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The neuroscience of working memory capacity and training

Key Points

  • The amount of information that can be maintained in working memory (WM) is limited. An individual's WM capacity is predictive of performance in higher cognitive abilities.

  • Although traditionally viewed as an immutable aptitude, more recently WM has been shown to improve with training. Importantly, improvement in WM can transfer between trained and non-trained tasks.

  • There are several models for the neural basis of WM. In this Review, we argue that the persistent discharges of prefrontal neurons play the most important part in the maintenance of information in WM.

  • Effects of WM training include increases in the activity of neurons in the prefrontal cortex, and increases in the strength of connectivity in the prefrontal cortex and between the prefrontal cortex and other areas.

  • Neural changes after training are found in cortical areas that process spatial information in WM and attention, potentially providing a basis for transfer to other cognitive and behavioural tasks that rely on spatial WM and spatially selective attention.

Abstract

Working memory — the ability to maintain and manipulate information over a period of seconds — is a core component of higher cognitive functions. The storage capacity of working memory is limited but can be expanded by training, and evidence of the neural mechanisms underlying this effect is accumulating. Human imaging studies and neurophysiological recordings in non-human primates, together with computational modelling studies, reveal that training increases the activity of prefrontal neurons and the strength of connectivity in the prefrontal cortex and between the prefrontal and parietal cortex. Dopaminergic transmission could have a facilitatory role. These changes more generally inform us of the plasticity of higher cognitive functions.

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Figure 1: Neural basis of working memory.
Figure 2: Working memory tasks.
Figure 3: Summary of factors underlying training-induced increases in capacity.

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Acknowledgements

This work was supported by US National Institutes of Health grant R01EY017077 and The Tab Williams Family Endowment (to C.C.).

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Correspondence to Torkel Klingberg.

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Glossary

Choice probability

Probability that the firing rate of one neuron in two identical stimulus conditions is different depending on the subject's choice.

Receptive field

Area of the visual field where a stimulus will elicit the firing of neuron under study.

Repetition suppression

A phenomenon whereby a stimulus that has been repeated elicits a smaller response than does a stimulus that appears for the first time.

Bump attractor

A stable state of activation of a network, with a spatial location maximally activated and adjacent locations activated to a lesser extent, forming a bump.

Gain mechanism

Mechanism of representing of two variables, whereby the activity of a neuron depends linearly on one continuous variable, multiplied by second variable.

Tuning curve

A graph of firing-rate intensity depending on the location (or other dimension) of a stimulus.

Functional connectivity

The likelihood that activity in one area leads to activity in the other.

Fano factor

Variance of spike counts divided by their mean, per unit of time.

Classifier

A model or algorithm achieving a classification decision of what a stimulus is, based on the combined activity of multiple neurons or voxels.

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Constantinidis, C., Klingberg, T. The neuroscience of working memory capacity and training. Nat Rev Neurosci 17, 438–449 (2016). https://doi.org/10.1038/nrn.2016.43

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