HEBB's RULE 2)
"…a synaptic connection that is used often will increase its strength so that the probability that it will be used later on increases".
This rule, quoted by F. HEYLIGHEN (1990b), may seemingly be extended to any type of learning network, able to learn by experience. It is however ambiguous, since it may lead the network into a rut, as in Pavlovian and Skinnerian conditioning.
St. KAUFFMAN describes this situation: "… a difficulty with most versions of the Hebbian synapse is that the system tends to dig itself into a Hebbian hole deep in the solid regime. Attractors tend to become too deeply grooved into the system. This embedding may inhibit flexible learning by trapping the system too readily in suboptimal responses" (1993, p.229).
This could be called the grooving problem and its possible relation to organizational closure should be investigated. This could even be significant in social systems.
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Bertalanffy Center for the Study of Systems Science (2020). Title of the entry. In Charles François (Ed.), International Encyclopedia of Systems and Cybernetics (2). Retrieved from www.systemspedia.org/[full/url]
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