Crowding--an essential bottleneck for object recognition: a mini-review

Abstract

Crowding, generally defined as the deleterious influence of nearby contours on visual discrimination, is ubiquitous in spatial vision. Crowding impairs the ability to recognize objects in clutter. It has been extensively studied over the last 80 years or so, and much of the renewed interest is the hope that studying crowding may lead to a better understanding of the processes involved in object recognition. Crowding also has important clinical implications for patients with macular degeneration, amblyopia and dyslexia. There is no shortage of theories for crowding-from low-level receptive field models to high-level attention. The current picture is that crowding represents an essential bottleneck for object perception, impairing object perception in peripheral, amblyopic and possibly developing vision. Crowding is neither masking nor surround suppression. We can localize crowding to the cortex, perhaps as early as V1; however, there is a growing consensus for a two-stage model of crowding in which the first stage involves the detection of simple features (perhaps in V1), and a second stage is required for the integration or interpretation of the features as an object beyond V1. There is evidence for top-down effects in crowding, but the role of attention in this process remains unclear. The strong effect of learning in shrinking the spatial extent of crowding places strong constraints on possible models for crowding and for object recognition. The goal of this review is to try to provide a broad, balanced and succinct review that organizes and summarizes the diverse and scattered studies of crowding, and also helps to explain it to the non-specialist. A full understanding of crowding may allow us to understand this bottleneck to object recognition and the rules that govern the integration of features into objects.

Fig. 1. Crowding

The reader can experience crowding by fixating the dot, and trying to identify one letter: in isolation (a), surrounded by 4 random flanking letters (b), surrounded by 2 horizontally placed random flanking letters (c), surrounded by 2 vertically placed random flanking letters (d).

Fig 2

The 2-dimensional shape of “crowding” in foveal (the small dot in the center) and peripheral vision (at 2.5. 5 and 10 degrees). From Toet & Levi, 1992.

Fig 3

The threshold resolution for feature conjunctions vs feature identification for normal fovea (open circle) periphery (solid circle) and the central field of a strabismic/anisometropic amblyope (square). The dotted line is the prediction of a model constrained by properties of V1. The + shows the size of a "perceptive hypercolumn". (Data and model replotted from Neri & Levi, 2006).

Fig. 4

Reading rate versus size (top) and spacing (bottom) for each eye of an amblyope (strabismic & anisometropic). The small symbols are for normal spacing (1.1 × size, these data also appear in Fig. 4) and the large symbols are for double spacing (2.2 × size). The thick lines are the best fit of the uncrowded-span model (Pelli et al., 2007) to the normal-spacing data. The thin lines in the top graph are copies, shifted left (arrows) by a factor of 2, to predict the double-spacing data if spacing limits reading.

Fig. 5. Learning to “uncrowd”

This figure re-plots the critical spacing (extent of crowding in degrees) for each observer before (abscissa) and after (ordinate) extended practice (6000 trials of identifying flanked letters). Re-plotted from Chung, 2007.