title: Multiple-object working memory--a model for behavioral performance.
abstract: 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.
authors.affiliation: Dipartimento di Fisica, Istituto di Fisica (INFM), Università di Roma La Sapienza, Piazzale A Moro 1, 00185, Roma, Italy. daniel.amit@roma1.infn.it
authors.forename: D J
authors.initials: DJ
authors.lastname: Amit
authors.forename: A
authors.initials: A
authors.lastname: Bernacchia
authors.forename: V
authors.initials: V
authors.lastname: Yakovlev
mesh_terms: D000818:Animals; D003463:Cues; D000882:Haplorhini; D008568:Memory / Q000502:physiology*; D016571:Neural Networks, Computer; D010775:Photic Stimulation / Q000379:methods; D021641:Recognition, Psychology / Q000502:physiology