MODULAR PROCESSING
The brain can solve immensely difficult computational problems. We can judge distances, we can identify objects, we can walk through complex environments relying solely on vision to guide us. These abilities are way beyond the capacities of current computers, even though their processing elements operate very much faster than our neurons. So, how can we solve complex problems of visual geometry so rapidly? The key lies in the brain’s parallel processing capacity. In principle, different aspects of a visual stimulus are analysed by different modules in the brain. One module may deal with form, another with motion, and another with colour. By splitting up the task in this way, it is possible to solve complicated problems rapidly. There might also be an evolutionary explanation for modularity in the brain. To add a new perceptual analysis feature to our existing perceptual analysis systems, the simplest route would be to leave the existing analysis systems unchanged and simply ‘bolt on’ a new feature. The alternative would be to rewire and reconfigure all the existing systems to add the mechanisms for the new analysis. It is harder to imagine how this might happen without the risk of radically disrupting the pre-existing systems. A computational stratagem like this poses new problems, however. There needs to be some way of ensuring that the different aspects of a stimulus, although processed separately, are nonetheless related to each other. A cricket ball heading towards you is red, shiny, round and moving. You need to know that these separate attributes all refer to the same object. How does our brain solve this problem? It is possible that the different brain regions analysing different aspects of the same stimulus show synchronized oscillations, which act to link these structures together (e.g. Gray et al., 1989). The visual processing modules are in some senses independent, but not completely so. Identifying a shape or form, for example, sometimes depends on solving the problem of colour or reflectance. A uniformly coloured, curved surface, lit from above, may emit different wavelengths of light from different points on the surface. We perceive it as being a single colour partly because we are also seeing it as a curved surface. And our perception of it as being curved equally depends partly upon light intensities and/or wavelengths reaching us from different points on the surface. So form and reflectance need to be solved simultaneously, and the solutions are, to some extent, interdependent. Although parallel processing is still an appropriate way to explain how the brain solves problems, as so often seems to be the case, things are a bit more complex than that.
The brain can solve immensely difficult computational problems. We can judge distances, we can identify objects, we can walk through complex environments relying solely on vision to guide us. These abilities are way beyond the capacities of current computers, even though their processing elements operate very much faster than our neurons. So, how can we solve complex problems of visual geometry so rapidly? The key lies in the brain’s parallel processing capacity. In principle, different aspects of a visual stimulus are analysed by different modules in the brain. One module may deal with form, another with motion, and another with colour. By splitting up the task in this way, it is possible to solve complicated problems rapidly. There might also be an evolutionary explanation for modularity in the brain. To add a new perceptual analysis feature to our existing perceptual analysis systems, the simplest route would be to leave the existing analysis systems unchanged and simply ‘bolt on’ a new feature. The alternative would be to rewire and reconfigure all the existing systems to add the mechanisms for the new analysis. It is harder to imagine how this might happen without the risk of radically disrupting the pre-existing systems. A computational stratagem like this poses new problems, however. There needs to be some way of ensuring that the different aspects of a stimulus, although processed separately, are nonetheless related to each other. A cricket ball heading towards you is red, shiny, round and moving. You need to know that these separate attributes all refer to the same object. How does our brain solve this problem? It is possible that the different brain regions analysing different aspects of the same stimulus show synchronized oscillations, which act to link these structures together (e.g. Gray et al., 1989). The visual processing modules are in some senses independent, but not completely so. Identifying a shape or form, for example, sometimes depends on solving the problem of colour or reflectance. A uniformly coloured, curved surface, lit from above, may emit different wavelengths of light from different points on the surface. We perceive it as being a single colour partly because we are also seeing it as a curved surface. And our perception of it as being curved equally depends partly upon light intensities and/or wavelengths reaching us from different points on the surface. So form and reflectance need to be solved simultaneously, and the solutions are, to some extent, interdependent. Although parallel processing is still an appropriate way to explain how the brain solves problems, as so often seems to be the case, things are a bit more complex than that.
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