Information Processing

Information processing is the change (processing) of information in any manner detectable by an observer. As such, it is a process that describes everything that happens (changes) in the universe, from the falling of a rock (a change in position) to the printing of a text file from a digital computer system. In the latter case, an information processor is changing the form of presentation of that text file. Information processing may more specifically be defined in terms used by Claude E. Shannon as the conversion of latent information into manifest information (McGonigle & Mastrian, 2011). Latent and manifest information is defined through the terms of equivocation (remaining uncertainty, what value the sender has chosen), dissipation (uncertainty of the sender what the receiver has received), and transformation (saved effort of questioning – equivocation minus dissipation) (Denning and Bell, 2012).

Within the field of cognitive psychology, information processing is an approach to the goal of understanding human thinking in relation to how they process the same kind of information as computers (Shannon & Weaver, 1963). It arose in the 1940s and 1950s, after World War II (Sternberg & Sternberg, 2012). The approach treats cognition as essentially computational in nature, with mind being the software and the brain being the hardware. The information processing approach in psychology is closely allied to the computational theory of mind in philosophy; it is also related, though not identical, to cognitivism in psychology and functionalism in philosophy (Horst, 2011).

Information processing may be sequential or parallel, either of which may be centralized or decentralized (distributed). The parallel distributed processing approach of the mid-1980s became popular under the name connectionism. The connectionist network is made up of different nodes, and it works by a "priming effect," and this happens when a "prime node activates a connected node" (Sternberg & Sternberg, 2012). But "unlike in semantic networks, it is not a single node that has a specific meaning, but rather the knowledge is represented in a combination of differently activated nodes"(Goldstein, as cited in Sternberg, 2012).

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