Cited by 328Memory and Neural MechanismsNeuroscience and Neuropharmacology ResearchMemory Processes and Influences

The DOI matches its Crossref record; 111 of the 111 references it cites were found in a registry or an index; none of the 171 sources its claims cite is checked for support yet.

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

Open access
Smolen, Paul, Zhang, Yili, Byrne, John H.·2016·Nature Reviews Neuroscience

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.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This review synthesizes cognitive theories and molecular/cellular evidence for why spaced training produces stronger memory than massed training, emphasizing refractory/priming processes, kinase and transcription dynamics, and computational optimization. It argues that models of signaling cascades can predict irregular spaced protocols and drug combinations that enhance or rescue synaptic plasticity, though many mechanisms remain incompletely validated.

Key findings

  • Spaced training is superior to massed training across many forms of learning and species. In *Aplysia*, five spaced 5-HT pulses (5 minutes each, 20-minute inter-stimulus interval) robustly elicit long-term facilitation lasting more than 24 hours, whereas continuous 5-HT over 25 minutes fails; four spaced electrical stimuli with 30-minute intervals yield long-term sensitization lasting more than 24 hours, with weak residual sensitization at 4 days, and daily repetition for 4 days yields stronger sensitization lasting more than 1 week.
  • Hippocampal LTP studies show that theta-burst stimuli at 10- or 40-minute intervals did not cumulatively increase LTP, but intervals of 60 or 90 minutes did; a refractory period of ~60 minutes is associated with priming and spine remodeling, and integrin receptor replacement over ~40–60 minutes is hypothesized to underlie this refractory period.
  • Optimal intervals are system-specific: in *Aplysia*, maximal long-term sensitization occurred with a 45-minute inter-stimulus interval, not 15 or 60 minutes; in mice, 60-minute intervals enhanced object recognition learning and ERK1/2 signaling in wild-type and fragile X syndrome model mice, whereas 20- or 120-minute intervals did not.
  • A computational model simulated 10,000 protocols of five trials with intervals of 0–45 minutes; the predicted irregular “enhanced” protocol produced greater LTF and LTS than the standard 20-minute-interval protocol. Extended models predicted rescue protocols after CBP or CREB1 knockdown, and a drug-modeling study found no single-drug effect fully rescued LTP in a Rubinstein–Taybi syndrome model, but two paired-drug effects did, with additive synergism.

Methods

  • Narrative review, not a primary experimental study; it integrates traditional learning theories (encoding variability, study-phase retrieval, deficient-processing/consolidation) with molecular and cellular findings.
  • Evidence is drawn from cited studies across humans, *Aplysia*, *Drosophila*, bees, rodents, non-human primates, hippocampal slices, and cell cultures, plus computational models of PKA, ERK/MAPK, CREB/C/EBP, CBP, LTP, and LTF.
  • Main analysis is qualitative synthesis and discussion of model predictions and empirical validations; no systematic review or meta-analytic methods are stated.

Limitations

  • Stated: some aspects of the spacing effect cannot be explained by current models; future models are likely incomplete owing to gaps in knowledge and uncertainties in biochemical parameters such as enzyme activities or protein concentrations.
  • Stated: some proposed mechanisms lack empirical validation (for example, the dCREB2 isoform phosphorylation competition hypothesis); network-level data are needed to assess encoding variability and study-phase retrieval theories; few studies delineate both minimum and maximum effective intervals for a given system.
  • Evident: as a review, it presents no new primary data and does not report a systematic search or risk-of-bias assessment; suggestions about clinical and educational benefits are forward-looking.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/nrn.2015.18

Cites

111 of 111 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

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Show all 109 claims

  • It may state two separate claims as one. 27
  • Probably not a substantial claim. 23
48 of 48
  • The authors state this as an assumption.

    In Landauer's model, the state of a neural circuit following the first learning trial is such that a second reinforcing trial soon after will not markedly increase the consolidation of the learning trace resulting from the first trial.

    “First, the state of a neural circuit following the first learning trial is such that a second reinforcing trial soon after will not markedly increase the consolidation of the learning trace resulting from the first trial (FIG. 1a).”
  • The authors state this as an assumption.

    In Landauer's model, the probability that the second trial can successfully reinforce the first trial declines with time.

    “The second assumption is that the probability that the second trial can successfully reinforce the first trial declines with time (FIG. 1e).”
  • The authors state this as an assumption.

    For maximum intervals, each trial or stimulus generates a separate, relatively short-lived biochemical trace, and for effective spaced learning these traces must overlap and summate, with the summed magnitude driving long-lasting synaptic potentiation.

    “For maximum intervals, a reasonable qualitative assumption is that each trial or stimulus generates a separate, relatively short-lived biochemical trace and that, for effective spaced learning, these traces must overlap and summate, with the summed magnitude driving long-lasting synaptic potentiation.”
  • The authors state this as an assumption.

    Reactivations of a memory reactivate some of the same neurons and synapses that were activated in the original learning sessions.

    “To more strongly connect this variant of deficient-processing theory to recent cellular and molecular data, one must also assume that reactivations of a memory reactivate some of the same neurons and synapses that were activated in the original learning sessions.”
  • The authors state this as an assumption.

    The necessity of PKA and ERK activation for LTF can be represented by a variable 'inducer' whose value is proportional to the product of PKA and MAPK activities.

    “In the model (FIG. 4a), the necessity of PKA and ERK activation for LTF was simply represented with a variable termed ‘inducer’. The value of inducer was proportional to the product of PKA and MAPK activities.”
  • The authors state this as an assumption.

    The amount of LTF and LTS increases with an increase in the peak value of inducer.

    “The amount of LTF and LTS was predicted to increase with an increase in the peak value of inducer.”
  • The authors state this as an assumption.

    Greater peak levels of phosphorylated C/EBP (pC/EBP) predict greater LTF.

    “In simulations of the effects of different spaced protocols, greater peak levels of phosphorylated C/EBP (pC/EBP) were taken to predict greater LTF.”
  • The authors state this as an assumption.

    The magnitude of LTP induction can be represented by an increase in a synaptic weight variable.

    “In this model, the magnitude of LTP induction was represented by an increase in a synaptic weight variable.”
  • The authors state this as an assumption.

    Parameter changes in the model correspond to plausible single-drug effects.

    “Then, starting from this simulation, the parameters were altered in ways corresponding to plausible single-drug effects.”
  • The authors state this as an assumption.

    Parameter changes in the model correspond to plausible paired-drug effects.

    “Thus, pairs of parameter changes were considered, corresponding to plausible paired-drug effects.”
  • The paper relies on this without support.

    CBP is an acetyltransferase and essential co-activator for several transcription factors, including pCREB.

    “CREB-binding protein (CBP) is an acetyltransferase and essential co-activator for several transcription factors, including phosphorylated CREB (pCREB).”
11 of 11

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • A 40-minute interval is described as both ineffective and approximately the minimum effective interval

    Minor

    The paper reports that theta-burst stimuli at 40-minute intervals did not cumulatively increase LTP, but later describes approximately 40 minutes as the minimum effective interval for the same spaced-LTP protocol.

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Citations

Cites

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Research works printed but not linked: 1

  1. Hintzman, D. L. in Theories in Cognitive Psychology: The Loyola Symposium (ed. Solso, R. L.) 77–90 (Erlbaum, 1974).

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Metadata

Authors
Smolen, P., Zhang, Y., & Byrne, J. H.
Corresponding author
Byrne, John H.
Venue
Nature Reviews Neuroscience
Published
2016-01-25
Volume
17
Issue
2
Pages
77-88
ISSN
  • 1471-003X (ISSN-L)
  • 1471-0048
OpenAlex
W2263535855
Cited by
326
References
110
Citations by year
2026: 32 · 2025: 52 · 2024: 51 · 2023: 39 · 2022: 36 · 2021: 32 · 2020: 29 · 2019: 15 · 2018: 13 · 2017: 22 · 2016: 5
FWCI
1.94
Type
journal article
Language
en
Open access
Locations
  • Nature reviews. Neuroscience (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Institute of Neurological Disorders and Stroke (NS073974, NS019895, R01NS019895, R01NS073974, R37NS019895)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
Keywords
spaced learning, massed training, intertrial interval, memory formation, synaptic plasticity, learning theory, signaling pathways, computational models of learning
MeSH terms
Animals, Computer Simulation, Humans, Learning, Models, Biological, Neuronal Plasticity, Time Factors, Signal Transduction, Memory, Long-Term

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