Model predictions for psychophysical experiments. Our model incorporates recurrent feedback processing from higher to lower stages. This leads to temporal model dynamics depending on bottom-up feedforward signal and top-down feedback signal. Without a change of the input signal (e.g., static input) model activities tend to converge after a few iterations. However, when the input signal temporally changes this leads to a conflict between bottom-up and top-down signals. Thus, the system acts like a short-term memory maintaining the actual state for a few time steps. Consistently, if the material appearance changes from opaque to transparent over time one would expect that the perception of the apparent stimulus is more prolonged in time. From this it follows that our model simulations predict a perceptual hysteresis effect for discrimination between opaque- transparent and transparent-opaque transitions induced by a top- down feedback mechanism. Such a hysteresis effects has been already observed psychophysically for motion direction disambiguation (leftward motion vs. rightward motion) [67]. Since both motion and form processing are based on the same neural principles we expect that the predicted hysteresis effect can also be measured psychophysically. Therefore, we are currently planning to investigate experimentally whether our model predictions are validated or not. The role of junctions in visual perception Our core model integrates like-oriented contrasts to simulate the process of contour perception in the visual system. When contours of different orientation meet at the same place non-collinear orientation combinations, namely junctions, are formed. The formation of junctions provides important cues, e.g., for the occurrence of occlusions and transparencies. Occlusions occur in almost every real word scene, and thus, surface completion is a fundamental visual process. In the following, we discuss the individual role of some basic junction types that can be extracted by our model. Transparency. It has been suggested that the perception of transparency is triggered by X-junctions formed by junctions of contours of the transparent and opaque regions at the overlapping area [68]. However, the presence of X-junctions is necessary but not sufficient to elicit a strong transparency effect. In addition, the luminance contrast around the X-junction must follow the two rules: (1) the direction of luminance contrast across an opaque border cannot change in the transparent region; (2) the luminance difference across an opaque border must be reduced in the transparent region [69,70]. A violation of these rules strongly diminishes the perception of transparency. Wolfe and collaborators [71] explored in a series of visual search experiments which cues are relevant to guide attention in a search for opaque targets among transparent distracters or vice versa. One of the experiments showed that performance is impaired when X-junctions are removed from transparent items. Another experiment showed that efficient search is still possible if X- junctions are merely occluded (i.e. an occluding bar is used that disrupts the X configurations). In summary, these findings show that indeed X-junctions play an important role in the perception of transparencies, but there seem to be many other factors that play an additional role for transparency perception. Nevertheless, our proposed architecture facilitates the perceptual interpretation of X-junction as proposed by Wolfe and colleagues. Occlusion. When an opaque surface occludes another surface of different luminance a T-junction is formed at the position where the boundary contours intersect each other. If the surface in front has the same luminance than the background the T-junctions collapses to an L-junction. We have shown that our Surface-Related Features model initially detects such configurations as L-junctions. After a short period of time, when contours are completed over gaps, such L-junctions are recognized by the model as T-junctions.This is consistent with the more context driven interpretation, as observed by Rubin [1]. Rubin investigated in psychophysical experiments how local occlusion cues, such as T-junctions and more global occlusion cues, specifically relatability and surface similarity, play a role in the emergence of amodal surface completion and illusory contour perception. Two contour fragments are relatable when they can be connected with a smooth contour without inflection points [71]. Rubin proposes that local T-junction cues can initiate completion processes and that relatability plays a part at later stages. Interestingly, in rare cases, T-junctions can also support the perception of X-junctions [72]. In their psychophysical experi- ments, the T-junctions were perceived as having an additional illusory contour leading to the perception of an X-junction (termed ‘‘implicit X-junction’’). This special case shows that T-junctions do not always lead to the perception of occluding opaque surfaces but can itself be altered in the more global context when prototypical surface patches are formed which may lead to a reinterpretation of local features. Figure-ground segregation. Separating figure from background is one of the most important tasks in vision. Figure and ground information in an image can be represented by assigning ownership of the border between two surfaces. The figure which occludes parts of the background leads to specific boundary configurations, in particular T-junction configurations which can help in the assignment of figure and background. In more detail, the stem of the ‘‘T’’ is formed by the boundary contour of the background surface while the top of the ‘‘T’’ corresponds to the boundary contour of the figure. Motivated by physiological evidence for cells that are selective for border- ownership information [37] some models were proposed where cues signaled by T-junctions are used to generate consistent representations of layered surfaces [30,38]. This underlines the importance of T-junction cues for the visual system. Motion perception. Junctions do not only play a role in static scenes, they are also important in the context of motion perception. An object’s motion cannot be determined from a single local measurement on its contour which is commonly known as the aperture problem [73]. However, at positions where multiple oriented contrasts (i.e. two-dimensional features, such as corners and junctions) are present the ambiguity can be resolved and further be propagated along object contours to get a more global motion percept [74]. Thus, tracking of two-dimensional features over time is a fundamental task in the analysis of motion signals. In a study by Pack et al. [75] it is suggested that end-stopped V1 neurons could provide local measures of two-dimensional feature correspondences in motion by responding preferentially to moving line endings. However, the results of Gue et al. [76] contrast with the suggestion that end-stop neurons can determine global motion directions. They propose that lateral and feedback connections play a critical role in V1 motion information integration. But still, it remains unclear whether cortical neurons represent object motion by selectively responding to two-dimensional features such as junctions and corners. On the other hand, motion of specific junction configurations, in particular T- and X-junctions generates erroneous motion trajectories. As shown in Figure 16, if edge motion from two bars moving in opposite horizontal directions is combined, the resulting intersection of constraints is in an incorrect vertical direction [77]. Thus, static form cues such as detected T-junctions could be selectively discounted in the process of motion interpretation [78]. PLoS ONE | www.plosone.org 19 June 2009 | Volume 4 | Issue 6 | e5909