Ординатура / Офтальмология / Английские материалы / Binocular Rivalry_Alais, Blake_2005

what percept the network is “seeing” means that a solution is assured for all iterations in terms of one of ten categorical cells. The second input feature is that of habituation. While this factor may not have been necessary to achieve rivalry in some cases, its presence can be argued as logical on the basis of single-cell electrophysiological studies where habituation is a common observation of recordings from cortex.

The issue of bottom-up or top-down theories of rivalry has not been directly addressed by this neural network attempt. An inspection of figure 18.1 shows that there is segregation of the visual information through the first couple of stages and that interaction between the two eyes really occurs only at the penultimate stage, just before the Kohonen classifier. In the current model it is the activity in the early extrastriate cortex that causes rivalry for dominance at the late extrastriate level. The result of the late extrastriate decision on the winner is fed back to the activity levels at the early extrastriate layer. Thus the current model embraces aspects of both theories. Also, the model does not really mimic the human visual system, where binocular interaction occurs at the level of striate cortex and where pipelining of information to “categorical cortex” in the inferotemporal regions occurs via several more synaptic steps utilizing predominantly binocular neurons.

The model does not give a prediction regarding cortical localization or hemispheric switching (Miller et al., 2000), largely because there is only one higher cortical region in the model. However, it is clear from the way in which attention was (somewhat artificially) implemented that the model would respond to neural firing changes accompanying such a hemispheric stimulation. Also, it is clear that rivalry does not require the presence of two hemispheres to compete, because the properties of the categorical cells are sufficient.

The one feature of this model that was not an input was not engineered, and yet gave plausibility to the top-down approach was the emergence of a principle of prediction of rivalry which was validated with high reliability. Consider the system from the bottom up and from the top down. A priori there is nothing from the bottom-up direction that allows a prediction of rivalry in particular choices of stimuli presented to the two eyes. While one might guess that stimuli with like contours are less likely to rival, it is only when the system has actually learned and categorized the set of stimulus images into one of ten categorical cells that one can predict with almost complete certainty which pairs of stimuli will not rival. The human visual system also shows a preference for images that it has learned. Yu and Blake (1992) showed that in rivalry, a face stimulus

predominated more than a pattern stimulus equated for spatial frequency, luminance, and contrast.

The Kohonen classifier is interesting in that neighboring classifiers are closer in terms of similarity than are more distant ones. This was seen in some of the rivalry cases where the expected categorical cells did not participate, but a neighboring categorical cell was involved. Also, rivalry comparison at high and low contrast showed, for example, that a stimulus may have been classified as GC#3 at low contrast but GC#4 at high contrast.

In principle, the model could be extended to provide an account of other bistable phenomena, where the low-level part of the network receives invariant input. Practically, this could be achieved through the training of images with binocular disparity (e.g., the two alternate 3D views of the Necker cube) and then testing with 2D (zero disparity) stimuli presented to the two eyes.

In conclusion, the results of this and other computational studies of binocular rivalry indicate that both bottom-up and top-down approaches yield rivalry behaviors that mimic well parts of the experimental literature. Thus it is likely that composite models, where rivalry may emerge from interactions at several levels of neural processing, need to be investigated.

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APPENDIX: SAMPLE CASES OF RIVALRY AND NONRIVALRY

Given the overall hypothesis of the study concerning the chance of rivalry—that stimuli which attract the same categorical cell to respond should not rival, while stimuli which associate with different categorical cells during training will rival—several cases exhibit the success (and variations of success) of the hypothesis.

Expect Rivalry—Get Regular Rivalry

Stim 1 versus stim 14. In this situation, with stim 1 presented to one eye and stim 14 presented to the other, rivalry was expected because stim 1 associated with categorical cell 7, while stim 14 associated with categorical cell 3 (see figure 18.3a). The figure shows a graphical representation of the rivaling stimuli as well as a representation across the 100 iterations (Time) of the left and right strength modifiers. Notice the regular alternation between the modifiers, with reciprocal weakening and strengthening (0.1 per iteration) until the point at which the “winner” (bottom graph, triangle symbols) changes identity. At this point the strength of the winning eye is enhanced by a factor (perhaps analogous to attention), with a corresponding drop in strength for the suppressed stimulus.

The overall strength of this factor was arbitrary, but was fixed throughout all of the network calculations. The similarity measure for the left and right eyes is shown in the second trace. It also shows regularity in alternation that is highly correlated with the switch in Kohonen choice (categorical selection) (lower trace). The “winner” is shown as a series of triangular markers taking on one of two states (upper right, lower left). In this case the change in “winner” occurs at precisely the same time as the change in Kohonen choice.

Expect Rivalry—Get Rivalry with Erroneous Percept

Stim 1 versus stim 18. Given the two stimuli, rivalry was expected between categorical cell 7 (stim 1) and categorical cell 9 (stim 18). However, as figure 18.3b shows, though rivalry was observed, it was between categorical cells 7 and 8 (bottom graph—selection), rather than GC7 and GC9. Note that the strength modifiers for the left and right eyes are again mirror

images around the value 1. Again the rivalry is quite regular, after an initial longer settling period—this appears to be associated with the relatively large change in similarity measure for each eye between the one percept and the other, what one might call “strong rivalry.” It is interesting that while “false” percept was generated, the incorrect categorical cell (GC8 rather then GC9) is a neighboring Kohonen classifier of the expected one, with the implication of some similarity relation between the neighbors. Such erroneous percept (disagreeing with either monocular input) is possible experimentally (Kovács et al., 1996).

Expect Rivalry—Get Irregular Rapid Rivalry Stim 5 (GC6) versus stim 0 (GC0). The lowest graph of figure 18.3c shows a rapid alternation between GC6 and GC0. While the rivalry is between the expected categorical cell representations, the frequency of alternation is irregular. This irregularity possibly arises because of the close similarity measures for the two eyes.

Expect Rivalry—Get Irregular, Infrequent Rivalry Stim 1 (GC7) versus stim 6 (GC0). While the strength modifiers overlap in a fashion very similar to that exhibited in figure 18.3a, the rivalry results are dramatically different. The similarity measures for the two eyes are not at all similar in shape, and the “winner” graph at the extreme bottom of figure 18.3d shows an irregularity in switching. This irregularity is further teased out by the Kohonen selection graph (lowest solid curve, figure 18.3d). The switching between categorical cells is not well predicted by the switching in winner. This possibly is due to the relatively small difference in similarity measure between dominance and suppression states for the two eyes (roughly of amplitude 30, compared with over 100 for figure 18.3a). This might be termed “weak rivalry.”

Failed Rivalry—When Expected

Stim 0 (GC0) versus stim 20 (GC7). One switch in percept near the start of the iteration was observed. After this time, the percept remained stuck at categorical cell 7 (see figure 18.3e). This failure of rivalry (when expected from the difference in categorical cell preference for the two stimuli) can be identified as due to the fact that the similarity measures never approached a crossing point.

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