How best to unify crowding?

In crowding, the perception of an object deteriorates in the presence of nearby elements. Obviously, crowding is a ubiquitous phenomenon, as elements are rarely seen in isolation. One of the main characteristics of crowding is that the elements themselves are not rendered invisible, but their features are averaged[1] or substituted[2] with those of neighboring elements. Recently, Harrison and Bex [3] presented "A Unifying Model of Orientation Crowding in Peripheral Vision", which elegantly explains these two characteristics of crowding with one unifying mechanism. They tested their model using a new crowding paradigm and demonstrated an excellent match between human and model results. A key prediction of their model is that a higher number of flankers leads to stronger crowding, simply because more non-target features contribute to the model's output and thus deteriorate performance. However, several recent studies have shown that increasing the number of flankers can actually improve performance [4-9]. Using the same experimental design as Harrison and Bex [3], we report here that adding more flankers can also improve performance in their paradigm, whereas their model predicts the opposite result. We propose that a truly unified model of crowding must include a grouping stage.