The VideoToolbox is a free collection of two hundred C subroutines for Macintosh computers that calibrates and controls the computer-display interface to create accurately specified visual stimuli. High-level platform-independent languages like MATLAB are best for creating the numbers that describe the desired images. Low-level, computer-specific VideoToolbox routines control the hardware that transforms those numbers into a movie. Transcending the particular computer and language, we discuss the nature of the computer-display interface, and how to calibrate and control it.

An adaptive psychometric procedure that places each trial at the current most probable Bayesian estimate of threshold is described. The procedure takes advantage of the common finding that the human psychometric function is invariant in form when expressed as a function of log intensity. The procedure is simple, fast, and efficient, and may be easily implemented on any computer. The QUEST procedure was developed at the Kenneth Craik Laboratory of Cambridge University. A.B.W. was supported by an NIH postdoctoral fellowship, F32 EYO'219. D.G.P. was supported by a British Ministry of Defence grant, "Spatial noise spectra and target detection/recognition," to F. W. Campbell. We thank, Stanley Klein, Daniel Kersten, and Joseph Hall for helpful comments.A.B.W.'5 mailing addressis:

A letter in the peripheral visual field is much harder to identify in the presence of nearby letters. This is "crowding." Both crowding and ordinary masking are special cases of "masking," which, in general, refers to any effect of a "mask" pattern on the discriminability of a signal. Here we characterize crowding, and propose a diagnostic test to distinguish it from ordinary masking. In ordinary masking, the signal disappears. In crowding, it remains visible, but is ambiguous, jumbled with its neighbors. Masks are usually effective only if they overlap the signal, but the crowding effect extends over a large region. The width of that region is proportional to signal eccentricity from the fovea and independent of signal size, mask size, mask contrast, signal and mask font, and number of masks. At 4 deg eccentricity, the threshold contrast for identification of a 0.32 deg signal letter is elevated (up to six-fold) by mask letters anywhere in a 2.3 deg region, 7 times wider than the signal. In ordinary masking, threshold contrast rises as a power function of mask contrast, with a shallow log-log slope of 0.5 to 1, whereas, in crowding, threshold is a sigmoidal function of mask contrast, with a steep log-log slope of 2 at close spacing. Most remarkably, although the threshold elevation decreases exponentially with spacing, the threshold and saturation contrasts of crowding are independent of spacing. Finally, ordinary masking is similar for detection and identification, but crowding occurs only for identification, not detection. More precisely, crowding occurs only in tasks that cannot be done based on a single detection by coarsely coded feature detectors. These results (and observers' introspections) suggest that ordinary masking blocks feature detection, so the signal disappears, while crowding (like "illusory conjunction") is excessive feature integration - detected features are integrated over an inappropriately large area because there are no smaller integration fields - so the integrated signal is ambiguous, jumbled with the mask. In illusory conjunction, observers see an object that is not there made up of features that are. A survey of the illusory conjunction literature finds that most of the illusory conjunction results are consistent with the spatial crowding described here, which depends on spatial proximity, independent of time pressure. The rest seem to arise through a distinct phenomenon that one might call "temporal crowding," which depends on time pressure ("overloading attention"), independent of spatial proximity.

It is now emerging that vision is usually limited by object spacing rather than size. The visual system recognizes an object by detecting and then combining its features. 'Crowding' occurs when objects are too close together and features from several objects are combined into a jumbled percept. Here, we review the explosion of studies on crowding-in grating discrimination, letter and face recognition, visual search, selective attention, and reading-and find a universal principle, the Bouma law. The critical spacing required to prevent crowding is equal for all objects, although the effect is weaker between dissimilar objects. Furthermore, critical spacing at the cortex is independent of object position, and critical spacing at the visual field is proportional to object distance from fixation. The region where object spacing exceeds critical spacing is the 'uncrowded window'. Observers cannot recognize objects outside of this window and its size limits the speed of reading and search.Object recognition means calling a chair a chair, despite variations in style, viewpoint, rendering and surrounding clutter. Crowding is a breakdown of object recognition.Let us begin by sketching a popular two-step model of object recognition: feature detection and combination. Features are components of images that are detected independently 1-4 . They are typically simple and nonoverlapping. The first step in object recognition is feature detection 4 . Each neuron in the primary visual cortex responds when a feature matches its receptive field. Only the features that drive neurons hard enough are detected 5 . In the second step, the brain combines some of the detected features to recognize the object. This combining step (including 'integration', 'binding', 'segmentation', 'pooling', 'grouping', 'contour integration' and 'selective attention') is still mysterious 3,4,6-11 . Some objects are recognized through a single combining of features over the whole object, whereas other objects require separate combining over each of several regions of the object 12-14 . These distinct regions define object parts. In an object with multiple parts, each part must be recognized before they are all joined together.The best evidence that features are indivisible elements that we detect and combine is that, even with practice, people combine information across features much less well than within a feature. Searching for a conjunction of several features is usually much harder than searching for a single feature 3 . Despite reading a billion letters over a lifetime, people still recognize letters inefficiently, by detecting and combining many simple features rather than by detecting each letter as a whole 4,15 . Crowding is inappropriate feature combination that spoils object recognition (reviewed in refs. 16,17 ).

More than 20 years ago, Tanner [Ann. N.Y. Acad. Sci. 89, 752 (1961)] noted that observers asked to detect a signal act as though they are uncertain about the physical characteristics of the signal to be detected. The popular assumptions of probability summation and decision variable, taken together, imply this uncertainty. This paper defines and uncertainty model of visual detection that assumes that the observer is uncertain among many signals and chooses the likeliest. With only four parameters, the uncertainty model explains why d' is approximately a power function of contrast ("nonlinear transduction") and accurately predicts effects of summation, facilitation, noise, subjective criterion, and task for near-threshold contrast. Thus the uncertainty model offers a synthesis of much of our current understanding of visual contrast detection and discrimination.