Theories of Humor - Computational-Neural Theory of Humor

Computational-Neural Theory of Humor

The Computer Model of a Sense of Humor theory was suggested by Suslov in 1992. Investigation of the general scheme of the information processing show a possibility of a specific malfunction, conditioned by the necessity of a quick deletion from consciousness of a false version. This specific malfunction can be identified with a humorous effect on the psychological grounds: it exactly corresponds to incongruity-resolution theory. However, an essentially new ingredient, a role of timing, is added to a well known role of ambiguity. In biological systems, a sense of humor inevitably develops in the course of evolution, because its biological function consists in quickening the transmission of the processed information into consciousness and in a more effective use of brain resources. A realization of this algorithm in neural networks justifies naturally Spencer’s hypothesis on the mechanism of laughter: deletion of a false version corresponds to zeroing of some part of the neural network and excessive energy of neurons is thrown out to the motor cortex, arousing muscular contractions. The theory treats on equal footing the humorous effect created by the linguistic means (verbal humor), as well as created visually (caricature, clown performance) or by tickling. The theory explains naturally different susceptibility of people to humor, absence of humorous effect from a trite joke, a role of intonation in telling jokes, nervous laughter, etc. According to this theory, the humor has a pure biological origin, while its social functions arose later. This conclusion corresponds to the known fact that already monkeys (as pointed by Charles Darwin) and even rats (as found recently) possess the sense of humor.

A practical realization of this algorithm needs extensive databases, whose creation in the automatic regime was suggested recently.