Evidence map›Paper›PMID 31103359›Full record

ArticleNeuron2019

A Flexible Model of Working Memory.

Flora Bouchacourt, Timothy J Buschman

Abstract read
In one paragraph

Article in Neuron, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

77 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. A population vector model of visual working memory for real-world scenes.Journal of experimental psychology. General · 2026
    Article
  5. Human Beta Oscillations Reflect Magnitude and Fidelity of Priority Shifts in Working Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  6. Dynamics of working memory drift and information flow across the cortical hierarchy.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  7. A Population Vector Model of Visual Working Memory for Real-World Scenes.bioRxiv : the preprint server for biology · 2026
    Article
  8. Article
  9. Article
  10. Neuronal normalization in monkey MT is an intensity-weighted average.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  11. Distributed and drifting signals for working memory load in human cortex.bioRxiv : the preprint server for biology · 2025
    Article
  12. Article
  13. Article
  14. Neuronal normalization in monkey MT is an intensity-weighted average.bioRxiv : the preprint server for biology · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Is working memory domain-general or domain-specific?Trends in cognitive sciences · 2024
    Review

17 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Flora BouchacourtPrinceton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA.
Timothy J BuschmanPrinceton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA; Department of Psychology, Princeton University, Princeton, NJ 08540, USA. Electronic address: tbuschma@princeton.edu.

Funding

Understanding the Network Mechanisms that Control Working MemoryR56MH115042 · NIMH · PRINCETON UNIVERSITY · PI BUSCHMAN, TIMOTHY J. · 2018 to 2019
$546k
NIMH NIH HHS R56 MH115042
6 · The paper itself

Abstract

Working memory is fundamental to cognition, allowing one to hold information "in mind." A defining characteristic of working memory is its flexibility: we can hold anything in mind. However, typical models of working memory rely on finely tuned, content-specific attractors to persistently maintain neural activity and therefore do not allow for the flexibility observed in behavior. Here, we present a flexible model of working memory that maintains representations through random recurrent connections between two layers of neurons: a structured "sensory" layer and a randomly connected, unstructured layer. As the interactions are untuned with respect to the content being stored, the network maintains any arbitrary input. However, in our model, this flexibility comes at a cost: the random connections overlap, leading to interference between representations and limiting the memory capacity of the network. Additionally, our model captures several other key behavioral and neurophysiological characteristics of working memory.

Indexed as

Models, NeurologicalAlgorithmsComputer SimulationHumansMemory, Short-TermNerve NetNeural PathwaysNeuronsSensationcapacity limitationscognitive controlcognitive flexibilitycomputational modelexcitation-inhibition balancemixed selectivityworking memory

Identifiers

PMID31103359
PMCPMC6613943

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.