How Can the Study of Aftereffects Tell Us About How the Brain Processes Visual Information?

Topics: Retina, Cerebral cortex, Visual perception Pages: 7 (2402 words) Published: April 13, 2013
How Can The Study Of Aftereffects Tell Us About How The Brain Processes Visual Information? The motion & tilt aftereffect (MTAE; Gibson & Radner, 1999) is a simple but intriguing visual phenomenon. After staring at a pattern of tilted lines or gratings, subsequent lines appear to have a slight tilt in the opposite direction. The effect resembles an afterimage from staring at a bright light, but it represents changes in orientation perception rather than in color or brightness. Most modem explanations of the MTAE are loosely based on the feature-detector model of the primary visual cortex (V1), which characterizes this area as a set of orientation-detecting neurons. Experiments showed that these neurons became more difficult to excite: repeated presentation of oriented visual stimuli, and the desensitization persisted for some time. This observation led to the fatigue theory of the MTAE: perhaps active neurons become fatigued due to repeated firing, causing the response to a test figure to change during adaptation. Assuming the perceived orientation is some sort of average over the orientation preferences of the activated neurons, the final perceived orientation would thus show the direct MTAE (Coltheart, 1999). The fatigue theory has been discredited for a number of reasons, chief among which is that individual V1 neurons actually do not appear to fatigue. In fact, their response to direct stimulation is essentially unchanged by adaptation to a visual stimulus. The now-popular inhibition theory postulates that the MTAE instead results from changing inhibition between orientation-detecting neurons. The inhibition hypothesis has recently been incorporated into theories of the larger purpose and function of the cortex. Barlow (2000) and Foldiak (2000) have proposed that the early cortical regions are acting to reduce the amount of redundant information present in the visual input. They suggest that motion & tilt aftereffects are not flaws in an otherwise well-designed system, but an unavoidable result of a self-organizing process that aims at producing an efficient, sparse encoding of the input through decorrelation. Based on these theories, Dong (1999) has shown analytically that perfect decorrelation can result in direct motion & tiltaftereffects that are similar to those found in humans. Only very recently, however, has it become computationally feasible to test the inhibition-decorrelation theory of the MTAE in a detailed model of cortical function, with limitations similar to those known to be present in the cortex. A Hebbian self-organizing process (the receptive-field laterally interconnected synergetically self-organizing map, or RF-LISSOM has been shown to develop feature detectors and specific lateral connections that could produce such motion & tilt aftereffects. The RF-LISSOM model gives rise to anatomical and functional characteristics of the cortex such as topographic maps, ocular dominance, orientation, and size preference columns and the patterned lateral connections between them. Although other models exist that explain how the feature detectors and afferent connections could develop by input-driven visual information, RF-LISSOM is the first model that also shows how the long-range lateral connections self-organize as an integral part of the process. The laterally connected model has also been shown to account for many of the dynamic aspects of the adult visual cortex, such as reorganization following retinal and cortical lesions. These findings suggest that the same visual information processes that drive development may be acting in the adult (see Fregnac, 2001). We explore this hypothesis here with respect to the MTAE. The cortical architecture for the model has been simplified and reduced to the minimum necessary configuration to account for the observed phenomena. Because the focus is on the two-dimensional organization of the cortex, each "neuron" in the model cortex corresponds...
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