(9-10 Hz) rises about 0. 7-1. 0 sec before target disap-pearance and higher frequencies are suppressed untilabout 1 sec prior to reappearance of target structures(Keesey & Nichols, 1967; Lehmann et al., 1965). Overview of Stabilized Image Studies: Only a Central Explanation Will Do The textbook account of stabilized image perception is that fading is due to the transient response propertiesof some retinogeniculate units. That analysis cannotaccount for counterintuitively rapid fading of stimulithat favor highly sustained parvocellular mechanisms. Nor can it account for any of the explicitly corticaleffects discussed above. Of these, image fading andfragmentation are consistent with known failure modes of segmentation networks that require a balance of competitive and cooperative interactions; fading canstem from relative weakness of competitive interac-tions; fragmentation can stem from weakness of coop-erative interactions. The Gestalt properties shown instabilized image studies correspond to emergent prop-erties of cooperative/competitive segmentation net-works (Horn & Opher, 2000; Wang & Terman, 1997; Sporns et al., 1991). As early as 1960, Pritchard et al. argued that fragmentation and partial reappearance offaded stabilized images is evidence for the dynamicformation of Hebbian cell assemblies—essentially thesame argument advanced in modern binding theory. Here, we argue that binding of units responding tononstabilized images is facilitated by the common in-formation in each form and motion mechanism induced by eye movements perturbing the retinal image, allow-ing motion and form-based segmentation networks toreinforce each other. DISCUSSION Summary of Reasoning At this point, it is useful to summarize our reasoning and the evidence that supports it so that we can lay out its strengths and weaknesses, and point out where futurework needs to be done: 1. Feng (2000) and Horn et al. (1991) showed that a preexisting correlation between mechanisms facilitates synchronizing those mechanisms' activity, a form offeature binding. In Horn et al. 's and Feng's work, theseinitial correlations stem from unspecified noise. What weadd here is an explicit physiological source for suchsignals (crosstalk induced by cortical demultiplexing of LGN signals), which results in stimulus-driven correla-tions between cortical mechanisms for achromatic form and color, and between achromatic form and motion. 2. Several investigators point out that feature-binding between mechanisms responding to common parts ofan image can improve part-binding/segmentation (Schil-len & Ko ¨nig, 1994; Finkel & Edelman, 1989; Poggio et al., 1988), a process called crossmodal reinforcement. 3. Other investigators point out that some part-binding (segmentation) models can fail in interestingways, for example, fragmentation of images (Terman &Wang, 1995; Marr et al., 1978). What we add to this is adiscussion of various ways a segmentation networkcould fail (depending on whether cooperative or com-petitive interactions were inadequate). 4. We point out that some odd percepts (e. g., fragmentation of stabilized images and melting ofequiluminant images) closely resemble these expectedfailure modes of segmentation models. We suggestthat these part-binding failures may be due to loss ofcrossmodal reinforcement (Point 2) that under normalcircumstances is available and relatively easy to bind because of stimulus-driven correlations (Point 1). Modeling—What Remains To Be Done Although each point in the above analysis is supported by experiments or mathematical models, it would bedesirable to supplement this analysis with a formalintegrated simulation and with a set of experimentalpredictions to be tested. We provide some such predic-tions in the sections below. A formal integrated simula-tion lies beyond the scope of this article, but we can lay out what would be required to implement one. For simplicity, we discuss form and color binding here—the steps required for form and motion binding arequite similar. (a) Start with a segmentation model, like Terman and Wang's (1995) that fails in one of theinteresting ways described in Point 4, and manipulatethe balance of competition and cooperation in themodel until it so fails. Two copies of this segmentation network are created, although they need not be sym-metrical (only failure in the color segmentation system isbeing studied in this example). (b) Create two sets ofinput mechanisms to drive the networks in (a)—an arrayof color units obeying Equation 9 and an array ofachromatic form units obeying Equation 8. To parame-terize these equations, the S e,Te,Si, and Tifunctions describing the spatial and temporal excitatory and in-hibitory responses of P cells need to be specified using psychophysical or physiological data. Suitable functions(derived psychophysically) can be found in Kelly (1989)and Burbeck and Kelly (1980). Under ordinary (non-equiluminant) conditions, there should be a correlationbetween the color inputs described by Equation 9 andthe achromatic units described by Equation 8. Thiscorrelation is implicit in the crosstalk terms of these equations, but can be made explicit using Equation 10. Now run the segmentation networks so that they syn-chronize for normal stimuli (stimuli that contain bothhue and luminance information). (c) Set the stimuli tobe equiluminant. The response of the achromatic form-1044 Journal of Cognitive Neuroscience Volume 16, Number 6