Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2021 May 12;11:10074. doi: 10.1038/s41598-021-89277-2

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© The Author(s) 2021

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

PMC Copyright notice

Using micro-pattern amplitude to vary global luminance difference. (a) Bootstrapped SDT psychometric function fits (200 bootstrapped re-samplings) with 90% confidence intervals of observer performance as a function of proportion unbalanced micropatterns (left panels) and absolute luminance difference (right panels). This shows that identical luminance differences give rise to significantly different levels of observer performance for the three Michaelson contrasts (right panels), i.e. global luminance difference is a very poor predictor of performance. Instead, observer performance is much better predicted by the proportion of unbalanced micro-patterns, (almost) irrespective of micro-pattern amplitude (left panels). (b) Data from Experiment 3 (black dots) and fits of the additive (red) and divisive (blue) image-computable signal detection theory models (IC-SDT) to the data. Each observer was tested at three different maximum micro-pattern amplitudes, which correspond to different Michaelson contrasts (0.2, 0.4, 0.8) of the stimuli. We see that a model incorporating a global luminance difference computation followed by contrast normalization (blue) provides an excellent fit to this data.