Surface-Related Features Figure 15. Hysteresis effect induced by feedback. The figure illustrates T- and X-junction activities extracted from the model based on temporal input sequences of two occluding squares where the topmost square changes material appearance from opaque (100% opacity) to transparent (90% opacity) (blue lines) over time. Furthermore the sequence was presented in reverse temporal order leading to a change of material appearance back from transparent to opaque (red lines). Activities were extracted at positions indicated by the red dot on the stimulus. Illustrated are results based on model simulations with feedback (top row) and without feedback connections (bottom row). Gray shaded areas indicate periods where stimulus properties linearly change. The results demonstrate that feedback leads to a hysteresis effect by temporary locking the prediction for a junction type (T- or X-junction). Without feedback the hysteresis effect disappears and both input sequences produce exactly the same results (only red curve is visible). Furthermore, the results demonstrate that feedback helps to disambiguate initial junction signals by amplifying the most likely prediction and suppressing weak predictions over the first few iterations. doi:10.1371/journal.pone.0005909.g015 One of the first computational models in the context of contour grouping that incorporate principles of long-range interactions and interlaminar recurrent processing has been proposed by Grossberg and colleagues [17] by introducing the Boundary Contour System (BCS). A more recent version of the BCS focuses more on the intercortical processing between areas V1 and V2 [50]. Grossberg and coworkers propose that V2 is mainly a slightly modified version of V1 operating at a coarser scale. Thus, they suggest that both areas, V1 and V2 share the same functional properties. Unlike them, we argue that V1 and V2 have different functional roles, e.g., corner selective cells occur in V1, bipole cells responding to illusory contours occur in V2. A more fundamental difference in the model of Grossberg and colleagues is that they use additive feedback connections with the effect that new activity can be generated in model area V1 at positions where initial bottom-up signals are zero. To compensate for this, they have to incorporate thresholds which lead to more complex balancing processes. However, we use modulatory feedback connections, which implies that initial bottom-up activity is required to generate activity. Thus, in our model, illusory contours characterized by zero luminance contrast can only be signaled in V2, but not in V1. This is consistent with electrophysical studies of Peterhans and von der Heydt [51] who concluded that illusory contour cells are virtually absent in V1. Concurrently, there is strong evidence for illusory contour selective cells in V2 [9]. A recurrent model of V1- V2 interactions based on modulatory feedback was first proposed by Neumann and Sepp [33]. Mundhenk and Itti [52] presented a multi-scale model for contour integration that is motivated by mechanisms in early visual cortex (V1). Similar to our model, the authors try to extract saliency values from contour representations. Individual contour saliency maps from different scales are combined by weighted PLoS ONE | www.plosone.org 17 June 2009 | Volume 4 | Issue 6 | e5909