Visual clutter concerns designers of user interfaces and information visualizations. This should not surprise visual perception researchers because excess and/or disorganized display items can cause crowding, masking, decreased recognition performance due to occlusion, greater difficulty at both segmenting a scene and performing visual search, and so on. Given a reliable measure of the visual clutter in a display, designers could optimize display clutter. Furthermore, a measure of visual clutter could help generalize models like Guided Search (J. M. Wolfe, 1994) by providing a substitute for "set size" more easily computable on more complex and natural imagery. In this article, we present and test several measures of visual clutter, which operate on arbitrary images as input. The first is a new version of the Feature Congestion measure of visual clutter presented in R. Rosenholtz, Y. Li, S. Mansfield, and Z. Jin (2005). This Feature Congestion measure of visual clutter is based on the analogy that the more cluttered a display or scene is, the more difficult it would be to add a new item that would reliably draw attention. A second measure of visual clutter, Subband Entropy, is based on the notion that clutter is related to the visual information in the display. Finally, we test a third measure, Edge Density, used by M. L. Mack and A. Oliva (2004) as a measure of subjective visual complexity. We explore the use of these measures as stand-ins for set size in visual search models and demonstrate that they correlate well with search performance in complex imagery. This includes the search-in-clutter displays of J. M. Wolfe, A. Oliva, T. S. Horowitz, S. Butcher, and A. Bompas (2002) and Bravo and Farid (2004), as well as new search experiments. An additional experiment suggests that color variability, accounted for by Feature Congestion but not the Edge Density measure or the Subband Entropy measure, does matter for visual clutter.

Peripheral vision provides a less faithful representation of the visual input than foveal vision. Nonetheless, we can gain a lot of information about the world from our peripheral vision, for example in order to plan eye movements. The phenomenon of crowding shows that the reduction of information available in the periphery is not merely the result of reduced resolution. Crowding refers to visual phenomena in which identification of a target stimulus is significantly impaired by the presence of nearby stimuli, or flankers. What information is available in the periphery? We propose that the visual system locally represents peripheral stimuli by the joint statistics of responses of cells sensitive to different position, phase, orientation, and scale. This “textural” representation by summary statistics predicts the subjective “jumble” of features often associated with crowding. We show that the difficulty of performing an identification task within a single pooling region using this representation of the stimuli is correlated with peripheral identification performance under conditions of crowding. Furthermore, for a simple stimulus with no flankers, this representation can be adequate to specify the stimulus with some position invariance. This provides evidence that a unified neuronal mechanism may underlie peripheral vision, ordinary pattern recognition in central vision, and texture perception. A key component of our methodology involves creating visualizations of the information available in the summary statistics of a stimulus. We call these visualizations “mongrels” and show that they are highly useful in examining how the early visual system represents the visual input. Mongrels enable one to study the “equivalence classes” of our model, i.e., the sets of stimuli that map to the same representation according to the model.

This review discusses several pervasive myths about peripheral vision, as well as what is actually true: Peripheral vision underlies a broad range of visual tasks, in spite of its significant loss of information. New understanding of peripheral vision, including likely mechanisms, has deep implications for our understanding of vision. From peripheral recognition to visual search, from change blindness to getting the gist of a scene, a lossy but relatively fixed peripheral encoding may determine the difficulty of many tasks. This finding suggests that the visual system may be more stable, and less dynamically changing as a function of attention, than previously assumed.

Vision is an active process: we repeatedly move our eyes to seek out objects of interest and explore our environment. Visual search experiments capture aspects of this process, by having subjects look for a target within a background of distractors. Search speed often correlates with target-distractor discriminability; search is faster when the target and distractors look quite different. However, there are notable exceptions. A given discriminability can yield efficient searches (where the target seems to “pop-out”) as well as inefficient ones (where additional distractors make search significantly slower and more difficult). Search is often more difficult when finding the target requires distinguishing a particular configuration or conjunction of features. Search asymmetries abound. These puzzling results have fueled three decades of theoretical and experimental studies. We argue that the key issue in search is the processing of image patches in the periphery, where visual representation is characterized by summary statistics computed over a sizable pooling region. By quantifying these statistics, we predict a set of classic search results, as well as peripheral discriminability of crowded patches such as those found in search displays.

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How efficient is visual search in real scenes? In searches for targets among arrays of randomly placed distractors, efficiency is often indexed by the slope of the reaction time (RT) × Set Size function. However, it may be impossible to define set size for real scenes. As an approximation, we hand-labeled 100 indoor scenes and used the number of labeled regions as a surrogate for set size. In Experiment 1, observers searched for named objects (a chair, bowl, etc.). With set size defined as the number of labeled regions, search was very efficient (~5 ms/item). When we controlled for a possible guessing strategy in Experiment 2, slopes increased somewhat (~15 ms/item), but they were much shallower than search for a random object among other distinctive objects outside of a scene setting (Exp. 3: ~40 ms/item). In Experiments 4–6, observers searched repeatedly through the same scene for different objects. Increased familiarity with scenes had modest effects on RTs, while repetition of target items had large effects (>500 ms). We propose that visual search in scenes is efficient because scene-specific forms of attentional guidance can eliminate most regions from the “functional set size” of items that could possibly be the target.

Visual search for a moving target among stationary distractors is more efficient than searching for a stationary target among moving distractors, and searching for a fast target among slow distractors is more efficient than vice versa. This indicates that the ease of search for a target with a particular motion is not determined simply by the difference between target and distractor velocities. We suggest a simple model for predicting ease of search for a unique motion, based upon a quantitative measure of target saliency. Essentially, search will be easier the more the target motion deviates from the general pattern of velocities in the scene. Our model predicts a number of well-known motion search phenomena, and suggests that one control for target saliency as well as target discriminability when drawing conclusions about visual system mechanisms from search experiments.

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