Visual search in noise and natural backgrounds.

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.