Shrinking Bouma’s window: How to model crowding in dense displays

Abstract

In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma’s law, i.e., all elements within a certain distance interfere with the target, and that adding more elements always leads to stronger crowding. Crowding is predominantly studied using sparse displays (a target surrounded by a few flankers). However, many studies have shown that this approach leads to wrong conclusions about human vision. Van der Burg and colleagues proposed a paradigm to measure crowding in dense displays using genetic algorithms. Displays were selected and combined over several generations to maximize human performance. In contrast to Bouma’s law, only the target’s nearest neighbours affected performance. Here, we tested various models to explain these results. We used the same genetic algorithm, but instead of selecting displays based on human performance we selected displays based on the model’s outputs. We found that all models based on the traditional feedforward pooling framework of vision were unable to reproduce human behaviour. In contrast, all models involving a dedicated grouping stage explained the results successfully. We show how traditional models can be improved by adding a grouping stage.

Broad attention uncovers benefits of stimulus uniformity in visual crowding

“…Limitations to the generality of Bouma’s rule were suggested by, for instance, its dependence on the density of the display 25 , 50 . While Bouma’s rule applied in sparse displays, crowding in densely cluttered displays seemed to depend only on the target’s ‘nearest neighbors’, i.e., those flankers that were within a radius far smaller than Bouma’s range.…”

The Irreducibility of Vision: Gestalt, Crowding and the Fundamentals of Vision

“…Hence, any theory of vision has to deal with crowding [36,37]. However, no model within the physiological framework even comes close to handling the complex effects presented here, including deep networks, as computer simulations have shown [9,17,[38][39][40][41][42]. Only models that took grouping and segmentation explicitly into account and make heavy use of time-consuming recurrent processing delivered good results [9,41,42].…”