A Neurophysiologically Plausible Population Code Model for Feature Integration Explains Visual Crowding

Berg, Ronald van den; Roerdink, Jos B. T. M.; Cornelissen, Frans W.

doi:10.1371/journal.pcbi.1000646

Cited by 87 publications

(97 citation statements)

References 46 publications

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“…However, the phenomenon of decreased performance with nearby contours also occurs with less structured stimuli (cf. Greenwood, Bex, & Dakin, 2010;Levi & Carney, 2009;Livne & Sagi, 2010;Parth & Rentschler, 1984;van den Berg, Roerdink, & Cornelissen, 2007). Levi et al (1984) studied the effect with Vernier targets, in the fovea and periphery.…”

Section: The Origin Of Crowding Researchmentioning

confidence: 99%

“…In particular, a new class of models has emerged, which build upon ensemble properties of the input patterns (Balas, Nakano, & Rosenholtz, 2009;Parkes et al, 2001;van den Berg, Roerdink, & Cornelissen, 2010). Compared to that of Parkes et al, the model by Balas et al (2009) encompasses a much wider range of input patterns-including letter stimuli-and is of particular interest here.…”

Section: Contrast Processing and Erroneous Feature Combinationsmentioning

confidence: 99%

“…van den Berg, Cornelissen, and Roerdink (2009) presented a crowding model that also predicts clutter. It further accounts for the observation of chromaticity information contributing to crowding (van den Berg et al, 2007;Chapter 8.4.1: Feedforward models of crowding).…”

Section: Computational Models Of Crowdingmentioning

confidence: 99%

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Peripheral vision and pattern recognition: A review

2011

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We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding.

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Section: The Origin Of Crowding Researchmentioning

confidence: 99%

Section: Contrast Processing and Erroneous Feature Combinationsmentioning

confidence: 99%

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Peripheral vision and pattern recognition: A review

2011

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“…Another option is that pooling occurs only amongst neurons that code for the same or similar features in accordance with the notion that crowding is weaker when target and flankers are dissimilar (Kooi et al, 1994;Saarela et al, 2009;Saarela et al, 2010;Sayim et al, 2008Sayim et al, , 2010. Such pooling models can explain why short and long single flankers yield better performance than single, equallength flankers (Parkes et al, 2001;van den Berg, Roerdink, & Cornelissen, 2010). However, such models fail to explain why performance improves when the number of long and short flankers increases.…”

Section: Poolingmentioning

confidence: 99%

Grouping, pooling, and when bigger is better in visual crowding

2012

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In crowding, perception of a target is strongly deteriorated by nearby elements. Crowding is often explained by pooling models predicting that adding flankers increases crowding. In contrast, the centroid hypothesis proposes that adding flankers decreases crowding--"bigger is better." In foveal vision, we have recently shown that adding flankers can increase or decrease crowding depending on whether the target groups or ungroups from the flankers. We have further shown how configural effects, such as good and global Gestalt, determine crowding. Foveal and peripheral crowding do not always reveal the same characteristics. Here, we show that the very same grouping and Gestalt results of foveal vision are also found in the periphery. These results can neither be explained by simple pooling nor by centroid models. We discuss when bigger is better and how grouping might shape crowding.

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“…The current main theories explain crowding either in terms of excessive feature pooling (e.g., Pelli & Tillman, 2008;van den Berg, Roerdink, & Cornelissen, 2010) or as due to a loss of positional information (source confusion) resulting in reporting a flanking object as the target (e.g., Dakin, Cass, Greenwood, & Bex, 2010;Greenwood, Bex, & Dakin, 2009;Strasburger, Harvey, & Rentschler, 1991;Strasburger & Malania, 2013). A recent model integrates both of these accounts by assuming that uncertainty (i.e., the width of the internal noise distribution) about both stimulus positions and identities depends on flanker proximity (van den Berg, Johnson, Martinez Anton, Schepers, & Cornelissen, 2012).…”

Section: Introductionmentioning

confidence: 99%

Eyes on crowding: Crowding is preserved when responding by eye and similarly affects identity and position accuracy

Yildirim¹,

Meyer²,

Cornelissen³

2015

Journal of Vision

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Peripheral vision guides recognition and selection of targets for eye movements. Crowding—a decline in recognition performance that occurs when a potential target is surrounded by other, similar, objects—influences peripheral object recognition. A recent model study suggests that crowding may be due to increased uncertainty about both the identity and the location of peripheral target objects, but very few studies have assessed these properties in tandem. Eye tracking can integrally provide information on both the perceived identity and the position of a target and therefore could become an important approach in crowding studies. However, recent reports suggest that around the moment of saccade preparation crowding may be significantly modified. If these effects were to generalize to regular crowding tasks, it would complicate the interpretation of results obtained with eye tracking and the comparison to results obtained using manual responses. For this reason, we first assessed whether the manner by which participants responded—manually or by eye—affected their performance. We found that neither recognition performance nor response time was affected by the response type. Hence, we conclude that crowding magnitude was preserved when observers responded by eye. In our main experiment, observers made eye movements to the location of a tilted Gabor target while we varied flanker tilt to manipulate target-flanker similarity. The results indicate that this similarly affected the accuracy of peripheral recognition and saccadic target localization. Our results inform about the importance of both location and identity uncertainty in crowding.

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A Neurophysiologically Plausible Population Code Model for Feature Integration Explains Visual Crowding

Cited by 87 publications

References 46 publications

Peripheral vision and pattern recognition: A review

Peripheral vision and pattern recognition: A review

Grouping, pooling, and when bigger is better in visual crowding

Eyes on crowding: Crowding is preserved when responding by eye and similarly affects identity and position accuracy

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