Annual Review of Vision Science - Volume 6, 2020

An ideal observer is a theoretical model observer that performs a specific sensory-perceptual task optimally, making the best possible use of the available information given physical and biological constraints. An image-computable ideal observer (pixels in, estimates out) is a particularly powerful type of ideal observer that explicitly models the flow of visual information from the stimulus-encoding process to the eventual decoding of a sensory-perceptual estimate. Image-computable ideal observer analyses underlie some of the most important results in vision science. However, most of what we know from ideal observers about visual processing and performance derives from relatively simple tasks and relatively simple stimuli. This review describes recent efforts to develop image-computable ideal observers for a range of tasks with natural stimuli and shows how these observers can be used to predict and understand perceptual and neurophysiological performance. The reviewed results establish principled links among models of neural coding, computational methods for dimensionality reduction, and sensory-perceptual performance in tasks with natural stimuli.

In studying visual perception, we seek to develop models of processing that accurately predict perceptual judgments. Much of this work is focused on judgments of discrimination, and there is a large literature concerning models of visual discrimination. There are, however, non-threshold visual judgments, such as judgments of the magnitude of differences between visual stimuli, that provide a means to bridge the gap between threshold and appearance. We describe two such models of suprathreshold judgments, maximum likelihood difference scaling and maximum likelihood conjoint measurement, and review recent literature that has exploited them.

In visual search tasks, observers look for targets among distractors. In the lab, this often takes the form of multiple searches for a simple shape that may or may not be present among other items scattered at random on a computer screen (e.g., Find a red T among other letters that are either black or red.). In the real world, observers may search for multiple classes of target in complex scenes that occur only once (e.g., As I emerge from the subway, can I find lunch, my friend, and a street sign in the scene before me?). This article reviews work on how search is guided intelligently. I ask how serial and parallel processes collaborate in visual search, describe the distinction between search templates in working memory and target templates in long-term memory, and consider how searches are terminated.

Scene processing is fundamentally influenced and constrained by spatial layout and spatial associations with objects. However, semantic information has played a vital role in propelling our understanding of real-world scene perception forward. In this article, we review recent advances in assessing how spatial layout and spatial relations influence scene processing. We examine the organization of the larger environment and how we take full advantage of spatial configurations independently of semantic information. We demonstrate that a clear differentiation of spatial from semantic information is necessary to advance research in the field of scene processing.