How best to unify crowding?

Abstract

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.

Visual crowding is a combination of an increase of positional uncertainty, source confusion, and featural averaging

“…Such a process is distinct from any single phenomenon such as source confusion, averaging or substitution but instead results from the broad spatial bandwidth of early stage filters. It is important to note that this model does not predict instances in which the magnitude of crowding is greatly modulated by certain configurations of flankers3361. We are not convinced, however, that these limitations argue against a population code; they instead require reconsideration of the way in which the features are weighted within the population code.…”

“…However, there are also several studies demonstrating that a metric of target-distractor distance alone fails to predict target visibility. For example, the extent of crowding varies according to: colour, shape, or polarity differences between target and flankers31; the duration of the crowded display28; and perceptual grouping of the flankers3233. To the best of our knowledge, no model has been advanced to attempt to account for all of these effects.…”

Can (should) theories of crowding be unified?

“…In a recent critique, Pachai et al (2016) demonstrated that crowding is reduced when the target C is surrounded by multiple flankers with the same orientation rather than just one, and the HB model fails to account for this reduction in crowding. In their reply, Harrison and Bex (2016) claimed that Pachai et al (2016) overlooked the critical aspect of their model, the weighting field. Moreover, the authors proposed that the apparent reduction of crowding with multiple surrounding flankers (having the same orientation) can be accounted for by a simple change in the front end of their model.…”

“…Moreover, the authors proposed that the apparent reduction of crowding with multiple surrounding flankers (having the same orientation) can be accounted for by a simple change in the front end of their model. Instead of using a bank of orientation-tuned filters in the first stage, Harrison and Bex (2016) used a filter-rectify-filter model of early visual texture processing (Bergen & Landy, 1991) and claimed that this updated version of their model can account for the data put forth by Pachai and colleagues (2016). Although Harrison and Bex (2016) did not perform any quantitative comparisons, here our aim was not to determine what type of front end would be best in a crowding model.…”

“…In short, their model consists of three stages: (a) a filtering stage, (b) a weighted-averaging stage, and (c) a decision-making stage. The essential property of the HB model, as the authors emphasized more recently (Harrison & Bex, 2016; Pachai, Doerig, & Herzog, 2016), is the weighting field centered on the target object. The contrast energy obtained from the first stage is weighted such that the contribution of a feature (regardless of its ownership) to the final percept decreases with its distance from the center of the target object.…”