2020 Edgar D. Tillyer Award Lecture: Wilson S. Geisler

Thursday, November 18, 12:00 – 14:00 ET // Register here

The Edgar D. Tillyer Award is presented to an individual who has performed distinguished work in the field of vision, including (but not limited to) the optics, physiology, anatomy or psychology of the visual system. The 2020 Tillyer Award is presented to Wilson S. Geisler, University of Texas at Austin, for pioneering theories of optimal visual processing that bring together scene statistics, physiological constraints, and task requirements to gain a new understanding of perceptual functions and eye movements.


Visual search in noise and natural backgrounds

Wilson S. Geisler, University of Texas at Auston

I will describe evidence for a theory of covert visual search developed within the framework of natural scene statistics and Bayesian statistical decision theory. The theory is unique in several ways: (1) it directly takes into account the statistical properties of natural images, (2) it takes into account the variation in neural processing with retinal location, as well as other known properties of the visual system, and hence contains almost no free parameters, and (3) it includes a principled attentional mechanism that efficiently allocates sensitivity gain across the visual field. This latter mechanism was discovered in experiments measuring covert search in white-noise backgrounds, where the target could appear anywhere within a large search area. In a separate experiment, target detectability (d’) was measured across the visual field when the target location was cued/known. The shape of this “d’ map” was consistent with the theory. The overall performance in the covert search task was also predicted quite well from this d’ map, with no free parameters, assuming parallel unlimited-capacity processing. However, paradoxically, detection accuracy was low in the foveal region, even though it was predicted to be very high. We show that this “foveal neglect” is the expected consequence of efficiently allocating a fixed total attentional sensitivity gain across neurons in visual cortex, rather than across locations in visual space (the traditional assumption). Furthermore, the theory predicts the detailed pattern of covert search performance in the white-noise backgrounds. Finally, I will describe predictions of the theory for search in natural images.

About our speaker:

Wilson (Bill) Geisler is the David Wechsler Regents Chair in Psychology at the University of Texas at Austin. He obtained an undergraduate degree in psychology from Stanford University in 1971 and a doctoral degree in mathematical and experimental psychology from Indiana University in 1975. He is a fellow of Optica (formerly OSA), a fellow of the Society of Experimental Psychologists, and a member of the National Academy of Sciences. Geisler’s research combines behavioral studies, neurophysiological studies, studies of natural stimuli, mathematical analysis, and computational modeling. He is best known for his work on the mathematics of how to perform perceptual tasks optimally (“ideal observer theory”), on the relationship between the statistical properties of natural stimuli and visual performance, on the nature of eye movements in natural tasks, and on the relationship between visual performance and the neurophysiology of the visual system.