The central theme of research in our lab is to understand how people with low vision see and recognize the world around them. To this end, we study how the visual system deals with four major aspects of visual deficits that often occur in low vision: blur, low contrast, visual field loss, and abnormal binocular interaction. We are particularly interested in how these deficits affect daily visual activities such as reading, object/face recognition or navigation, and whether there are any perceptual and cortical changes associated with these deficits. We are also interested in developing effective training/rehabilitative regimens to improve the visual function of low vision patients. Our research involves psychophysical, eye tracking, fMRI and computational investigations in normal and visually impaired humans (e.g., macular degeneration, glaucoma, amblyopia and other disorders of the retina and optic nerve). Our research is funded by the National Institutes of Health (NIH) and Research to Prevent Blindness (RPB).

Here are a few featured studies: 

Using deep learning, we show that human contrast sensitivity (i.e., a building block of human pattern vision) can be reliably decoded from retinal structural (retinal imaging) data. The activation maps from the network help us identify and localize the exact retinal layers closely linked to human contrast sensitivity, i.e., the ganglion cell layers, where ganglion cell bodies and its dendritic structures are located.

Here we show that slower reading speed in glaucoma is closely related to the shrinkage of the visual span, the number of letters that can be reliably recognized in one glance.

Here we show that the variation in the sampling density of RGCs across the human retina is closely matched to the variation in the extent of spatial integration required for either luminance detection or object recognition. Our empirical data combined with the simulation results of computational models suggest that a fixed number of RGCs subserves spatial integration of visual input, independent of the visual-field location.

Crowding, the inability to recognize an individual object in clutter, is considered a major impediment to object recognition in peripheral vision. Despite its significance, the cortical loci of crowding are not well understood. In particular, the role of the primary visual cortex (V1) remains unclear. Here we utilize a diagnostic feature of crowding to identify the earliest cortical locus of crowding.

People with a central scotoma often adopt an eccentric retinal location (Preferred Retinal Locus, PRL) for fixation. Here we develop a novel training paradigm as a model system to study the nature of the PRL formation and its impacts on visual function. The training paradigm is designed to effectively induce a PRL at any intended retinal location by integrating oculomotor control and pattern recognition.

How would the oculomotor system adjust to a loss of foveal vision (central scotoma)? Here we show that the oculomotor system can spontaneously and rapidly adopt a peripheral locus for fixation and can rereference saccades to this locus in normally sighted individuals whose central vision is blocked by an artificial scotoma.