Parallel processing (psychology)

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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. These are individually analyzed and then compared to stored memories, which helps the brain identify what you are viewing.[2] The brain then combines all of these into the field of view that then seen and comprehended.[3] 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.[4]

Background[]

Parallel Distributed Processing Models are 'neurally inspired', emulating the organisational structure of nervous systems of living organisms.[5] 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.[6]

Serial vs Parallel Processing[]

In contrast to parallel processing, serial processing involves sequential processing of information, without any overlap of processing times.[7]

Visual Search[]

In case of serial processing, the elements are searched one after the other in a serial order to find the target. When the target is found, the search terminates. Alternatively, it continues to the end to ensure that the target is not present. This results in reduced accuracy and increased time for displays with more objects.

On the other hand, in the case of parallel processing, all objects are processed simultaneously but the completion times may vary. This may or may not reduce the accuracy, but the time courses are similar irrespective of the size of the display.[8]

However, there are concerns about the efficiency of parallel processing models in case of complex tasks which are discussed ahead in this article.

Aspects of a parallel distributed processing model[]

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

Processing Units[]

These units may include abstract elements such as features, shapes and words, and are generally categorised into three types: input, output and hidden units.

  • 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.

Activation State[]

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.

Output Functions[]

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.

Connectivity Patterns[]

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.

Propagation Rule[]

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.

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.

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.

Environmental Representation[]

In PDP models, the environment is represented as a time-varying stochastic function over the space of input patterns.[9] 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.  [5]

Limitations[]

Limitations of parallel processing have been brought up in several analytical studies. The main limitations highlighted include capacity limits of the brain, attentional blink rate interferences, limited processing capabilities, and information limitations in visual searches.

There are processing limits to the brain in the execution of complex tasks like object recognition. All parts of the brain cannot process at full capacity parallely. Attention controls the allocation of resources to the tasks. To work efficiently, attention must be guided from object to object.[10]

These limits to attentional resources sometimes lead to serial bottlenecks in parallel processing, meaning that parallel processing is obstructed by serial processing in between. However, there is evidence for coexistence of serial and parallel processes.[11]

Feature Integration Theory[]

The Feature Integration Theory by Anne Treisman is one of the theories that integrates serial and parallel processing while taking into account attentional resources. It consists of two stages-

  1. Detection of features- This stage occurs instantaneously and uses parallel processing. In this step, we pick up all the basic features of a display simultaneously, even if we are paying attention to a specific object.
  2. Integration of features- This step is more time consuming and uses serial processing. It leads to the perception of whole objects and patterns.[12]

See also[]

References[]

  1. ^ LaBerge, David; Samuels, S.Jay (1974). "Toward a theory of automatic information processing in reading". Cognitive Psychology. Elsevier BV. 6 (2): 293–323. doi:10.1016/0010-0285(74)90015-2. ISSN 0010-0285.
  2. ^ Hinton, Geoffrey (2014). Parallel models of associative memory. New York: Psychology Press. ISBN 978-1-315-80799-7.
  3. ^ Wässle, Heinz (2004). "Parallel processing in the mammalian retina". Nature Reviews Neuroscience. 5 (10): 747–757. doi:10.1038/nrn1497. ISSN 1471-003X. PMID 15378035.
  4. ^ Cohen, J. D.; Dunbar, K.; McClelland, J. L. (1988-06-16). "On the Control of Automatic Processes: A Parallel Distributed Processing Model of the Stroop Effect". Fort Belvoir, VA. {{cite journal}}: Cite journal requires |journal= (help)
  5. ^ a b Rumelhart, David E. (1986). Parallel distributed processing : explorations in the microstructure of cognition. James L. McClelland, San Diego. PDP Research Group University of California. Cambridge, Mass.: MIT Press. ISBN 0-262-18120-7. OCLC 12837549.
  6. ^ Holyoak, Keith J. (1987). Rumelhart, David E.; McClelland, James L.; Group, PDP Research (eds.). "A Connectionist View of Cognition". Science. 236 (4804): 992–996. ISSN 0036-8075.
  7. ^ Townsend, James T. (January 1990). "Serial vs. Parallel Processing: Sometimes They Look like Tweedledum and Tweedledee but they can (and Should) be Distinguished". Psychological Science. 1 (1): 46–54. doi:10.1111/j.1467-9280.1990.tb00067.x. ISSN 0956-7976.
  8. ^ Dosher, Barbara Anne; Han, Songmei; Lu, Zhong-Lin (2010). "Information-limited parallel processing in difficult heterogeneous covert visual search". Journal of Experimental Psychology: Human Perception and Performance. 36 (5): 1128–1144. doi:10.1037/a0020366. ISSN 1939-1277.
  9. ^ Snodgrass, Joan Gay; Townsend, James T.; Ashby, F. Gregory (1985). "Stochastic Modeling of Elementary Psychological Processes". The American Journal of Psychology. 98 (3): 480. doi:10.2307/1422636. ISSN 0002-9556.
  10. ^ Wolfe, Jeremy M. (August 1992). "The Parallel Guidance of Visual Attention". Current Directions in Psychological Science. 1 (4): 124–128. doi:10.1111/1467-8721.ep10769733. ISSN 0963-7214.
  11. ^ Sigman, Mariano; Dehaene, Stanislas (2008-07-23). "Brain Mechanisms of Serial and Parallel Processing during Dual-Task Performance". Journal of Neuroscience. 28 (30): 7585–7598. doi:10.1523/JNEUROSCI.0948-08.2008. ISSN 0270-6474. PMID 18650336.
  12. ^ "Features and Objects in Visual Processing", Foundations of Cognitive Psychology, The MIT Press, 2002, retrieved 2022-02-16
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