Eytan Ruppin, Yehezkel Yeshurun
This work presents an Attractor Neural Network (ANN) model of Re(cid:173) call and Recognition. It is shown that an ANN model can qualitatively account for a wide range of experimental psychological data pertaining to the these two main aspects of memory access. Certain psychological phenomena are accounted for, including the effects of list-length, word(cid:173) frequency, presentation time, context shift, and aging. Thereafter, the probabilities of successful Recall and Recognition are estimated, in order to possibly enable further quantitative examination of the model.