The Brainard Lab studies human vision, both experimentally and through computational modeling of visual processing. Our primary concern is with how the visual system estimates object properties from the information available in the light signal incident at the eye. To study this general problem, we conduct psychophysical experiments to investigate questions such as how object color appearance is related to object surface properties under a wide range of illumination conditions and how color is used to identify objects, and formulate computational models of the results. In addition, we are interested in developing machine visual systems that can mimic human performance and in understanding the neural mechanisms of vision.
Representative Recent Publications
(See Publications for more, or download Brainard's CV in PDF Format. Or visit Brainard's Google Scholar Page).
Spitschan, M., Bock, A. S., Ryan, J., Frazzetta, G., Brainard, D. H., Aguirre, G. K. (2017). The human visual cortex response to melanopsin-directed stimulation is accompanied by a distinct perceptual experience. PNAS, 114(46), 12291–12296, doi: 10.1073/pnas.1711522114. Download PDF. Press release.
Cooper, R. F., Tuten, W. S., Dubra, A., Brainard, D. H., Morgan, J. I. W. (2017). Non-invasive assessment of human cone photoreceptor function. Biomedical Optics Express, 8(11), 5098-5112, doi: 10.1364/BOE.8.005098. https://www.osapublishing.org/boe/abstract.cfm?uri=boe-8-11-5098.
Lindsey, D. T., Brown, A. M., Brainard D. H. & Apicella, C. A. (2015). Hunter-gatherer color naming provides new insight into the evolution of color terms. Current Biology, 25, 2441-2446, doi: 10.1016/j.cub.2015.08.006. Download PDF. Press coverage: Science World Report, Medical Daily, Phys.org.
Radonjić A., Cottaris N. P., & Brainard D. H. (2015). Color constancy in a naturalistic, goal directed task. Journal of Vision, 15(13):3, http://jov.arvojournals.org/article.aspx?articleid=2436912, doi:10.1167/15.13.3.
Benson, N. C, Manning, J. R. & Brainard, D. H. (2014). Unsupervised learning of cone spectral classes from natural images. PLoS Computational Biology, 10(6): e1003652, http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003652#abstract0, doi:10.1371/journal.pcbi.1003652. This work is mentioned in this article on the evolution of color vision, in The Scientist.
Here is a link to our list of preregistered experiments.