Pfizer COVID-19 vaccine appointments are available to our patients. Sign up for Connect today to schedule your vaccination. Continue your routine care with us by scheduling an in-person appointment or Video Visit.

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

TitleEfficient coding of natural scene statistics predicts discrimination thresholds for grayscale textures.
Publication TypeJournal Article
Year of Publication2020
AuthorsTesileanu T, Conte MM, Briguglio JJ, Hermundstad AM, Victor JD, Balasubramanian V
JournalElife
Volume9
Date Published2020 Aug 03
ISSN2050-084X
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).

DOI10.7554/eLife.54347
Alternate JournalElife
PubMed ID32744505
Grant List2011058 / / US-Israel Binational Science Foundation /
EY07977 / EY / NEI NIH HHS / United States