Prediction of free word associations based on Hebbian learning

Abstract

An associative lexical net whose weights are computed on the basis of the co-occurrences of words using Hebb's rule has been built. The co-occurrences of word pairs are determined by shifting a window over a large body of text. To estimate the associative response to a given stimulus word, the corresponding node is activated and its activity is propagated in the net. The proposed model assumes that words with high activities after propagation correspond to the associative responses of human subjects. These predictions have been tested and confirmed using the association norms collected by Russel and Jenkins.<>