(2020)
Efficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures
eLife 9:e54347.
https://doi.org/10.7554/eLife.54347

Share this article

https://doi.org/10.7554/eLife.54347

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Further reading

    1. Neuroscience

    Variance predicts salience in central sensory processing

    Ann M Hermundstad, John J Briguglio ... Gašper Tkačik
    Research Article Updated

    Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime—when sampling limitations constrain performance—efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.