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In psychology, parallel processing is the ability of the brain to simultaneously process incoming stimuli of differing quality. [1] Parallel processing is associated with the visual system in that the brain divides what it sees into four components: color , motion , shape , and depth .
Much of the early work that applied a predictive coding framework to neural mechanisms came from sensory processing, particularly in the visual cortex. [3] [12] These theories assume that the cortical architecture can be divided into hierarchically stacked levels, which correspond to different cortical regions. Every level is thought to house ...
Studies of sensory processing in humans and other animals has traditionally been performed one sense at a time, [10] and to the present day, numerous academic societies and journals are largely restricted to considering sensory modalities separately ('Vision Research', 'Hearing Research' etc.). However, there is also a long and parallel history ...
[4] [11] High object speeds have a similar effect—faster objects are harder to track, and humans are completely unable to track objects that move sufficiently fast. This "speed limit", however, is much slower than the maximum object speed at which humans can judge the object's movement direction.
[2] [3] [4] These stages are: Stage 1 Processing of basic object components, such as color, depth, and form. Stage 2 These basic components are then grouped on the basis of similarity, providing information on distinct edges to the visual form. Subsequently, figure-ground segregation is able to take place.
[11] [12] In fact, many experiments have supported that primates' capacity for numbers is comparable to human children. [11] Through these experiments, it is clear that there are several neurological processing mechanisms at work— the approximate number system (ANS), number ordinality, the parallel individuation system (PNS), and subitization ...
Systolic arrays (< wavefront processors), first described by H. T. Kung and Charles E. Leiserson are an example of MISD architecture. In a typical systolic array, parallel input data flows through a network of hard-wired processor nodes, resembling the human brain which combine, process, merge or sort the input data into a derived result.
These tasks are assigned individually to many processors. The amount of work associated with a parallel task is low and the work is evenly distributed among the processors. Hence, fine-grained parallelism facilitates load balancing. [3] As each task processes less data, the number of processors required to perform the complete processing is high.