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Examples[ edit ] The most famous example of figure—ground perception is probably the faces—vase drawing that Danish psychologist Edgar Rubin described.
If the two curvy edges between the black and white regions are assigned inward then the central white region is seen as a vase shape in front of a black background. No faces are perceived in this case. On the other hand, if the edges are assigned outwards, then the two black profile faces are perceived on a white background and no vase shape is perceived. The human visual system will then settle on either of the interpretations of the Rubin vase and alternate between them.
Functional brain imaging shows that when people see the Rubin image as a face, there is activity in the temporal lobe, specifically in the face-selective region   Perceptual process[ edit ] How does the brain decide in a visual scene which item is the figure and which are part of the ground? This perceptual decision can be based on many cues, all of which are of a probabilistic nature. For instance, size helps us distinguish between the figure and the ground, since smaller regions are often but not always figures.
Object shape can help us distinguish figure from ground, because figures tend to be convex. Movement also helps; the figure may be moving against a static environment. This feedback is carried out by surface contour signals that are generated by a contrast-sensitive on-center off-surround network whose inputs are the filled-in surface activities within each FIDO.
The inhibitory connections of this network's off-surround act across position and within depth to generate contrast-sensitive surface contour output signals from each FIDO.
Dissociation of Color and Figure-Ground Effects in the Watercolor Illusion
Surface contour signals are hereby generated at positions where sufficiently large changes in brightness or color occur within successfully filled-in surface regions. If the object surface in a FIDO is surrounded by a closed boundary, then there is typically a discontinuity in the contrasts across the object boundary, so surface contours can be generated at these positions.
Surface contour signals are not generated at boundary positions near a big boundary gap, since brightnesses and colors can then be equal, hence have zero contrast, on both sides of the boundary due to the spread of filling-in across the gap.
How closed boundaries regulate seeing and recognition in depth. A closed boundary can form at the nearer depth Depth 1 by combining a binocular vertical boundary at the left side of the square with three monocular boundaries that are projected along the line of sight to all depths.
The Depth 1 surface contours excite, and thereby strengthen, the boundaries at Depth 1 that controlled filling-in at Depth 1. These surface contours also inhibit the redundant boundaries at Depth 2 at the same positions. As a result, the pruned boundaries across all depths, after the surface contour feedback acts, can project to object recognition networks in inferotemporal cortex to facilitate amodal recognition, without being contaminated by spurious boundaries.
See Fang and Grossberg for simulations of how this process works in response to random dot stereograms. Surface contour output signals generate feedback signals to the boundary representations that induced them.
These feedback signals are delivered to the boundary representations by another on-center off-surround network. The inhibitory surface-to-boundary connections of this network act within position and across depth Figure 8. The on-center signals strengthen the boundaries that generated the successfully filled-in surfaces. The off-surround signals inhibit spurious boundaries at the same positions but farther depths by a process that is called boundary pruning.
Surface contour signals hereby achieve complementary consistency by strengthening consistent boundaries and pruning inconsistent boundaries. The inhibited inconsistent boundaries can then contribute to neither seeing nor recognition in the final percept, thereby preventing the perception of spurious percepts of transparency and recognition of irrelevant contour fragments.
Because surface contour signals are generated by the contrast of a filled-in surface, they are sensitive to a particular contrast, and not to the opposite one.
The Laws of Figure/Ground, Prägnanz, Closure, and Common Fate - Gestalt Principles (3)
Their feedback to boundaries thus makes the boundary cells also sensitive to this contrast, even though the boundaries, in the absence of surface contour feedback signals, pool opposite contrast polarities, starting at V1 complex cells, so that they can complete boundaries of objects in front of textured backgrounds Grossberg, In the surface contour off-surround networks, inhibitory strength from a surface contour decreases with the distance from the source cell.
Thus, the strength of the inhibitory signals decreases as the depth difference increases between the depth of the surface that generates the surface contour signals and the recipient boundaries. In particular, the brightness of a Kanizsa square increases with the amplitude of the filled-in activity within the square.
A larger activity creates larger surface contour signals at each position. These signals are multiplied by the strengths of the inhibitory connections from the signal source to the recipient boundary at the same position but different depths. Due to the decrease in size of the inhibitory connections across depth, these net signals also get smaller as the depth difference increases.
The top image in Figure 9 represents the total strength of these inhibitory signals across depth at a lower level of brightness, and the bottom image represents the total inhibitory signals across depth at a higher level of brightness.
The two dark horizontal edges depict the x axis that calibrates the depth difference between boundaries for each brightness level, with the upper horizontal edge corresponding to the lower brightness level and the lower horizontal edge corresponding to the higher brightness level.
The numbers 1 and 2 indicate that the same level of inhibition is achieved at a larger depth difference in response to a brighter Kanizsa square. A larger number of boundary depths are hereby inhibited by a brighter square than a dimmer one. As a result, the boundary depths that survive well enough to represent the background are more separated in depth from the brighter square than those that survive in response to a dimmer square.
In short, brighter Kanizsa squares look closer, relative to their backgrounds, than dimmer ones. A cross-section of the inhibitory off-surround across depth that is caused by surface contour outputs.
The top row shows the inhibitory signals in response to a less bright Kanizsa square. The bottom row shows the inhibitory signals in response to a more bright Kanizsa square. The numerals 1 and 2 indicate one of the depths where the inhibitory signals are equal. This illustrates how the brighter Kanizsa square at depth 1 can inhibit boundaries at more depths between that of the Kanizsa square and its inducers such as depth 2thereby making the brighter square stand out more in depth.
In fact, surface contour signals help to explain many data about visual perception and object recognition.
By eliminating boundaries at depths that do not support visible filled-in surfaces, boundary pruning helps to achieve the process of surface capture whereby feature contours can selectively fill-in visible surface qualia at depths where binocular fusion of object boundaries can successfully occur, and can thereby create closed boundaries that can contain the filling-in process.
Surface contour and boundary pruning signals hereby work together to generate 3D percepts based on successfully filled-in surface regions. Importantly, by eliminating spurious boundaries, surface contour signals also initiate figure-ground separation. They do so by enabling occluding and partially occluded surfaces to be separated onto different depth planes, and inhibiting spurious boundaries at the further depths. This process accomplishes two things: See Fang and GrossbergGrossbergand Kelly and Grossberg for further details and simulated figure-ground percepts.
See Bakin et al. Monocular Boundaries and T-Junctions There are several reasons why a boundary may not be closed, so that the brightnesses and colors within them may flow out of the gap in the boundary. Two of them will be summarized here. One reason concerns how monocular boundaries help to form depthful percepts in response to a 3D scene. Another follows from how T-junctions provide a cue to relative depth order for objects in 2D pictures or at far distances in 3D scenes.
For distant objects for which binocular disparity is not a useful depth cue, monocular cues, such as T-junctions, may be used to determine relative depth when one object is nearer than another object, and occludes parts of the farther object Howard and Rogers, Some boundaries in a 3D scene may be perceived monocularly during da Vinci stereopsis Nakayama and Shimojo, ; Gillam et al. Monocular boundaries do not have a definite depth associated with them. How, then, does the brain decide to which depth they should be assigned?
A proposed approach to this Monocular-Binocular Interface Problem was suggested Grossberg,in order to explain data about 3D figure-ground perception. The same hypothesis was shown by Grossberg and Howe to help explain many data about 3D surface perception. This hypothesis proposes that the outputs of monocular boundary cells are added to binocular boundary representations at all depth planes in the interstripes of cortical area V2 along their respective lines-of-sight, possibly in layer 4.
Yazdanbakhsh and Watanabe have done psychophysical experiments to test this hypothesis with positive results. Figure 8 illustrates this hypothesis in response to the image of a square whose right vertical boundary is seen only monocularly due to da Vinci stereopsis. The three-sided boundary at Depth 2 in Figure 8 arises because the vertical boundary is monocular due to da Vinci stereopsis, and the two horizontal boundaries do not generate strong depth information because they do not strongly activate cells that are sensitive to a definite range of binocular disparities.
At Depth 1, this three-sided boundary is closed by a fourth vertical boundary that is binocularly viewed in the square, and thereby generates a preferred binocular disparity at Depth 1. The closed boundary at Depth 1 can contain surface filling-in, whereas the open boundary at Depth 2 cannot.
Figure 8 shows how surface contour feedback from the filled-in closed boundary at Depth 1 inhibits redundant boundaries at its positions and further depths, including the open three-sided boundary at Depth 2.
Due to this near-to-far inhibition, the depth-ambiguous three-sided boundary is assigned Depth 1 and a definite border ownership assignment is also made to the closed boundary at this depth. The same surface contour mechanism helps to explain how monocular cues, such as T-junctions, may be used to determine relative depth when one object is nearer than another object, and occludes parts of the further object. This explanation also clarifies how a 3D percept of occluding and occluded objects may be generated in response to a 2D picture that includes such T-junctions.
For example, consider the lower left pictorial display in Figure 4. This figure is composed of three abutting rectangles, but it irresistibly generates a 3D percept of a horizontal rectangle that partially occludes a vertical rectangle lying behind it. Here, the horizontal boundaries between the occluding rectangle and its abutting two rectangles are shared.
Due to properties of boundary grouping and completion by bipole cells e. This happens because horizontally-oriented bipole cells receive inputs from horizontal lines on both sides of each intervening vertical line, whereas the vertically-oriented boundary near their intersection receives inputs from only one vertical line. The horizontally-oriented bipole cells can therefore inhibit the vertically-oriented bipole cells where the horizontal and vertical lines are joined more than conversely, thereby creating small gaps in the vertical boundaries near where they abut the horizontal boundaries Figure 10A.
T-junctions and end gaps in figure-ground perception. An end gap in the vertical boundary arises because, for cells near where the horizontal top and vertical stem of the T come together, the top of the T activates bipole cells along the top of the T much more than bipole cells are activated along the T stem. As a result the stem boundary gets inhibited by the short-range inhibitory signals from the horizontal bipole cells, whereas the top boundary does not receive comparable inhibition from the vertical bipole cells Reprinted with permission from Grossberg, This 2D picture can be perceived as either of two 3D parallelopipeds whose shapes flip bistably through time.
C When attention switches from one circle to another, that circle pops forward as a figure and its brightness changes. See text and Grossberg and Yazdanbakhsh for an explanation. Reprinted with permission from Tse These end gaps allow brightness and color to flow between the vertical bars and their surrounds during surface filling-in, thereby equalizing the contrasts on both sides of the remaining boundaries near these gaps. Only the boundary of the horizontal rectangle is closed, so only it can contain its surface filling-in, and generate surface contour feedback.
All redundant copies of this horizontal rectangular boundary will be inhibited at further depths by near-to-far surface contour inhibition. The boundaries of the two abutting rectangles are spared by this inhibition.
As a result, border ownership of the horizontal boundaries by the near occluding rectangle is achieved, and the two rectangles above and below the occluder can complete collinear vertical boundaries and fill-in between them at a further depth, thereby giving rise to a completed vertical rectangle behind the occluding horizontal rectangle. This completed rectangle is used to recognize the two rectangles above and below the occluder as a partially occluded vertical rectangle.
Computer simulations of 3D percepts generated by 2D pictures with T-junctions are given in Kelly and Grossberg Additional mechanisms are needed to generate the modal, or consciously visible, percepts of the unoccluded parts of both occluding and occluded objects in depth.
FACADE theory proposes how boundaries and surfaces may be amodally completed in V2 for purposes of recognition, but that conscious perception of the unoccluded surfaces of opaque objects may be completed in V4.
These proposed V2 and V4 representations enable the brain to complete the representations of partially occluded objects behind their occluders without forcing all occluders to appear transparent. See Grossbergand Grossberg and Yazdanbakhsh for further details about how these V2-to-V4 interactions are proposed to work, including computer simulations of opaque and transparent percepts.
The present article focuses on properties of V2. Absolute disparity is the horizontal difference in the retinal positions of an image feature that is registered in the left and right foveas after fixation.
In contrast, many cells in cortical area V2 are sensitive to relative disparity Thomas et al. Relative disparity is the difference in absolute disparity of two visible features in the visual field Cumming and Parker, ; Cumming and DeAngelis,notably of a figure and its background. Absolute disparity varies with distance of an object from an observer.
It can change across a visual scene without affecting relative disparity. Indeed, relative disparity, unlike absolute disparity, can be unchanged by the distance of visual stimuli from an observer, or by vergence eye movements that occur as the observer inspects objects at different depths Miles, ; Yang, Thus, relative disparity is a more invariant measure of an object's depth and its 3-D shape than is absolute disparity.
The model demonstrates that shunting lateral inhibition of layer 4 cells in cortical area V2 can cause a peak shift in cell responses Figure This peak shift is sufficient to transform absolute disparity into relative disparity Figures 1213thereby creating cells that are sensitive to one or the other side of a figure against its background.
Model circuit for transforming absolute disparity into relative disparity: Reprinted with permission from Grossberg et al. Relative disparity data and simulations. Left panel Sample cell data from experiments and model: A Experimental data of two V2 cell responses for relative disparity Reprinted with permission from Thomas et al. B Two model V2 layer 4 neurons with disparity tuning curves with changes in surround disparity. The model neurons simulate the position of data peaks and their shifts, but not all aspects of the amplitudes in the data.
This is due to the simplicity of the model. Despite the simplicity, the model is capable of capturing the key shift properties. Right panel Shift ratio statistics. The shift ratio is defined as the shift in peaks of the tuning curve relative to the difference, or shift, of surround disparities.
The shift ratio summarizes the statistics of the type of disparity observed: C Shift ratio summary reprinted with permission from Thomas et al. An exhaustive number of combinations would have required permutations derived from choosing two surrounds without repetition from a set of cells, leading to 19, permutations. However, the best available data from Thomas et al. This random selection chose, for each cell, four shift ratios to derive a total of shifts and shift ratios. These shift ratios were, in turn, randomly sampled without replacement to select 75 and 91 shifts, respectively, to match the number of shifts computed in the experimental data [Reprinted with permission from Grossberg et al.
Shifts toward absolute disparity or relative disparity depend on these parameters. Shift toward absolute disparity.