Multi-item working memory - A behavioral study

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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.

Original languageAmerican English
Pages (from-to)602-615
Number of pages14
JournalCerebral Cortex
Volume15
Issue number5
DOIs
StatePublished - May 2005

Bibliographical note

Funding Information:
This study was supported by a Center of Excellence Grant ‘Changing Your Mind’ from the Israel Science Foundation and a Center of Excellence Grant ‘Statistical Mechanics and Complexity’ (SMC) from the INFM, Roma-1, as well as a grant from the National Institute for Psychobiology in Israel to VY. A.B. is grateful for a PhD grant of the SMC Center. We thank Dr Ehud Zohary for assistance throughout this study, Ms Svetlana Lein for software support for the behavioral experiments, and Dr Boris Gutkin for bringing the Haarmann and Usher model to our attention.

Keywords

  • Attractor neural networks
  • Multiple memories
  • Recency
  • Serial order
  • Sternberg
  • Working memory

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