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= Parallel processing (psychology) = Jump to navigationJump to search In psychology, parallel processing is the ability of the brain to simultaneously process incoming stimuli of differing quality. Parallel processing is a part of vision in that the brain divides what it sees into four components: color, motion, shape, and depth. These are individually analyzed and then compared to stored memories, which helps the brain identify what you are viewing. The brain then combines all of these into the field of view that you see and comprehend. Parallel processing has been linked, by some experimental psychologists, to the Stroop effect. This is a continual and seamless operation. for example: if one is standing between two different groups of people who are simultaneously carrying on two different conversations, one may be able to pick up only some information of both conversation at the same time.

Background
Parallel Distributed Processing Models are "neurally inspired". A general mathematical framework is provided for them.

Parallel processing models assume that information is represented in the brain using patterns of activation. Information processing encompasses the interactions of neuron like “units” linked by synapse like “connections”. These can be either excitatory or inhibitory. Every individual unit's activation level is updated using a function of connection strengths and activation level of other units. A set of response units is activated by the propagation of activation patterns. The connection weights are eventually adjusted using learning.

There are eight major aspects of a parallel distributed processing model:


 * 1) A set of processing units- These units may include abstract elements such as features, shapes, words, etc. The units are generally categorised into three types- input, output and hidden. Input units receive signals from either sensory stimuli or other parts of the processing system. The output units send signals out of the system. The hidden units function entirely inside the system.
 * 2) A state of activation- This is a representation of the state of the system. The pattern of activation is represented using a vector of N real numbers, over the set of processing units. It is this pattern that captures what the system is representing at any time.
 * 3) An output function for each unit-  An output function maps the current state of activation to an output signal. The units interact with their neighbouring units by transmitting signals. The strengths of these signals are determined by their degree of activation. This in turn affects the degree to which they affect their neighbours.
 * 4) A pattern of connectivity among units- the pattern of connectivity determines how the system will react to an arbitrary input. The total pattern of connectivity is represented by specifying the weights for every connection. A positive weight represents an excitatory input and a negative weight represents an inhibitory input.
 * 5) A rule of propagation- A net input is produced for each type of input using rules that take the output vector and combine it with the connectivity matrices. In the case of a more complex pattern connectivity, the rules are more complex too.
 * 6) An activation rule- A new state of activation is produced for every unit by joining the net inputs of impinging units combined together and the current state of activation for that unit.
 * 7) A learning rule- The patterns of connectivity are modified using experience. The modifications can be of three types: First, the development of new connections. Second,  the loss of existing connection. Last, the modification of strengths of connections that already exist.  The first two can be considered as special cases of the last one. When the strength of a connection is changed from zero to a positive or negative one, it is like forming a new connection. When the strength of a connection is changed to zero, it is like losing an existing connection.
 * 8) Representation of the environment- In PDP models, the environment is represented as a time-varying stochastic function over the space of input patterns. This means that at any given point, there is a possibility that any of the possible set of input patterns is impinging on the input units.