Visual crowding: a fundamental limit on conscious perception and object recognition

doi:10.1016/j.tics.2011.02.005

Trends in Cognitive Sciences

Volume 15, Issue 4, April 2011, Pages 160-168

https://doi.org/10.1016/j.tics.2011.02.005 Get rights and content

Crowding, the inability to recognize objects in clutter, sets a fundamental limit on conscious visual perception and object recognition throughout most of the visual field. Despite how widespread and essential it is to object recognition, reading and visually guided action, a solid operational definition of what crowding is has only recently become clear. The goal of this review is to provide a broad-based synthesis of the most recent findings in this area, to define what crowding is and is not, and to set the stage for future work that will extend our understanding of crowding well beyond low-level vision. Here we define six diagnostic criteria for what counts as crowding, and further describe factors that both escape and break crowding. All of these lead to the conclusion that crowding occurs at multiple stages in the visual hierarchy.

Section snippets

Dispelling the illusion

With regular flicks of the eye, we establish and maintain the illusion of a continuous high-resolution representation of our visual environment. This compelling illusion is easy to dispel by trying to describe the details of objects in your peripheral visual field; scrutinizing or trying to count objects in the visual periphery is impossible. This partly reflects the well-known decline in visual acuity in peripheral vision. However, the most widespread impediment to reading and object

Operationally defining crowding

The significance of crowding is clear from phenomenological demonstrations of its power and ubiquity in natural scenes (Figure 1). Ultimately, however, characterizing and understanding the mechanism(s) of crowding require more than a phenomenological description. Recent work has established that there are several diagnostic criteria for crowding, and use of these as converging evidence can help studies to individuate and distinguish crowding from other effects, such as masking, lateral

Crowding and appearance

As discussed above, crowded objects do not simply disappear. On the contrary, crowding changes the appearance of the crowded zone, which is important because it can help to distinguish among the main models for crowding (masking, pooling and substitution, as discussed below). As examples, Tyler and Likova noted their impression of a crowded letter as a ‘gray, or inchoate, smudge between the two outer letters, including the inner parts of those letters’ [22] (see also Figure 1, Figure 2).

What information survives crowding?

The type of information that gets through to conscious perception under conditions of crowding provides important clues about the nature of crowding.

What information breaks crowding?

Under certain circumstances, crowding might be reduced or released completely.

Where in the brain does crowding take place?

There has been a great deal more psychophysical than neurophysiological work on crowding. Above all, neurophysiological studies of crowding have proven that it is surprisingly difficult to isolate the neural mechanism(s) of crowding per se (Box 2). Nevertheless, psychophysical studies have helped to narrow down the level(s) at which crowding occurs and will help to guide the design of more stringent future neurophysiological experiments.

Although there is great diversity in the stimuli used

Models of crowding

There is no shortage of ideas about crowding, but few are computational or make specific quantitative predictions. The large number of different models can be distilled down to three basic classes: (i) masking, (ii) pooling (either of low-level features or by attention) and (iii) substitution. Within each class many different architectures and algorithms have been proposed. These extant models are largely descriptive and have been reviewed in detail elsewhere [1]. There are few quantitative

Multiple levels of crowding

Although there is great heterogeneity in results on crowding, there is sufficient evidence to cast doubt on the idea that crowding is a unitary effect due to a single stage of processing, although this is implicitly assumed in most studies on crowding 1, 2; rather, the collective work suggests that crowding happens independently at several stages of visual processing. In support of this view are observations that crowding is specific to the similarity between and the configuration of target and

The crowded future

Our discussion of crowding has been confined exclusively to visual perception, but the implications of crowding are far and wide and are likely to increase rapidly in the near future (Box 3). For example, crowding might exert a fundamental limit on visually guided actions in naturally cluttered scenes. Although the impact of crowding per se on visually guided action has rarely been investigated 14, 76, 77, several studies suggest that clutter impairs action 78, 79. More intriguingly, there is

Acknowledgements

This work was supported by grants from the National Eye Institute: R01EY01728 to D.L. and R01EY018216 and NSF CAREER 0748689 to D.W. We are grateful to Jason Fischer for helpful comments on an earlier draft of this manuscript. Both authors contributed equally to this work.

Glossary

Aftereffect: delayed or prolonged physiological or psychological response following exposure to a stimulus.
Amblyopia: developmental abnormality that results from physiological alterations in the visual cortex and impairs form vision (from the Greek, amblyos for blunt and opia for vision).
Anisotropy: difference in the extent of crowding when measured along different axes.
Eccentricity: where in the visual field, relative to the locus of fixation, a stimulus appears.
Flanker: object or feature that is close

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Visual crowding is the difficulty in identifying an object when surrounded by neighbouring flankers, representing a bottleneck for object perception. Crowding arises not only from the activity of visual areas but also from parietal areas and fronto-parietal network activity. Parietal areas would provide the dorsal-to-ventral guidance for object identification and the fronto-parietal network would modulate the attentional resolution. Several studies highlighted the relevance of beta oscillations (15–25 Hz) in these areas for visual crowding and other connatural visual phenomena. In the present study, we investigated the differential contribution of beta oscillations in the parietal cortex and fronto-parietal network in the resolution of visual crowding. During a crowding task with letter stimuli, high-definition transcranial Alternating Current Stimulation (tACS) in the beta band (18 Hz) was delivered bilaterally on parietal sites, on the right fronto-parietal network, and in a sham regime. Resting-state EEG was recorded before and after stimulation to measure tACS-induced aftereffects. The influence of crowding was reduced only when tACS was delivered bilaterally on parietal sites. In this condition, beta power was reduced after the stimulation. Furthermore, the magnitude of tACS-induced aftereffects varied as a function of individual differences in beta oscillations. Results corroborate the link between parietal beta oscillations and visual crowding, providing fundamental insights on brain rhythms underlying the dorsal-to-ventral guidance in visual perception and suggesting that beta tACS can induce plastic changes in these areas. Remarkably, these findings open new possibilities for neuromodulatory interventions for disorders characterised by abnormal crowding, such as dyslexia.
Peripheral vision is mainly for looking rather than seeing
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Vision includes looking and seeing. Looking, mainly via gaze shifts, selects a fraction of visual input information for passage through the brain’s information bottleneck. The selected input is placed within the attentional spotlight, typically in the central visual field. Seeing decodes, i.e., recognizes and discriminates, the selected inputs. Hence, peripheral vision should be mainly devoted to looking, in particular, deciding where to shift the gaze. Looking is often guided exogenously by a saliency map created by the primary visual cortex (V1), and can be effective with no seeing and limited awareness. In seeing, peripheral vision not only suffers from poor spatial resolution, but is also subject to crowding and is more vulnerable to illusions by misleading, ambiguous, and impoverished visual inputs. Central vision, mainly for seeing, enjoys the top-down feedback that aids seeing in light of the bottleneck which is hypothesized to starts from V1 to higher areas. This feedback queries for additional information from lower visual cortical areas such as V1 for ongoing recognition. Peripheral vision is deficient in this feedback according to the Central-peripheral Dichotomy (CPD) theory. The saccades engendered by peripheral vision allows looking to combine with seeing to give human observers the impression of seeing the whole scene clearly despite inattentional blindness.
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Channel pruning is an efficient technique for model compression, removing redundant parts of a convolutional neural network with minor degradation in classification accuracy. Previous criteria of channel pruning ignore neurons’ intrinsic relationship and the high correlation with input samples. Inspired by the visual crowding phenomenon in neuroscience, this paper presents a novel channel pruning method via reverse neuron crowding, dubbed CPRNC, to address this issue. First, CPRNC involves a neuron crowding degree measure (NCDM) module, which builds the relationship model among all artificial neurons by observing their crowding behaviors. Subsequently, each channel’s importance is evaluated by the crowding degree of corresponding channels. Considering that the channel importance is affected by the characteristic of input samples, CPRNC designs a neuron crowding degree recalibrate (NCDR) module. NCDR emphasizes discriminative samples to recalibrate the channel priority list generated by NCDM, further enhancing the precision of the pruning criterion. Experimental results show that CPRNC achieves performance that competes with state-of-the-art pruning methods, including dynamic channel pruning and learning-based pruning. For example, we prune ResNet-50 with 56.7% FLOPs on the large-scale dataset ImageNet1K with only a 0.19% decrease in accuracy. At low pruning rates, CPRNC achieves lossless compression, e.g., the pruned ResNet-56 on CIFAR-10 increases accuracy by 0.13% over the baseline model at 56.3% FLOPs reduction.
Does crowding predict conjunction search? An individual differences approach
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Searching for objects in the visual environment is an integral part of human behavior. Most of the information used during such visual search comes from the periphery of our vision, and understanding the basic mechanisms of search therefore requires taking into account the inherent limitations of peripheral vision. Our previous work using an individual differences approach has shown that one of the major factors limiting peripheral vision (crowding) is predictive of single feature search, as reflected in response time and eye movement measures. Here we extended this work, by testing the relationship between crowding and visual search in a conjunction-search paradigm. Given that conjunction search involves more fine-grained discrimination and more serial behavior, we predicted it would be strongly affected by crowding. We tested sixty participants with regard to their sensitivity to both orientation and color-based crowding (as measured by critical spacing) and their efficiency in searching for a color/orientation conjunction (as indicated by manual response times and eye movements). While the correlations between the different crowding tasks were high, the correlations between the different crowding measures and search performance were relatively modest, and no higher than those previously observed for single-feature search. Instead, observers showed very strong color selectivity during search. The results suggest that conjunction search behavior relies more on top-down guidance (here by color) and is therefore relatively less determined by individual differences in sensory limitations as caused by crowding.
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Trends in Cognitive Sciences

ReviewVisual crowding: a fundamental limit on conscious perception and object recognition

Section snippets

Dispelling the illusion

Operationally defining crowding

Crowding and appearance

What information survives crowding?

What information breaks crowding?

Where in the brain does crowding take place?

Models of crowding

Multiple levels of crowding

The crowded future

Acknowledgements

Glossary

Vision Res.

Vision Res.

Vision Res.

Curr. Biol.

Vision Res.

Vision Res.

Vision Res.

Curr. Biol.

Curr. Biol.

Vision Res.

Vision Res.

Vision Res.

Vision Res.

Vision Res.

Neuron

Vision Res.

Vision Res.

Curr. Biol.

Vision Res.

Curr. Opin. Neurobiol.

Vision Res.

Vision Res.

Vision Res.

J. Exp. Child Psychol.

Vision Res.

Vision Res.

Vision Res.

Vision Res.

Vision Res.

Vision Res.

Ophthalmic Physiol. Opt.

Prog. Brain Res.

The uncrowded window of object recognition

Nat. Neurosci.

Suppressive and facilitatory spatial interactions in peripheral vision: peripheral crowding is neither size invariant nor simple contrast masking

J. Vis.

Crowding is unlike ordinary masking: distinguishing feature integration from detection

J. Vis.

Interaction effects in parafoveal letter recognition

Nature

Horizontal and vertical asymmetry in visual spatial crowding effects

J. Vis.

Attentional resolution and the locus of visual awareness

Nature

Asymmetry of lateral interference in visual letter identification

Percept. Psychophys.

Crowding and surround suppression: not to be confused

J. Vis.

Holistic crowding of Mooney faces

J. Vis.

The roles of cortical image separation and size in active visual search performance

J. Vis.

Temporal properties of the polarity advantage effect in crowding

J. Vis.

On the relations between crowding and visual masking

Percept. Psychophys.

Time course of masking in spatial resolution tasks

Optom. Vis. Sci.

Precueing attention to the target location diminishes crowding and reduces the critical distance

J. Vis.

Crowding: a neuroanalytic approach

J. Vis.

Compulsory averaging of crowded orientation signals in human vision

Nat. Neurosci.

Visual clutter causes high-magnitude errors

PLoS Biol.

Review
Visual crowding: a fundamental limit on conscious perception and object recognition