Multiple-object Working Memory--A Model for Behavioral Performance

In a psychophysics experiment, monkeys were shown a sequence of two to eight images, randomly chosen out of a set of 16, each image followed by a delay interval, the last image in the sequence being a repetition of any (one) of the images shown in the sequence. The monkeys learned to recognize the repetition of an image. The performance level was studied as a function of the number of images separating cue (image that will be repeated) from match for different sequence lengths, as well as at fixed cue-match separation versus length of sequence. These experimental results are interpreted as features of multi-item working memory in the framework of a recurrent neural network. It is shown that a model network can sustain multi-item working memory. Fluctuations due to the finite size of the network, together with a single extra ingredient, related to expectation of reward, account for the dependence of the performance on the cue-position, as well as for the dependence of performance on sequence length for fixed cue-match separation.