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Revisiting the Dress: Illumination Assumptions Account for Individual Differences
It’s been a little over two years since “The Dress,” a photo that become a viral phenomenon when viewers disagreed over the color of the stripped dress – was it black and blue or white and gold. Since then, multiple papers have been published each trying to answer the question of how people could have such dramatically different perceptions?
In my study published in the April issue of Journal of Vision, I address a possible explanation empirically by reporting on data from ∼13,000 observers who were surveyed online. We show that assumptions about the illumination of the dress—i.e., whether the stimulus was illuminated by natural or artificial light or whether it was in a shadow—strongly affects the subjective interpretation of observers, compared to demographic factors, such as age or gender, which have a relatively smaller influence. We interpret these findings in a Bayesian framework by also showing that prior exposure to long- or short-wavelength lights due to circadian type shapes the subjective experience of the dress stimulus in theoretically expected ways. Read the journal article here.
The illumination source in the original image of the dress is unclear. It is unclear whether the image was taken in daylight or artificial light, and if the light comes from above or behind. If things are unclear, people assume that it was illuminated with the light that they have seen more often in the past. In general, the human visual system has to take the color of the illumination into account when determining the color of objects. This is called color constancy.
That’s why a sweater looks largely the same inside a house and outside, even though the wavelengths hitting the retina are very different (due to the different illumination). So if someone assumes blue light, they will mentally subtract that and see the image as yellow. If someone assumes yellow light, they will mentally subtract it and see blue. The sky is blue, so if someone assumes daylight, they will see the dress as gold.
Artificial incandescent light is relatively long-wavelength (appearing yellow-ish), so if someone assumes that, they will see it as blue. People who get up in the morning see more daylight in their lifetime and tend to see the dress as white and gold, people who get up later and stay up late see more artificial light in their lifetime and tend to see the dress as black and blue.
This is a flashy result. Which should be concerning because scientific publishing seems to have traded off rigor with appeal in the past. However, I really do not believe that this was the case here.
In terms of scientific standards, the paper has the following features:
- High power: > 13,000 participants
- Conservative p-value: Voluntarily adopted p < 0.01 as a reasonable significance threshold to guard against multiple comparison issues.
- Internal replication prior to publication: This led to a publication delay of over a year, but it is important to be sure.
- No excluding of participants or flexible stopping: Everyone who had taken the survey by the time of lodging the paper for review at the journal was included.
- #CitizenScience: As this effect holds up “in the wild”, it is reasonable to assume that it doesn’t fall apart outside of carefully controlled laboratory conditions.
If you’d like to participate in Dr. Wallisch’s next study about #thedress and similar images, take his survey here.
About the Author
Pascal Wallisch received his PhD from the University of Chicago, did postdoctoral work at the Center for Neural Science at New York University, and currently serves as a clinical assistant professor of Psychology at New York University. His research interests are at the intersection of Psychology and Neuroscience, specifically Cognitive and Computational Neuroscience. His current work focuses on motion perception, autism and the appraisal of film.
He is the co-author of MATLAB for Neuroscientists, the best-selling study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology, now in its second edition.
He recently co-authored the book Neural Data Science with Eric Lee Nylen, which published in March 2017. This book is a primer with MATLAB and Python that presents important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience. The complete book is available for download today on ScienceDirect here.
If you prefer to purchase a print copy, you can visit the Elsevier website here. Use discount code STC317 at checkout and save up to 30%!
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