Spacing effect

Still openEvidence checked

Subjects: Memory and learning · Brain and cognition

Collections: Learning and intelligence · Memory and learning

Reading depth

The problem

Learn a new word twice. Seeing it again after a gap can leave a stronger later memory than seeing it again at once. But a long gap may also make the first encounter hard to retrieve. What makes the gap helpful: a changed context, recalling the earlier encounter, or a combination?[1]

Scope

Human verbal declarative learning: discriminate contextual variability from study-phase retrieval as causes of lag-by-retention effects, beyond quantitative curve fitting.

What remains open

A 2026 model fitted patterns from six verbal-learning experiments. It describes how performance changes with practice and delay, but the fit does not reveal whether changed context or successful recall caused the benefit. The tested verbal materials also leave other kinds of learning open.[1]

Why it matters

The same amount of practice can leave different amounts of lasting memory. Understanding the cause could help explain when a later repetition reinforces a word and when the earlier encounter has become too difficult to recover. That is a conditional learning prediction, not one best waiting time for everyone.[1]

Research directions

2 directions to explore

Definitions

2 terms
retention interval

The time from the last learning encounter to the memory test. It is different from the gap between learning encounters.[1]

study-phase retrieval

Recovering an earlier learning encounter while studying the item again. One account of spacing proposes that this successful recovery contributes to later memory.[1]

Sources

Partial results
Martin Riopel and Patrice Potvin

Scale-invariant modeling unified distributed-practice patterns. Scope: Six simple verbal-material experiments.

Fits do not establish unique mechanism; material and joint-measure assumptions restrict transfer.

Source date: 2026-03-25Reported fits to accuracy/response-time patterns include a repeated bird-name task.Read the source ↗
  1. A Scale-Invariant Learning Model for Distributed Practice Effects Martin Riopel and Patrice Potvin · 2026-03-25
    Notes on A Scale-Invariant Learning Model for Distributed Practice Effects

    Primary quantitative model fits distributed practice data from six verbal-material experiments; fit success does not uniquely identify a biological mechanism.

  2. Spaced practice and reactive inhibition have limited or no effects on motor sequence learning Mohan W. Gupta and Timothy C. Rickard · 2026-05-23
    Notes on Spaced practice and reactive inhibition have limited or no effects on motor sequence learning

    Primary motor-sequence experiment marks a domain boundary; verbal spacing cannot simply be assumed to explain motor practice.

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