Visual discomfort and variations in chromaticity in art and nature
Cite this dataset
Penacchio, Olivier et al. (2021). Visual discomfort and variations in chromaticity in art and nature [Dataset]. Dryad. https://doi.org/10.5061/dryad.bcc2fqzc5
Visual discomfort is related to the statistical regularity of visual images. The contribution of luminance contrast to visual discomfort is well understood and can be framed in terms of a theory of efficient coding of natural stimuli, and linked to metabolic demand. While colour is important in our interaction with nature, the effect of colour on visual discomfort has received less attention. In this study, we build on the established association between visual discomfort and differences in chromaticity across space. We average the local differences in chromaticity in an image and show that this average is a good predictor of visual discomfort from the image. It accounts for part of the variance left unexplained by variations in luminance. We show that the local chromaticity difference in uncomfortable stimuli is high compared to that typical in natural scenes, except in particular infrequent conditions such as the arrangement of colourful fruits against foliage. Overall, our study discloses a new link between visual ecology and discomfort whereby discomfort arises when adaptive perceptual mechanisms are overstimulated by specific classes of stimuli rarely found in nature.
This repository contains the raw data for the three experiments in the paper, Matlab code to compute the colour metric ("average chromaticity difference") and to recolour the stimuli as done in Experiment 3, the computed values of the metric for all the experments, and R code for the statistical analysis of the three experiments (please see readMe.txt for details).
NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation, Award: 26282
R15 AREA award from the National Institute of Mental Health, Award: 122935
NSF EPSCoR grant, Award: 1632849