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

Ronald van den Berg,Jos B. T. M. Roerdink,Frans W. Cornelissen,Konrad P. Kording

doi:10.1371/journal.pcbi.1000646

Ronald van den Berg, Jos B. T. M. Roerdink + Show 2 more

Open Access

https://doi.org/10.1371/journal.pcbi.1000646

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Abstract

An object in the peripheral visual field is more difficult to recognize when surrounded by other objects. This phenomenon is called “crowding”. Crowding places a fundamental constraint on human vision that limits performance on numerous tasks. It has been suggested that crowding results from spatial feature integration necessary for object recognition. However, in the absence of convincing models, this theory has remained controversial. Here, we present a quantitative and physiologically plausible model for spatial integration of orientation signals, based on the principles of population coding. Using simulations, we demonstrate that this model coherently accounts for fundamental properties of crowding, including critical spacing, “compulsory averaging”, and a foveal-peripheral anisotropy. Moreover, we show that the model predicts increased responses to correlated visual stimuli. Altogether, these results suggest that crowding has little immediate bearing on object recognition but is a by-product of a general, elementary integration mechanism in early vision aimed at improving signal quality.

Highlights

Since Korte [1] originally described perceptual phenomena of reading in peripheral vision, a substantial number of studies have shown the important role of spacing for object recognition
Motivated by findings from physiological [25,26] and theoretical [27] studies, we model feature integration as a summation of population codes. We demonstrate that this approach allows to explain several fundamental crowding properties in a single, unified model, including aspects of critical spacing [6,15], compulsory averaging of crowded orientation signals [10], and an asymmetry between the effects of foveally and peripherally placed flankers [28,29]
There has been an explosion of studies on crowding, driven, in part, by the belief that understanding crowding will help to understand a range of visual behaviours, including object recognition, visual search, reading, and texture recognition

Summary

Introduction

Since Korte [1] originally described perceptual phenomena of reading in peripheral vision, a substantial number of studies have shown the important role of spacing for object recognition. The strength of the crowding effect depends on the spacing between objects (Figure 1). The largest spacing at which there is a measurable effect is commonly referred to as the ‘critical spacing’. An important and often replicated finding is that the critical spacing for object recognition is proportional to the viewing eccentricity [5]. Critical spacing is found to be highly invariant to a great variety of stimulus manipulations, such as contrast and size [6,7,8]. Critical spacing is the most extensively studied crowding property and, because of its robustness, sometimes considered the defining property of crowding [3]

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