The Computational Approach Theory to Psychological Perception

2021-05-06 07:22:11
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Many theories have been advanced by different scholars with the aim of explaining the functioning of the human mind. Among these theories, the computational approach theory has been among the leading theories in explanation of this particular concept. In this paper, focus has been accorded to the computational approach and how it relates to other psychological phenomena like distance and size perception, motion perception, and the visual pattern perception. Computational approach was put forward by David Marr, a neurobiologist and computer scientist. Marrs work on this topic contributed greatly to a theoretical analysis of vision in scientific problem by proposing vision theories in different areas. Some of the areas in which theories relating to Marrs work were used include; edge detection and perception of depth and shape. The neurobiologist made distinction between algorithmic, computational, and implementation levels of analysis, and this guided the thoughts of vision scientists since then. Marr made it clear that the algorithmic level is to specify an algorithm or procedure for carrying out the aim specified by the computational level (Marr, 2010). Additionally, the scientist emphasized on the criticality of the computational level as it is the level at which the problem is describe to allow for the entire system to solve. Finally, he explained that the implementation level deals with the details of the hardware in which the algorithm is embodied. The theory relates the working of a computer and its components to the working of the human brain. Different models have been developed to illustrate this relationship as well. The computational approach is a psychological theory that relates the working of the human mind or brain with a computer system (Foley, Hugh & Matlin, 2010). The theory lies under computational psychology, a discipline that lies in between artificial intelligence and psychology. Computational psychology is concerned with building of computer models of human cognitive processes (Marr, 2010). Normally, this is based on an analogy between the human mind and computer programs.

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Relationships between the computational approach theory to psychological perception.

Perception can be defined as the organization, identification and interpretation of sensory information with the motive of representing and understanding the environment. The mind perceives signals transmitted to it by the nervous system, while the nervous system obtains the information from the sensory organs. The sensory organs in this case include the eyes, nose, skin, ears, and the tongue. Immediately information is received by the sensory organs, it is transmitted to the brain via sensory neurons for interpretation. Once the signals (nerve impulses) reach the brain, they are organized, identified, and interpreted. The brain then makes the necessary decision and sends impulses to the right organs for action (Foley, Hugh & Matlin, 2010). To understand perception, one will need to deeply understand all the biological components that are involved in perception. Marr said, trying to understand perception by studying only neurons is like trying to understand bird flight by studying only feathers. He said to understand bird flight, one needs to understand aerodynamics in detail. It is at this point that the different structure of feathers as well as different shapes of bird wings can make sense.

Likewise, in order to comprehend how the human visual system works, the computational approach becomes of gross importance. In relation to Marrs works, vision can be used to explain the interrelationship between computational approaches to other psychological disciplines. In this case, the sense of sight is used and its relationship with the computer hardware illustrated. Also, the human sensory nerves (neurons) are compared to the computers inputting devices, while the brain is compared to the computers central processing unit (CPU). The results actions taken by the human body after interpretation by the brain are compared to the outcome displayed on the computer monitor after processing by the CPU. Just like a computer system receives commands from the hard ware and software, the brain too receives instruction from the sensory organs.

Therefore, a clear understanding of all the organs involved in perception, as well as the computational approach, enables one to correlate the computational approach to various perceptual concepts (, 2016). For instance, size perception can be illustrated by the use of a pen held at different distances form a viewers eye. A pan of approximately 12 cm will require a large pupil angle to view if it is held close to ones eye. However, as the pen is moved away from a viewers eye, the pupil angle decreases but the brain maintains the size of the pen as 12 cm (, 2016). Also a simple diagrammatic illustration can be used to explain how human brain perceives motion. For example in the figure below, the viewer will make a judgment that the direction of motion of the shape was clockwise. This is because, through the expectation-Maximization theory, the viewer will expect that a small rotation in the clockwise direction was made.

The above illustration is a combination of pattern and motion perceptions. The patterns on the two figures have been used to predict the motion exhibited by the shapes.

In conclusion, Marrs work as the father of computational approach has gone a long way in assisting scientists in explanation of the working of the human brain. A great relationship between the computational approach and other psychological concepts can also be noted from the illustrations of the approach to the various perceptions as above. The above illustration is a combination of pattern and motion perceptions.

References,. (2016). The computational approach to vision. Retrieved 24 February 2016, from

Foley, Hugh J., Matlin, M. W. (2010). Sensation and Perception, 5th Edition. [VitalSource Bookshelf Online]. Retrieved from, D. (2010). Vision. Cambridge, Mass.: MIT Press.

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