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The right time to learn: mechanisms and optimization of spaced learning

Nature Reviews Neuroscience volume 17, pages 77–88 (2016)Cite this article

Subjects

Key Points

  • Spaced learning or training is robustly superior to massed training for many forms of human learning and for virtually every animal model system that has been examined. The phenomenon has been a major focus of learning theorists for the past 50 years.

  • Recent cellular and molecular data have begun to elucidate mechanisms for the efficacy of spaced training. Dynamics of signalling cascades, dendritic spine remodelling and transcription cooperate to sustain this efficacy.

  • Some of these data appear to align with a traditional learning theory, deficient-processing theory.

  • Computational models of signalling cascades can be used to predict irregularly spaced protocols to enhance learning or rescue learning deficits.

  • Models further suggest ways in which combining pharmacotherapy with optimized spaced protocols can further enhance learning. This strategy may have promise for improving learning in some human cognitive disorders.

Abstract

For many types of learning, spaced training, which involves repeated long inter-trial intervals, leads to more robust memory formation than does massed training, which involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently have data begun to delineate the underlying cellular and molecular mechanisms of spaced training, and we review these theories and data here. Computational models of the implicated signalling cascades have predicted that spaced training with irregular inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning.

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Figure 1: Early conceptual model of how learning trace dynamics generate an optimal interval.
Figure 2: Model and hypotheses describing synaptic strengthening during spaced learning.
Figure 3: Different mechanisms may underlie enhancement of learning by spaced intervals of widely varying lengths.
Figure 4: Dynamics of a model that has successfully predicted greater efficacy for a learning protocol with irregularly spaced intervals.
Figure 5: A model predicts that a pair of drugs can act synergistically to enhance LTP.

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Acknowledgements

This work was supported by US National Institutes of Health grants NS073974 and NS019895.

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  1. Department of Neurobiology and Anatomy, W. M. Keck Center for the Neurobiology of Learning and Memory, The University of Texas Medical School at Houston, P.O. BOX 20708, Houston, 77030, Texas, USA

    Paul Smolen, Yili Zhang & John H. Byrne

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  1. Paul Smolen
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The authors declare no competing financial interests.

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Glossary

Memory extinction

The decline of a learned behavioural response to a conditioned stimulus following the withdrawal of reinforcement stimuli that were previously paired with repetitions of the conditioned stimulus.

Reinforcement

A broad term used here to describe a stimulus or item that enhances the strength or lifetime of a memory.

Habituation

A decrease in the behavioural response to a stimulus following frequent repetitions of that stimulus; this term is distinct from extinction, because habituation can denote a decrease in response to a stimulus that was never paired with a reinforcing stimulus.

Memory reactivations

These are reinstatements of conditioned behavioural responses or of neural activity associated with specific responses and can be elicited by presentation of a conditioning stimulus or of the context in which learning previously occurred, or be spontaneous, occurring as a part of normal ongoing neural activity.

Drug synergism

In combined-drug treatment, a synergistic effect of the combination is an effect that is greater than that which would be predicted by considering the individual drugs as independent and not interacting.

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Smolen, P., Zhang, Y. & Byrne, J. The right time to learn: mechanisms and optimization of spaced learning. Nat Rev Neurosci 17, 77–88 (2016). https://doi.org/10.1038/nrn.2015.18

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