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

Although we perceive a richly detailed visual world, our ability to identify individual objects is severely limited in clutter, particularly in peripheral vision. Models of such “crowding” have generally been driven by the phenomenological misidentifications of crowded targets: using stimuli that do not easily combine to form a unique symbol (e.g. letters or objects), observers typically confuse the source of objects and report either the target or a distractor, but when continuous features are used (e.g. orientated gratings or line positions) observers report a feature somewhere between the target and distractor. To reconcile these accounts, we develop a hybrid method of adjustment that allows detailed analysis of these multiple error categories. Observers reported the orientation of a target, under several distractor conditions, by adjusting an identical foveal target. We apply new modelling to quantify whether perceptual reports show evidence of positional uncertainty, source confusion, and featural averaging on a trial-by-trial basis. Our results show that observers make a large proportion of source-confusion errors. However, our study also reveals the distribution of perceptual reports that underlie performance in this crowding task more generally: aggregate errors cannot be neatly labelled because they are heterogeneous and their structure depends on target-distractor distance.