Revisiting the Dress: Lessons for the Study of Qualia and ScienceShare
Good science takes time and usually raises more questions than it answers. This is no exception.
When #thedress first came out in February 2015, vision scientists had plenty of ideas why some people might be seeing it differently than others, but no one knew for sure. Now we have some evidence as to what might be going on. 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.
*Open science: Shortly (once I put the infrastructure in place), data and analysis code will be made openly available for download. Also, the paper was published – on purpose – in an open-access journal.
Good science takes time and usually raises more questions than it answers. This is no exception. If you want to help us out, take this brief 5-minute survey. The more data we have, the more useful the data we already have becomes.
This post also appeared at Pascal's website Pascal's Pensées