The human retina — the part of the eye that converts incoming light into electrochemical signals — has about 100 million light-sensitive cells. So retinal images contain a huge amount of data. High-level visual-processing tasks — like object recognition, gauging size and distance, or calculating the trajectory of a moving object — couldn’t possibly preserve all that data: The brain just doesn’t have enough neurons. So vision scientists have long assumed that the brain must somehow summarize the content of retinal images, reducing their informational load before passing them on to higher-order processes.
At the Society of Photo-Optical Instrumentation Engineers’ Human Vision and Electronic Imaging conference on Jan. 27, Ruth Rosenholtz, a principal research scientist in the Department of Brain and Cognitive Sciences, presented a new mathematical model of how the brain does that summarizing. The model accurately predicts the visual system’s failure on certain types of image-processing tasks, a good indication that it captures some aspect of human cognition.