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Cerebral Cortex, Vol. 13, No. 5, 435-443, May 2003
© 2003 Oxford University Press

Multiple-object Working Memory—A Model for Behavioral Performance

D.J. Amit1,2, A. Bernacchia1 and V. Yakovlev3

1 Dipartimento di Fisica, Istituto di Fisica (INFM), Università di Roma ‘La Sapienza’, Piazzale A. Moro 1, 00185, Roma, Italy, , 2 Racah Institute of Physics and , 3 Institute of Life Sciences, Hebrew University, Jerusalem, Israel

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.


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