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Cerebral Cortex Advance Access published online on September 1, 2004
Cerebral Cortex, doi:10.1093/cercor/bhh161
© 2004 by Oxford University Press
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Article

Multi-item Working Memory -- A Behavioral Study

Volodya Yakovlev 1, Alberto Bernacchia 2, Tanya Orlov 1, Shaul Hochstein 1*, Daniel Amit 3

1 Neurobiology Department, Institute of Life Sciences and Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel
2 Dip. Di Fisiologia Umana and INFM Dip. di Fisica (INFM), Università di Roma, La Sapienza, Rome, Italy
3 Istituto di Fisica (INFM), Università di Roma, La Sapienza, Rome, Italy; Racah Institute of Physics, Hebrew University, Jerusalem, Israel

* To whom correspondence should be addressed. E-mail: shaul{at}vms.huji.ac.il.


  Abstract

Macaque monkeys were trained to recognize the repetition of one of the images already seen in a sequence of random length. On average, performance decreased with sequence length. However, this was due to a complex combination of factors, as follows: performance was found to decrease with the separation in the sequence of the test (repetition image) from the cue (its first appearance in the sequence), for trials with sequences of fixed length. In contrast, performance improved as a function of sequence length, for equal cue-test separations. Reaction times followed a complementary trend: they increased with cue-test separation and decreased with sequence length. The frequency of false positives (FPs) indicates that images are not always removed from working memory between successive trials, and that the monkeys rarely confuse different images. The probability of miss errors depends on number of intervening stimulus presentations, while FPs depend on elapsed time. A simple two-state stochastic model of multi-item working memory is proposed that guides the account for the main effects of performance and false positives, as well as their interaction. In the model, images enter WM when they are presented, or by spontaneous jump-in. Misses are due to spontaneous jump-out of images previously seen.

Keywords: attractor neural networks; multiple memories; recency; serial order; Sternberg; working memory.
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