dmacleod

Personal

Bio

I try to understand the process of human vision in physiological or mechanistic terms, using the tools of psychophysics in conjunction with electrophysiological and anatomical data from animals. This involves tracing the sequence of operations that occurs as information flows from retina to brain. 

One representative project asks: Why isn't vision perfect? Bypassing optical losses by using interference fringe patterns directly generated on the retina as stimuli, we have shown that considerable information about the finest details may survive in the retinal image but be lost in neural processing, and that all of this neural loss occurs later than the primary sensitivity-regulating processes of cone vision (which must therefore be strictly local--either internal to the cones or fed by single cones). Most recently and most surprisingly, we find that unresolvably fine patterns can activate primary visual cortex and there produce pattern-specific aftereffects (such as tilt aftereffects or orientation-selective losses of visual contrast sensitivity), even though the subject can not discriminate their orientation. It follows that activation of single orientation-selective neurons in visual cortex is not a sufficient condition for perception of orientation, and that our stimuli are penetrating the visual system as far as primary visual cortex (the region of cortex thought to be most critical for the perception of detail), yet fail to penetrate to conscious experience. We hope that this can be confirmed by MRI experiments using these laser stimuli.

In another line of work, the neural coding of color and luminance is being investigated, both absolutely and in its dependence on context, with attention to known physiological nonlinearities. We are trying to characterize quantitatively the nonlinearities in the neural representation of color, and relate them on the one hand to mathematically optimal solutions to the problem of representing colors with the sort of distribution that is environmentally typical, and on the other hand to color difference data through a neurally constrained form of multi-dimensional scaling.

URL

http://psy2.ucsd.edu/~dmacleod/