Beyond Bouma's window: How to explain global aspects of crowding?
Fig 2
a. Standard view of visual processing. First, edges are detected by low-level neurons with small receptive fields. Higher level neurons pool signals from lower level neurons in a hierarchical, feedforward manner, creating higher level representations of objects by combining low-level features [25,26]. For example, two low-level edge detectors may be combined to create a “corner” representation. Four such corner detectors can be assembled to create a rectangle representation. Receptive field size naturally increases along this pathway since, for example, a rectangle covers larger parts of the visual field than the lines making up the rectangle. b. Uncrowding. Observers performed a vernier discrimination task. The y-axis shows the threshold for which observers correctly discriminate the vernier offset in 75% of trials (so performance is good when the threshold is low). First, only a vernier is presented, an easy task (performance for this condition is shown as the dashed horizontal line). Then, a flanking square is added making the task much more difficult (leftmost stimulus). This is a classic crowding effect. Importantly, adding more flanking squares improved performance gradually, i.e., performance improved the more squares are presented [19]. We call this effect uncrowding. c. The global configuration of the entire stimulus determines crowding. Performance is strongly affected by elements far away from the target as shown in these examples [15]. d. Performance is not determined by local interactions only. In this display, fine-grained vernier acuity of about 200” depends on elements as far away as 8.5 degrees—a difference of two orders of magnitude, extending far beyond Bouma’s window.